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    Tundra vegetation change and impacts on permafrost

    1.Meredith, M. et al. in IPCC Special Report on the Ocean and Cryosphere in a Changing Climate Ch. 3 (eds Pörtner, H.-O. et al.) (Intergovernmental Panel on Climate Change, 2019).2.Blok, D. et al. Shrub expansion may reduce summer permafrost thaw in Siberian tundra. Glob. Change Biol. 16, 1296–1305 (2010). A field study in which dwarf-shrub canopies were removed experimentally, resulting in increased thaw depths, thereby, underscoring the protective role of vegetation cover on permafrost.
    Google Scholar 
    3.van Huissteden, J. Thawing Permafrost: Permafrost Carbon in a Warming Arctic (Springer, 2020).4.Jorgenson, M. et al. Resilience and vulnerability of permafrost to climate change. Can. J. For. Res. 40, 1219–1236 (2010).
    Google Scholar 
    5.Kropp, H. et al. Shallow soils are warmer under trees and tall shrubs across Arctic and Boreal ecosystems. Environ. Res. Lett. 16, 015001 (2020).
    Google Scholar 
    6.Myers-Smith, I. H. & Hik, D. S. Shrub canopies influence soil temperatures but not nutrient dynamics: an experimental test of tundra snow–shrub interactions. Ecol. Evol. 3, 3683–3700 (2013).
    Google Scholar 
    7.Sturm, M. et al. Snow–shrub interactions in Arctic tundra: a hypothesis with climatic implications. J. Clim. 14, 336–344 (2001).
    Google Scholar 
    8.Sturm, M. et al. Winter biological processes could help convert arctic tundra to shrubland. BioScience 55, 17–26 (2005).
    Google Scholar 
    9.Chapin, F. S. et al. Role of land-surface changes in Arctic summer warming. Science 310, 657–660 (2005).
    Google Scholar 
    10.Loranty, M. M. et al. Reviews and syntheses: Changing ecosystem influences on soil thermal regimes in northern high-latitude permafrost regions. Biogeosciences 15, 5287–5313 (2018). Review article showing how Arctic ecosystem processes can influence soil thermal dynamics in permafrost soil.
    Google Scholar 
    11.Shur, Y. L. & Jorgenson, M. T. Patterns of permafrost formation and degradation in relation to climate and ecosystems. Permafr. Periglac. Process. 18, 7–19 (2007).
    Google Scholar 
    12.Chadburn, S. E. et al. An observation-based constraint on permafrost loss as a function of global warming. Nat. Clim. Change 7, 340–344 (2017).
    Google Scholar 
    13.Smith, S. L., O’Neill, H. B., Isaksen, K., Noetzli, J. & Romanovsky, V. E. The changing thermal state of permafrost. Nat. Rev. Earth. Environ. 3 https://doi.org/10.1038/s43017-021-00240-1 (2022).14.Ksenofontov, S., Backhaus, N. & Schaepman-Strub, G. ‘There are new species’: indigenous knowledge of biodiversity change in Arctic Yakutia. Polar Geogr. 42, 34–57 (2019).
    Google Scholar 
    15.Schuur, E. A. et al. Vulnerability of permafrost carbon to climate change: implications for the global carbon cycle. BioScience 58, 701–714 (2008).
    Google Scholar 
    16.Kokelj, S. V. & Jorgenson, M. Advances in thermokarst research. Permafr. Periglac. Process. 24, 108–119 (2013).
    Google Scholar 
    17.Keuper, F. et al. A frozen feast: thawing permafrost increases plant-available nitrogen in subarctic peatlands. Glob. Change Biol. 18, 1998–2007 (2012).
    Google Scholar 
    18.Salmon, V. G. et al. Nitrogen availability increases in a tundra ecosystem during five years of experimental permafrost thaw. Glob. Change Biol. 22, 1927–1941 (2016).
    Google Scholar 
    19.Blume-Werry, G., Milbau, A., Teuber, L. M., Johansson, M. & Dorrepaal, E. Dwelling in the deep–strongly increased root growth and rooting depth enhance plant interactions with thawing permafrost soil. New Phytol. 223, 1328–1339 (2019).
    Google Scholar 
    20.Wang, P. et al. Above- and below-ground responses of four tundra plant functional types to deep soil heating and surface soil fertilization. J. Ecol. 105, 947–957 (2017).
    Google Scholar 
    21.Nauta, A. L. et al. Permafrost collapse after shrub removal shifts tundra ecosystem to a methane source. Nat. Clim. Change 5, 67–70 (2015).
    Google Scholar 
    22.Osterkamp, T. et al. Physical and ecological changes associated with warming permafrost and thermokarst in interior Alaska. Permafr. Periglac. Process. 20, 235–256 (2009).
    Google Scholar 
    23.Schuur, E. A. et al. Climate change and the permafrost carbon feedback. Nature 520, 171–179 (2015).
    Google Scholar 
    24.Koven, C. D. et al. Permafrost carbon-climate feedbacks accelerate global warming. Proc. Natl Acad. Sci. USA 108, 14769–14774 (2011).
    Google Scholar 
    25.Abbott, B. W. & Jones, J. B. Permafrost collapse alters soil carbon stocks, respiration, CH4, and N2O in upland tundra. Glob. Change Biol. 21, 4570–4587 (2015).
    Google Scholar 
    26.Voigt, C. et al. Warming of subarctic tundra increases emissions of all three important greenhouse gases – carbon dioxide, methane, and nitrous oxide. Glob. Change Biol. 23, 3121–3138 (2017).
    Google Scholar 
    27.Lenton, T. M. et al. Climate tipping points – too risky to bet against. Nature 575, 592–595 (2019).
    Google Scholar 
    28.Miner, K. R. Permafrost carbon emissions in a changing Arctic. Nat. Rev. Earth Environ. https://doi.org/10.1038/s43017-021-00230-3 (2022).Article 

    Google Scholar 
    29.Peterson, K. & Billings, W. Tundra vegetational patterns and succession in relation to microtopography near Atkasook, Alaska. Arct. Alp. Res. 12, 473–482 (1980).
    Google Scholar 
    30.Bliss, L. in North American Terrestrial Vegetation (eds Barbour, M. G. & Billings W. D.) (Cambridge Univ. Press, 1988).31.Walker, D. A. et al. The circumpolar Arctic vegetation map. J. Veg. Sci. 16, 267–282 (2005).
    Google Scholar 
    32.Frost, G. V., Epstein, H. E. & Walker, D. A. Regional and landscape-scale variability of Landsat-observed vegetation dynamics in northwest Siberian tundra. Environ. Res. Lett. 9, 025004 (2014).
    Google Scholar 
    33.Walker, D. A. et al. Environment, vegetation and greenness (NDVI) along the North America and Eurasia Arctic transects. Environ. Res. Lett. 7, 015504 (2012).
    Google Scholar 
    34.Raynolds, M. K. et al. A raster version of the Circumpolar Arctic Vegetation Map (CAVM). Remote Sens. Environ. 232, 111297 (2019).
    Google Scholar 
    35.Chernov, Y. I. & Matveyeva, N. in Polar Alpine Tundra (ed. Wielgolaski, F. E.) 361–507 (Elsevier, 1997).36.Elvebakk, A. in The Species Concept in the High North: A Panarctic Flora Initiative (eds Nordal, I. & Razzhivin, V. Y.) 81–112 (The Norwegian Academy of Science and Letters, 1999).37.Yurtsev, B. A. Floristic division of the Arctic. J. Veg. Sci. 5, 765–776 (1994).
    Google Scholar 
    38.Elmendorf, S. C. et al. Plot-scale evidence of tundra vegetation change and links to recent summer warming. Nat. Clim. Change 2, 453–457 (2012). A meta-analysis of field-observed vegetation changes from 46 polar sites indicating widespread increases of shrub vegetation and increased plant size.
    Google Scholar 
    39.Iversen, C. M. et al. The unseen iceberg: plant roots in arctic tundra. New Phytol. 205, 34–58 (2015).
    Google Scholar 
    40.Hobbie, J. E. & Hobbie, E. A. 15N in symbiotic fungi and plants estimates nitrogen and carbon flux rates in Arctic tundra. Ecology 87, 816–822 (2006).
    Google Scholar 
    41.Nielsen, U. N. & Wall, D. H. The future of soil invertebrate communities in polar regions: different climate change responses in the Arctic and Antarctic? Ecol. Lett. 16, 409–419 (2013).
    Google Scholar 
    42.Clemmensen, K. E. et al. A tipping point in carbon storage when forest expands into tundra is related to mycorrhizal recycling of nitrogen. Ecol. Lett. 24, 1193–1204 (2021).
    Google Scholar 
    43.Minke, M., Donner, N., Karpov, N., de Klerk, P. & Joosten, H. Patterns in vegetation composition, surface height and thaw depth in polygon mires in the Yakutian Arctic (NE Siberia): a microtopographical characterisation of the active layer. Permafr. Periglac. Process. 20, 357–368 (2009).
    Google Scholar 
    44.Liljedahl, A. K. et al. Pan-Arctic ice-wedge degradation in warming permafrost and its influence on tundra hydrology. Nat. Geosci. 9, 312–318 (2016).
    Google Scholar 
    45.Grunberg, I., Wilcox, E. J., Zwieback, S., Marsh, P. & Boike, J. Linking tundra vegetation, snow, soil temperature, and permafrost. Biogeosciences 17, 4261–4279 (2020). A field study reporting that large variations in soil temperatures and thaw depths can be explained by vegetation-mediated differences in snow height.
    Google Scholar 
    46.Magnússon, R. I. et al. Rapid vegetation succession and coupled permafrost dynamics in Arctic thaw ponds in the Siberian lowland tundra. J. Geophys. Res. Biogeosci. 125, e2019JG005618 (2020).
    Google Scholar 
    47.Jorgenson, M. et al. Role of ground ice dynamics and ecological feedbacks in recent ice wedge degradation and stabilization. J. Geophys. Res. Earth Surf. 120, 2280–2297 (2015). Outlines the role of ground ice and vegetation succession in thermokarst terrain, including first estimates of recovery times.
    Google Scholar 
    48.Bjorkman, A. D. et al. Status and trends in Arctic vegetation: evidence from experimental warming and long-term monitoring. Ambio 49, 678–692 (2020). A meta-analysis of plant species responses to experimental climate warming across Arctic sites, finding that shrubs and graminoids generally responded positively to warming, whereas lichens and bryophytes responded more negatively.
    Google Scholar 
    49.Frost, G. V. et al. Arctic Report Card 2020: Tundra Greenness. https://doi.org/10.25923/46rm-0w23 (NOAA, 2020). Provides an annual update of Arctic NDVI, offering a long-standing record of Arctic greening and browning.50.Myers-Smith, I. H. et al. Complexity revealed in the greening of the Arctic. Nat. Clim. Change 10, 106–117 (2020). Review article outlining complexity in Arctic greening and browning dynamics. The temporal and spatial scale of spectral data and the role of non-vegetation-related processes and ground-truthing remains essential.
    Google Scholar 
    51.Berner, L. T. et al. Summer warming explains widespread but not uniform greening in the Arctic tundra biome. Nat. Commun. 11, 4621 (2020).
    Google Scholar 
    52.Sistla, S. A. et al. Long-term warming restructures Arctic tundra without changing net soil carbon storage. Nature 497, 615–618 (2013).
    Google Scholar 
    53.Bhatt, U. S. et al. Circumpolar Arctic Tundra vegetation change is linked to sea ice decline. Earth Interact. 14, 1–20 (2010).
    Google Scholar 
    54.Oechel, W. C. & Billings, W. in Arctic Ecosystems in a Changing Climate: an Ecophysiological Perspective (eds Chapin, F. S. III et al.) 139–168 (Academic Press, 1992).55.Shaver, G. R. et al. Species composition interacts with fertilizer to control long-term change in tundra productivity. Ecology 82, 3163–3181 (2001).
    Google Scholar 
    56.Bret-Harte, M. S., Shaver, G. R. & Chapin, F. S. III Primary and secondary stem growth in arctic shrubs: implications for community response to environmental change. J. Ecol. 90, 251–267 (2002).
    Google Scholar 
    57.Mack, M. C., Schuur, E. A. G., Bret-Harte, M. S., Shaver, G. R. & Chapin, F. S. Ecosystem carbon storage in arctic tundra reduced by long-term nutrient fertilization. Nature 431, 440–443 (2004).
    Google Scholar 
    58.Myers-Smith, I. H. et al. Climate sensitivity of shrub growth across the tundra biome. Nat. Clim. Change 5, 887–891 (2015).
    Google Scholar 
    59.McGuire, A. D. et al. Sensitivity of the carbon cycle in the Arctic to climate change. Ecol. Monogr. 79, 523–555 (2009).
    Google Scholar 
    60.van der Kolk, H.-J., Heijmans, M. M., van Huissteden, J., Pullens, J. W. & Berendse, F. Potential Arctic tundra vegetation shifts in response to changing temperature, precipitation and permafrost thaw. Biogeosciences 13, 6229–6245 (2016).
    Google Scholar 
    61.Myers-Smith, I. H. et al. Eighteen years of ecological monitoring reveals multiple lines of evidence for tundra vegetation change. Ecol. Monogr. 89, e01351 (2019).
    Google Scholar 
    62.Leffler, A. J., Klein, E. S., Oberbauer, S. F. & Welker, J. M. Coupled long-term summer warming and deeper snow alters species composition and stimulates gross primary productivity in tussock tundra. Oecologia 181, 287–297 (2016).
    Google Scholar 
    63.Euskirchen, E. et al. Importance of recent shifts in soil thermal dynamics on growing season length, productivity, and carbon sequestration in terrestrial high-latitude ecosystems. Glob. Change Biol. 12, 731–750 (2006).
    Google Scholar 
    64.McGuire, A. D. et al. Dependence of the evolution of carbon dynamics in the northern permafrost region on the trajectory of climate change. Proc. Natl Acad. Sci. USA 115, 3882–3887 (2018).
    Google Scholar 
    65.National Academies of Sciences, Engineering, and Medicine. Understanding Northern Latitude Vegetation Greening and Browning: Proceedings of a Workshop (The National Academies Press, 2019).66.Phoenix, G. K. & Bjerke, J. W. Arctic browning: extreme events and trends reversing arctic greening. Glob. Change Biol. 22, 2960–2962 (2016).
    Google Scholar 
    67.Bokhorst, S. et al. Impacts of extreme winter warming in the sub-Arctic: growing season responses of dwarf shrub heathland. Glob. Change Biol. 14, 2603–2612 (2008).
    Google Scholar 
    68.Bret-Harte, M. S. et al. The response of Arctic vegetation and soils following an unusually severe tundra fire. Philos. Trans. R. Soc. B Biol. Sci. 368, 20120490 (2013).
    Google Scholar 
    69.Farquharson, L. M. et al. Climate change drives widespread and rapid thermokarst development in very cold permafrost in the Canadian High Arctic. Geophys. Res. Lett. 46, 6681–6689 (2019).
    Google Scholar 
    70.Turetsky et al. Permafrost collapse is accelerating carbon release. Nature 569, 32–34 (2019). Reveals that abrupt thaw of permafrost could double the estimated future release of greenhouse gases from permafrost soils compared with scenarios of gradual thaw.
    Google Scholar 
    71.Bokhorst, S. F., Bjerke, J. W., Tømmervik, H., Callaghan, T. V. & Phoenix, G. K. Winter warming events damage sub-Arctic vegetation: consistent evidence from an experimental manipulation and a natural event. J. Ecol. 97, 1408–1415 (2009).
    Google Scholar 
    72.Bjerke, J. W. et al. Record-low primary productivity and high plant damage in the Nordic Arctic Region in 2012 caused by multiple weather events and pest outbreaks. Environ. Res. Lett. 9, 084006 (2014).
    Google Scholar 
    73.Treharne, R., Bjerke, J. W., Tømmervik, H., Stendardi, L. & Phoenix, G. K. Arctic browning: impacts of extreme climatic events on heathland ecosystem CO2 fluxes. Glob. Change Biol. 25, 489–503 (2019).
    Google Scholar 
    74.Olofsson, J., Tommervik, H. & Callaghan, T. V. Vole and lemming activity observed from space. Nat. Clim. Change 2, 880–883 (2012).
    Google Scholar 
    75.Lara, M. J., Nitze, I., Grosse, G., Martin, P. & McGuire, A. D. Reduced arctic tundra productivity linked with landform and climate change interactions. Sci. Rep. 8, 2345 (2018).
    Google Scholar 
    76.Verdonen, M., Berner, L. T., Forbes, B. C. & Kumpula, T. Periglacial vegetation dynamics in Arctic Russia: decadal analysis of tundra regeneration on landslides with time series satellite imagery. Environ. Res. Lett. 15, 105020 (2020).
    Google Scholar 
    77.Assmann, J. J., Myers-Smith, I. H., Kerby, J. T., Cunliffe, A. M. & Daskalova, G. N. Drone data reveal heterogeneity in tundra greenness and phenology not captured by satellites. Environ. Res. Lett. 15, 125002 (2020).
    Google Scholar 
    78.Raynolds, M. K. & Walker, D. A. Increased wetness confounds Landsat-derived NDVI trends in the central Alaska North Slope region, 1985–2011. Environ. Res. Lett. 11, 085004 (2016).
    Google Scholar 
    79.Magnússon, R. Í. et al. Shrub decline and expansion of wetland vegetation revealed by very high resolution land cover change detection in the Siberian lowland tundra. Sci. Total Environ. 782, 146877 (2021).
    Google Scholar 
    80.Nitze, I. & Grosse, G. Detection of landscape dynamics in the Arctic Lena Delta with temporally dense Landsat time-series stacks. Remote Sens. Environ. 181, 27–41 (2016).
    Google Scholar 
    81.Chen, Y., Hu, F. S. & Lara, M. J. Divergent shrub-cover responses driven by climate, wildfire, and permafrost interactions in Arctic tundra ecosystems. Glob. Change Biol. 27, 652–663 (2021).
    Google Scholar 
    82.Huete, A. et al. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ. 83, 195–213 (2002).
    Google Scholar 
    83.Siewert, M. B. & Olofsson, J. Scale-dependency of Arctic ecosystem properties revealed by UAV. Environ. Res. Lett. 15, 094030 (2020).
    Google Scholar 
    84.Beamish, A. et al. Recent trends and remaining challenges for optical remote sensing of Arctic tundra vegetation: a review and outlook. Remote Sens. Environ. 246, 111872 (2020).
    Google Scholar 
    85.Blok, D. et al. The response of Arctic vegetation to the summer climate: relation between shrub cover, NDVI, surface albedo and temperature. Environ. Res. Lett. 6, 035502 (2011).
    Google Scholar 
    86.Boelman, N. T., Gough, L., McLaren, J. R. & Greaves, H. Does NDVI reflect variation in the structural attributes associated with increasing shrub dominance in arctic tundra? Environ. Res. Lett. 6, 035501 (2011).
    Google Scholar 
    87.Sturm, M., Racine, C. & Tape, K. Climate change – increasing shrub abundance in the Arctic. Nature 411, 546–547 (2001).
    Google Scholar 
    88.Tape, K., Sturm, M. & Racine, C. The evidence for shrub expansion in Northern Alaska and the Pan-Arctic. Glob. Change Biol. 12, 686–702 (2006).
    Google Scholar 
    89.Jorgenson, J. C., Raynolds, M. K., Reynolds, J. H. & Benson, A. M. Twenty-five year record of changes in plant cover on tundra of northeastern Alaska. Arct. Antarctic Alp. Res. 47, 785–806 (2015).
    Google Scholar 
    90.Jorgenson, J. C., Jorgenson, M. T., Boldenow, M. L. & Orndahl, K. M. Landscape change detected over a half century in the Arctic National Wildlife Refuge using high-resolution aerial imagery. Remote Sens. 10, 1305 (2018).
    Google Scholar 
    91.Hobbie, J. E. et al. Ecosystem responses to climate change at a Low Arctic and a High Arctic long-term research site. Ambio 46, 160–173 (2017).
    Google Scholar 
    92.Virkkala, A.-M., Abdi, A. M., Luoto, M. & Metcalfe, D. B. Identifying multidisciplinary research gaps across Arctic terrestrial gradients. Environ. Res. Lett. 14, 124061 (2019).
    Google Scholar 
    93.Ropars, P. & Boudreau, S. Shrub expansion at the forest-tundra ecotone: spatial heterogeneity linked to local topography. Environ. Res. Lett. 7, 015501 (2012).
    Google Scholar 
    94.Ropars, P., Levesque, E. & Boudreau, S. How do climate and topography influence the greening of the forest-tundra ecotone in northern Québec? A dendrochronological analysis of Betula glandulosa. J. Ecol. 103, 679–690 (2015).
    Google Scholar 
    95.Tremblay, B., Levesque, E. & Boudreau, S. Recent expansion of erect shrubs in the Low Arctic: evidence from Eastern Nunavik. Environ. Res. Lett. 7, 035501 (2012).
    Google Scholar 
    96.Boulanger-Lapointe, N., Levesque, E., Boudreau, S., Henry, G. H. R. & Schmidt, N. M. Population structure and dynamics of Arctic willow (Salix arctica) in the High Arctic. J. Biogeogr. 41, 1967–1978 (2014).
    Google Scholar 
    97.Frost, G. V., Epstein, H. E., Walker, D. A., Matyshak, G. & Ermokhina, K. Patterned-ground facilitates shrub expansion in Low Arctic tundra. Environ. Res. Lett. 8, 015035 (2013).
    Google Scholar 
    98.Lantz, T. C., Kokelj, S. V., Gergel, S. E. & Henry, G. H. Relative impacts of disturbance and temperature: persistent changes in microenvironment and vegetation in retrogressive thaw slumps. Glob. Change Biol. 15, 1664–1675 (2009).
    Google Scholar 
    99.Huebner, D. C. & Bret-Harte, M. S. Microsite conditions in retrogressive thaw slumps may facilitate increased seedling recruitment in the Alaskan Low Arctic. Ecol. Evol. 9, 1880–1897 (2019).
    Google Scholar 
    100.Lantz, T. C., Marsh, P. & Kokelj, S. V. Recent shrub proliferation in the Mackenzie Delta uplands and microclimatic implications. Ecosystems 16, 47–59 (2013).
    Google Scholar 
    101.Hu, F. S. et al. Arctic tundra fires: natural variability and responses to climate change. Front. Ecol. Environ. 13, 369–377 (2015).
    Google Scholar 
    102.Harris, I., Osborn, T. J., Jones, P. & Lister, D. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Sci. Data 7, 109 (2020).
    Google Scholar 
    103.Didan, K. MYD13Q1 MODIS/Aqua vegetation indices 16-day L3 global 250 m SIN grid V006. NASA EOSDIS Land Processes DAAC https://doi.org/10.5067/MODIS/MYD13Q1.006 (2015).Article 

    Google Scholar 
    104.Didan, K. MOD13Q1 MODIS/Terra vegetation indices 16-day L3 global 250 m SIN grid V006. NASA EOSDIS Land Processes DAAC https://doi.org/10.5067/MODIS/MOD13Q1.006 (2015).Article 

    Google Scholar 
    105.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).
    Google Scholar 
    106.Brown, J., Ferrians, O. Jr, Heginbottom, J. A. & Melnikov, E. Circum-Arctic Map of Permafrost and Ground-ice Conditions (US Geological Survey, 1997).107.Jones, G. A. & Henry, G. H. Primary plant succession on recently deglaciated terrain in the Canadian High Arctic. J. Biogeogr. 30, 277–296 (2003).
    Google Scholar 
    108.Cornelissen, J. H. C. et al. Global change and arctic ecosystems: is lichen decline a function of increases in vascular plant biomass? J. Ecol. 89, 984–994 (2001).
    Google Scholar 
    109.Aguirre, D., Benhumea, A. E. & McLaren, J. R. Shrub encroachment affects tundra ecosystem properties through their living canopy rather than increased litter inputs. Soil Biol. Biochem. 153, 108121 (2021).
    Google Scholar 
    110.Gornall, J. L., Jonsdottir, I. S., Woodin, S. J. & Van der Wal, R. Arctic mosses govern below-ground environment and ecosystem processes. Oecologia 153, 931–941 (2007).
    Google Scholar 
    111.Soudzilovskaia, N. A., Bodegom, P. M. & Cornelissen, J. H. Dominant bryophyte control over high-latitude soil temperature fluctuations predicted by heat transfer traits, field moisture regime and laws of thermal insulation. Funct. Ecol. 27, 1442–1454 (2013).
    Google Scholar 
    112.Blok, D. et al. The cooling capacity of mosses: controls on water and energy fluxes in a Siberian tundra site. Ecosystems 14, 1055–1065 (2011).
    Google Scholar 
    113.Belke-Brea, M., Domine, F., Barrere, M., Picard, G. & Arnaud, L. Impact of shrubs on winter surface albedo and snow specific surface area at a low Arctic site: In situ measurements and simulations. J. Clim. 33, 597–609 (2020).
    Google Scholar 
    114.Wilcox, E. J. et al. Tundra shrub expansion may amplify permafrost thaw by advancing snowmelt timing. Arct. Sci. 5, 202–217 (2019).
    Google Scholar 
    115.Frost, G. V., Epstein, H. E., Walker, D. A., Matyshak, G. & Ermokhina, K. Seasonal and long-term changes to active-layer temperatures after tall shrubland expansion and succession in Arctic tundra. Ecosystems 21, 507–520 (2018).
    Google Scholar 
    116.Wilson, M. A., Burn, C. & Humphreys, E. in Cold Regions Engineering 2019 (eds Bilodeau, J.-P., Nadeau, D. F., Fortier, D. & Conciatori, D.) 687–695 (American Society of Civil Engineers, 2019).117.Liljedahl, A. K., Timling, I., Frost, G. V. & Daanen, R. P. Arctic riparian shrub expansion indicates a shift from streams gaining water to those that lose flow. Commun. Earth Environ. 1, 50 (2020).
    Google Scholar 
    118.Paradis, M., Lévesque, E. & Boudreau, S. Greater effect of increasing shrub height on winter versus summer soil temperature. Environ. Res. Lett. 11, 085005 (2016).
    Google Scholar 
    119.Beringer, J., Chapin, F. S., Thompson, C. C. & McGuire, A. D. Surface energy exchanges along a tundra-forest transition and feedbacks to climate. Agric. For. Meteorol. 131, 143–161 (2005).
    Google Scholar 
    120.Kemppinen, J. et al. Dwarf shrubs impact tundra soils: drier, colder, and less organic carbon. Ecosystems 24, 1378–1392 (2021). Quantifies the effects of shrub abundance on the soil thermal regime using a distinction between a rough, tall-shrub canopy and an aerodynamic, dwarf-shrub canopy.
    Google Scholar 
    121.Jorgenson, M. T., Ely, C. & Terenzi, J. in Shared Science Needs: Report from the Western Alaska Landscape Conservation Cooperative Science Workshop (eds Reynolds, J. H. & Wiggins, H. V.) 130–137 (2012).122.Sturm, M., Douglas, T., Racine, C. & Liston, G. E. Changing snow and shrub conditions affect albedo with global implications. J. Geophys. Res. Biogeosci. 110, G01004 (2005).
    Google Scholar 
    123.Zhang, T. Influence of the seasonal snow cover on the ground thermal regime: an overview. Rev. Geophys. 43, RG4002 (2005).
    Google Scholar 
    124.Domine, F., Barrere, M. & Morin, S. The growth of shrubs on high Arctic tundra at Bylot Island: impact on snow physical properties and permafrost thermal regime. Biogeosciences 13, 6471–6486 (2016).
    Google Scholar 
    125.Lawrence, D. M. & Swenson, S. C. Permafrost response to increasing Arctic shrub abundance depends on the relative influence of shrubs on local soil cooling versus large-scale climate warming. Environ. Res. Lett. 6, 045504 (2011).
    Google Scholar 
    126.Barrere, M., Domine, F., Belke-Brea, M. & Sarrazin, D. Snowmelt events in autumn can reduce or cancel the soil warming effect of snow–vegetation interactions in the Arctic. J. Clim. 31, 9507–9518 (2018).
    Google Scholar 
    127.Loranty, M. M., Goetz, S. J. & Beck, P. S. Tundra vegetation effects on pan-Arctic albedo. Environ. Res. Lett. 6, 024014 (2011).
    Google Scholar 
    128.Bonfils, C. et al. On the influence of shrub height and expansion on northern high latitude climate. Environ. Res. Lett. 7, 015503 (2012).
    Google Scholar 
    129.Williamson, S. N., Barrio, I. C., Hik, D. S. & Gamon, J. A. Phenology and species determine growing-season albedo increase at the altitudinal limit of shrub growth in the sub-Arctic. Glob. Change Biol. 22, 3621–3631 (2016).
    Google Scholar 
    130.Juszak, I., Eugster, W., Heijmans, M. & Schaepman-Strub, G. Contrasting radiation and soil heat fluxes in Arctic shrub and wet sedge tundra. Biogeosciences 13, 4049–4064 (2016).
    Google Scholar 
    131.Göckede, M. et al. Negative feedback processes following drainage slow down permafrost degradation. Glob. Change Biol. 25, 3254–3266 (2019).
    Google Scholar 
    132.Bonan, G. Ecological Climatology: Concepts and Applications (Cambridge Univ. Press, 2015).133.Eugster, W. et al. Land–atmosphere energy exchange in Arctic tundra and boreal forest: available data and feedbacks to climate. Glob. Change Biol. 6, 84–115 (2000).
    Google Scholar 
    134.Liljedahl, A. et al. Nonlinear controls on evapotranspiration in arctic coastal wetlands. Biogeosciences 8, 3375–3389 (2011).
    Google Scholar 
    135.Zwieback, S., Chang, Q., Marsh, P. & Berg, A. Shrub tundra ecohydrology: rainfall interception is a major component of the water balance. Environ. Res. Lett. 14, 055005 (2019).
    Google Scholar 
    136.Subin, Z. M. et al. Effects of soil moisture on the responses of soil temperatures to climate change in cold regions. J. Clim. 26, 3139–3158 (2013).
    Google Scholar 
    137.Aalto, J., Scherrer, D., Lenoir, J., Guisan, A. & Luoto, M. Biogeophysical controls on soil-atmosphere thermal differences: implications on warming Arctic ecosystems. Environ. Res. Lett. 13, 074003 (2018).
    Google Scholar 
    138.Asmus, A. L. et al. Shrub shading moderates the effects of weather on arthropod activity in arctic tundra. Ecol. Entomol. 43, 647–655 (2018).
    Google Scholar 
    139.Hinkel, K., Paetzold, F., Nelson, F. & Bockheim, J. Patterns of soil temperature and moisture in the active layer and upper permafrost at Barrow, Alaska: 1993–1999. Glob. Planet. Change 29, 293–309 (2001).
    Google Scholar 
    140.Douglas, T. A., Turetsky, M. R. & Koven, C. D. Increased rainfall stimulates permafrost thaw across a variety of Interior Alaskan boreal ecosystems. NPJ Clim. Atmos. Sci. 3, 28 (2020).
    Google Scholar 
    141.Neumann, R. B. et al. Warming effects of spring rainfall increase methane emissions from thawing permafrost. Geophys. Res. Lett. 46, 1393–1401 (2019).
    Google Scholar 
    142.Aartsma, P., Asplund, J., Odland, A., Reinhardt, S. & Renssen, H. Microclimatic comparison of lichen heaths and shrubs: shrubification generates atmospheric heating but subsurface cooling during the growing season. Biogeosciences 18, 1577–1599 (2021).
    Google Scholar 
    143.Fisher, J. P. et al. The influence of vegetation and soil characteristics on active-layer thickness of permafrost soils in boreal forest. Glob. Change Biol. 22, 3127–3140 (2016).
    Google Scholar 
    144.Van Cleve, K. et al. Taiga ecosystems in interior Alaska. BioScience 33, 39–44 (1983).
    Google Scholar 
    145.Kade, A., Romanovsky, V. & Walker, D. The n-factor of nonsorted circles along a climate gradient in Arctic Alaska. Permafr. Periglac. Process. 17, 279–289 (2006).
    Google Scholar 
    146.Atchley, A. L., Coon, E. T., Painter, S. L., Harp, D. R. & Wilson, C. J. Influences and interactions of inundation, peat, and snow on active layer thickness. Geophys. Res. Lett. 43, 5116–5123 (2016).
    Google Scholar 
    147.Klene, A. E., Nelson, F. E., Shiklomanov, N. I. & Hinkel, K. M. The n-factor in natural landscapes: variability of air and soil-surface temperatures, Kuparuk River Basin, Alaska, USA. Arct. Antarct. Alp. Res. 33, 140–148 (2001).
    Google Scholar 
    148.van Everdingen, R. O. Multi-Language Glossary of Permafrost and Related Ground-Ice Terms (National Snow and Ice Data Center/World Data Center for Glaciology, 2005).149.Iwahana, G. et al. Geocryological characteristics of the upper permafrost in a tundra-forest transition of the Indigirka River Valley, Russia. Polar Sci. 8, 96–113 (2014).
    Google Scholar 
    150.Lewkowicz, A. G. & Way, R. G. Extremes of summer climate trigger thousands of thermokarst landslides in a High Arctic environment. Nat. Commun. 10, 1329 (2019).
    Google Scholar 
    151.Kanevskiy, M. et al. Degradation and stabilization of ice wedges: implications for assessing risk of thermokarst in northern Alaska. Geomorphology 297, 20–42 (2017).
    Google Scholar 
    152.Olefeldt, D. et al. Circumpolar distribution and carbon storage of thermokarst landscapes. Nat. Commun. 7, 13043 (2016).
    Google Scholar 
    153.Jorgenson, M., Shur, Y. L. & Pullman, E. R. Abrupt increase in permafrost degradation in Arctic Alaska. Geophys. Res. Lett. 33, L02503 (2006).
    Google Scholar 
    154.Stieglitz, M., Déry, S., Romanovsky, V. & Osterkamp, T. The role of snow cover in the warming of arctic permafrost. Geophys. Res. Lett. 30, 1721 (2003).
    Google Scholar 
    155.Anisimov, O. & Zimov, S. Thawing permafrost and methane emission in Siberia: Synthesis of observations, reanalysis, and predictive modeling. Ambio 50, 2050–2059 (2021).
    Google Scholar 
    156.Tei, S. et al. An extreme flood caused by a heavy snowfall over the Indigirka River basin in Northeastern Siberia. Hydrol. Process. 34, 522–537 (2020).
    Google Scholar 
    157.Jones, B. M. et al. Recent Arctic tundra fire initiates widespread thermokarst development. Sci. Rep. 5, 15865 (2015).
    Google Scholar 
    158.Fraser, R. H. et al. Climate sensitivity of high Arctic permafrost terrain demonstrated by widespread ice-wedge thermokarst on Banks Island. Remote Sens. 10, 954 (2018).
    Google Scholar 
    159.Kokelj, S. V., Lantz, T. C., Tunnicliffe, J., Segal, R. & Lacelle, D. Climate-driven thaw of permafrost preserved glacial landscapes, northwestern Canada. Geology 45, 371–374 (2017).
    Google Scholar 
    160.Raynolds, M. K. et al. Cumulative geoecological effects of 62 years of infrastructure and climate change in ice-rich permafrost landscapes, Prudhoe Bay Oilfield, Alaska. Glob. Change Biol. 20, 1211–1224 (2014).
    Google Scholar 
    161.Yang, M., Nelson, F. E., Shiklomanov, N. I., Guo, D. & Wan, G. Permafrost degradation and its environmental effects on the Tibetan Plateau: a review of recent research. Earth Sci. Rev. 103, 31–44 (2010).
    Google Scholar 
    162.Payette, S., Delwaide, A., Caccianiga, M. & Beauchemin, M. Accelerated thawing of subarctic peatland permafrost over the last 50 years. Geophys. Res. Lett. 31, L18208 (2004).
    Google Scholar 
    163.French, H. & Shur, Y. The principles of cryostratigraphy. Earth Sci. Rev. 101, 190–206 (2010).
    Google Scholar 
    164.Burn, C. R. & Friele, P. Geomorphology, vegetation succession, soil characteristics and permafrost in retrogressive thaw slumps near Mayo, Yukon Territory. Arctic 42, 31–40 (1989).
    Google Scholar 
    165.Walvoord, M. A. & Kurylyk, B. L. Hydrologic impacts of thawing permafrost — a review. Vadose Zone J. 15, vzj2016-01 (2016).
    Google Scholar 
    166.Zona, D. et al. Characterization of the carbon fluxes of a vegetated drained lake basin chronosequence on the Alaskan Arctic Coastal Plain. Glob. Change Biol. 16, 1870–1882 (2010).
    Google Scholar 
    167.Jorgenson, M. T. & Shur, Y. Evolution of lakes and basins in northern Alaska and discussion of the thaw lake cycle. J. Geophys. Res. Earth Surf. 112, F02S17 (2007).
    Google Scholar 
    168.Cray, H. A. & Pollard, W. H. Vegetation recovery patterns following permafrost disturbance in a Low Arctic setting: case study of Herschel Island, Yukon, Canada. Arct. Antarct. Alp. Res. 47, 99–113 (2015).
    Google Scholar 
    169.Baltzer, J. L., Veness, T., Chasmer, L. E., Sniderhan, A. E. & Quinton, W. L. Forests on thawing permafrost: fragmentation, edge effects, and net forest loss. Glob. Change Biol. 20, 824–834 (2014).
    Google Scholar 
    170.Scheffer, M., Hirota, M., Holmgren, M., Van Nes, E. H. & Chapin, F. S. Thresholds for boreal biome transitions. Proc. Natl Acad. Sci. USA 109, 21384–21389 (2012).
    Google Scholar 
    171.Nitze, I., Grosse, G., Jones, B. M., Romanovsky, V. E. & Boike, J. Remote sensing quantifies widespread abundance of permafrost region disturbances across the Arctic and Subarctic. Nat. Commun. 9, 5423 (2018).
    Google Scholar 
    172.Elmendorf, S. C. et al. Global assessment of experimental climate warming on tundra vegetation: heterogeneity over space and time. Ecol. Lett. 15, 164–175 (2012).
    Google Scholar 
    173.Strauss, J. et al. Deep Yedoma permafrost: a synthesis of depositional characteristics and carbon vulnerability. Earth Sci. Rev. 172, 75–86 (2017).
    Google Scholar 
    174.Hjort, J. E. A. Impacts of permafrost degradation on infrastructure. Nat. Rev. Earth. Environ. 3 https://doi.org/10.1038/s43017-021-00247-8 (2022).175.Kumpula, T., Pajunen, A., Kaarlejärvi, E., Forbes, B. C. & Stammler, F. Land use and land cover change in Arctic Russia: Ecological and social implications of industrial development. Glob. Environ. Change 21, 550–562 (2011).
    Google Scholar 
    176.Nitzbon, J. et al. Fast response of cold ice-rich permafrost in northeast Siberia to a warming climate. Nat. Commun. 11, 2201 (2020).
    Google Scholar 
    177.Lawrence, D. M., Koven, C. D., Swenson, S. C., Riley, W. J. & Slater, A. Permafrost thaw and resulting soil moisture changes regulate projected high-latitude CO2 and CH4 emissions. Environ. Res. Lett. 10, 094011 (2015).
    Google Scholar 
    178.Bintanja, R. & Andry, O. Towards a rain-dominated Arctic. Nat. Clim. Change 7, 263–267 (2017).
    Google Scholar 
    179.Mekonnen, Z. A., Riley, W. J., Grant, R. F. & Romanovsky, V. E. Changes in precipitation and air temperature contribute comparably to permafrost degradation in a warmer climate. Environ. Res. Lett. 16, 024008 (2021).
    Google Scholar 
    180.Mikhailov, I. Changes in the soil-plant cover of the high Arctic of Eastern Siberia. Eurasian Soil. Sci. 53, 715–723 (2020).
    Google Scholar 
    181.Frost, G. V. et al. Multi-decadal patterns of vegetation succession after tundra fire on the Yukon-Kuskokwim Delta, Alaska. Environ. Res. Lett. 15, 025003 (2020).
    Google Scholar 
    182.Whitley, M. A. et al. Assessment of LiDAR and spectral techniques for high-resolution mapping of sporadic permafrost on the Yukon-Kuskokwim Delta, Alaska. Remote Sens. 10, 258 (2018).
    Google Scholar  More

  • in

    Chromosome-level genome assembly of Bactrocera dorsalis reveals its adaptation and invasion mechanisms

    1.Qin, Y.-j. et al. Population structure of a global agricultural invasive pest, Bactrocera dorsalis (Diptera: Tephritidae). Evol. Appl. 11, 1990–2003 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    2.Christenson, L. D. & Foote, R. H. Biology of fruit flies. Annu. Rev. Entomol. 5, 171–192 (1960).
    Google Scholar 
    3.Clarke, A. R. et al. Invasive phytophagous pests arising through a recent tropical evolutionary radiation: the Bactrocera dorsalis complex of fruit flies. Annu. Rev. Entomol. 50, 293–319 (2005).CAS 
    PubMed 

    Google Scholar 
    4.Culliney, T. W. The aliens have landed: invasive species threaten Hawaii agriculture. Agric. Hawaii 3, 6–9 (2002).
    Google Scholar 
    5.Cantrell, B., Chadwick, B. & Cahill, A. Fruit Fly Fighters: Eradication of the Papaya Fruit Fly (CSIRO, Collingwood, 2002).6.Ekesi, S., De Meyer, M., Mohamed, S. A., Virgilio, M. & Borgemeister, C. Taxonomy, ecology, and management of native and exotic fruit fly species in Africa. Annu. Rev. Entomol. 61, 219–238 (2016).CAS 
    PubMed 

    Google Scholar 
    7.Duyck, P. F., David, P. & Quilici, S. A review of relationships between interspecific competition and invasions in fruit flies (Diptera: Tephritidae). Ecol. Entomol. 29, 511–520 (2004).
    Google Scholar 
    8.Liu, H., Zhang, C., Hou, B. H., Ou-Yang, G. C. & Ma, J. Interspecific competition between Ceratitis capitata and two Bactrocera spp. (Diptera: Tephritidae) evaluated via adult behavioral interference under laboratory conditions. J. Econ. Entomol. 110, 1145–1155 (2017).PubMed 

    Google Scholar 
    9.Li, F. et al. Insect genomes: progress and challenges. Insect Mol. Biol. 28, 739–758 (2019).CAS 
    PubMed 

    Google Scholar 
    10.Schutze, M. K. et al. Synonymization of key pest species within the Bactrocera dorsalis complex (Diptera: Tephritidae): taxonomic changes based on a review of 20 years of integrative morphological, molecular, cytogenetic, behavioural, and chemoecological data. Syst. Entomol. 40, 456–471 (2015).
    Google Scholar 
    11.Dudchenko, O. et al. De novo assembly of the Aedes aegypti genome using Hi-C yields chromosome-length scaffolds. Science 356, 92–95 (2017).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    12.Wu, N. et al. Fall webworm genomes yield insights into rapid adaptation of invasive species. Nat. Ecol. Evol. 3, 105–115 (2019).PubMed 

    Google Scholar 
    13.Papanicolaou, A. et al. The whole genome sequence of the Mediterranean fruit fly, Ceratitis capitata (Wiedemann), reveals insights into the biology and adaptive evolution of a highly invasive pest species. Genome Biol. 17, 192 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    14.Sim, S. B. & Geib, S. M. A chromosome-scale assembly of the Bactrocera cucurbitae genome provides insight to the genetic basis of white pupae. G3 (Bethesda) 7, 1927–1940 (2017).CAS 

    Google Scholar 
    15.Parra, G., Bradnam, K. & Korf, I. CEGMA: a pipeline to accurately annotate core genes in eukaryotic genomes. Bioinformatics 23, 1061–1067 (2007).CAS 
    PubMed 

    Google Scholar 
    16.Simão, F. A., Waterhouse, R. M., Ioannidis, P., Kriventseva, E. V. & Zdobnov, E. M. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics 31, 3210–3212 (2015).
    Google Scholar 
    17.Ashburner, M. et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25, 25–29 (2000).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    18.Kanehisa, M., Goto, S., Sato, Y., Furumichi, M. & Tanabe, M. KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 40, 109–114 (2012).
    Google Scholar 
    19.Ting, C. T. et al. Gene duplication and speciation in Drosophila: evidence from the Odysseus locus. Proc. Natl Acad. Sci. USA 101, 12232–12235 (2004).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    20.Yuan, Y. W. & Wessler, S. R. The catalytic domain of all eukaryotic cut-and-paste transposase superfamilies. Proc. Natl Acad. Sci. USA 108, 7884–7889 (2011).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    21.Rappoport, N. & Linial, M. Trends in genome dynamics among major orders of insects revealed through variations in protein families. BMC Genomics 16, 583 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    22.Sackton, T. B. et al. Dynamic evolution of the innate immune system in Drosophila. Nat. Genet. 39, 1461–1468 (2007).CAS 
    PubMed 

    Google Scholar 
    23.Stephen, B. H. & David, L. H. Key evolutionary innovations and their ecological mechanisms. Hist. Biol. 10, 151–173 (1995).
    Google Scholar 
    24.Yu, G. C., Wang, L. G., Han, Y. Y. & He, Q. Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    25.Lindquist, S. The heat-shock response. Annu. Rev. Biochem. 55, 1151–1191 (1986).CAS 
    PubMed 

    Google Scholar 
    26.Parsell, D. A. & Lindquist, S. The function of heat-shock proteins in stress tolerance-degradation and reactivation of damaged proteins. Annu. Rev. Genet. 27, 437–496 (1993).CAS 
    PubMed 

    Google Scholar 
    27.Feder, M. E. & Hofmann, G. E. Heat-shock proteins, molecular chaperones, and the stress response. Annu. Rev. Physiol. 61, 243–282 (1999).CAS 
    PubMed 

    Google Scholar 
    28.Iwama, G. K., Thomas, P. T., Forsyth, R. H. B. & Vijayan, M. M. Heat shock protein expression in fish. Rev. Fish. Biol. Fish. 8, 35–56 (1998).
    Google Scholar 
    29.Azad, P., Ryu, J. & Haddad, G. G. Distinct role of Hsp70 in Drosophila hemocytes during severe hypoxia. Free Radic. Biol. Med. 51, 530–538 (2011).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    30.Zhao, P. et al. Genome-wide analysis of the potato hsp20 gene family: identification, genomic organization and expression profiles in response to heat stress. BMC Genomics 19, 61 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    31.Weinstein, D. J. et al. The genome of a subterrestrial nematode reveals adaptations to heat. Nat. Commun. 10, 5268 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    32.Gu, X. et al. A transcriptional and functional analysis of heat hardening in two invasive fruit fly species, Bactrocera dorsalis, and Bactrocera correcta. Evol. Appl. 12, 1147–1163 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    33.Sorensen, J. G., Dahlgaard, J. & Loeschcke, V. Genetic variation in thermal tolerance among natural populations of drosophila buzzatii: down regulation of hsp70 expression and variation in heat stress resistance traits. Funct. Ecol. 15, 289–296 (2001).
    Google Scholar 
    34.Terblanche, J. S. et al. Ecologically relevant measures of tolerance to potentially lethal temperatures. J. Exp. Biol. 214, 3713–3725 (2011).PubMed 

    Google Scholar 
    35.Raza, M. F. et al. Gut microbiota promotes host resistance to low-temperature stress by stimulating its arginine and proline metabolism pathway in adult Bactrocera dorsalis. PLoS Pathog. 16, e1008441 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    36.Trempolec, N., Dave-Coll, N. & Nebreda, A. R. Snapshot: p38 MAPK signaling. Cell 152, 656–656.e1 (2013).CAS 
    PubMed 

    Google Scholar 
    37.Tatar, M. et al. A mutant Drosophila insulin receptor homolog that extends life-span and impairs neuroendocrine function. Science 292, 107–110 (2001).CAS 
    PubMed 

    Google Scholar 
    38.Vrailas-Mortimer, A. et al. A muscle-specific p38 MAPK/Mef2/MnSOD pathway regulates stress, motor function, and life span in Drosophila. Dev. Cell 21, 783–795 (2011).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    39.Li, F. F., Xia, J., Li, J. M., Liu, J. M. & Wang, X. W. P38 MAPK is a component of the signal transduction pathway triggering cold stress response in the med cryptic species of Bemisia tabaci. J. Integr. Agr. 11, 303–311 (2012).CAS 

    Google Scholar 
    40.Xiao, X. P. et al. A Mesh-Duox pathway regulates homeostasis in the insect gut. Nat. Microbiol. 2, 17020 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    41.Wan, F. et al. A chromosome-level genome assembly of Cydia pomonella provides insights into chemical ecology and insecticide resistance. Nat. Commun. 10, 4237–4237 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    42.Drew, R. & Yuval, B. Fruit Flies (Tephritidae): Phylogeny and Evolution of Behavior (eds Aluja, M. & Norrbom, A.) 731−749 (CRC Press, 2000).43.Dahanukar, A., Hallem, E. A. & Carlson, J. R. Insect chemoreception. Curr. Opin. Neurobiol. 15, 423–430 (2005).CAS 
    PubMed 

    Google Scholar 
    44.Bargmann, C. I. Comparative chemosensation from receptors to ecology. Nature 444, 295 (2006).CAS 
    PubMed 

    Google Scholar 
    45.Benton, R. Multigene family evolution: perspectives from insect chemoreceptors. Trends Ecol. Evol. 30, 590–600 (2015).PubMed 

    Google Scholar 
    46.Miyazaki, H. et al. Functional characterization of olfactory receptors in the Oriental fruit fly Bactrocera dorsalis that respond to plant volatiles. Insect Biochem. Mol. Biol. 101, 32–46 (2018).CAS 
    PubMed 

    Google Scholar 
    47.Ono, H. et al. Functional characterization of olfactory receptors in three Dacini fruit flies (Diptera: Tephritidae) that respond to 1-nonanol analogs as components in the rectal glands. Comp. Biochem. Physiol. B: Biochem. Mol. Biol. 239, 110346 (2020).
    Google Scholar 
    48.Harris, R. M. & Hofmann, H. A. Seeing is believing: dynamic evolution of gene families. Proc. Natl Acad. Sci. USA 112, 1252–1253 (2015).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    49.Nei, M., Niimura, Y. & Nozawa, M. The evolution of animal chemosensory receptor gene repertoires: roles of chance and necessity. Nat. Rev. Genet. 9, 951−963 (2008).50.Arguello, J. R. et al. Extensive local adaptation within the chemosensory system following Drosophila melanogaster’s global expansion. Nat. Commun. 7, 11855 (2016).
    Google Scholar 
    51.Li, S. et al. The genomic and functional landscapes of developmental plasticity in the American cockroach. Nat. Commun. 9, 1008 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    52.Vontas, J. et al. Insecticide resistance in Tephritid flies. Pestic. Biochem. Physiol. 100, 199–205 (2011).CAS 

    Google Scholar 
    53.Bergé, J. B., Feyereisen, R. & Amichot, M. Cytochrome P450 monooxygenases and insecticide resistance in insects. Philos. Trans. R. Soc. Lond. B: Biol. Sci. 353, 1701–1705 (1998).
    Google Scholar 
    54.Scott, J. G. Cytochromes P450 and insecticide resistance. Insect Biochem. Mol. Biol. 29, 757–777 (1999).CAS 
    PubMed 

    Google Scholar 
    55.Rane, R. V. et al. Detoxifying enzyme complements and host use phenotypes in 160 insect species. Curr. Opin. Insect Sci. 31, 131–138 (2019).PubMed 

    Google Scholar 
    56.Pendleton, M. et al. Assembly and diploid architecture of an individual human genome via single-molecule technologies. Nat. Methods 12, 780–786 (2015).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    57.Belton, J. M. et al. Hi-C: a comprehensive technique to capture the conformation of genomes. Methods 58, 268–276 (2012).CAS 
    PubMed 

    Google Scholar 
    58.Burton, J. N., Liachko, I., Dunham, M. J. & Shendure, J. Species-level deconvolution of metagenome assemblies with Hi-C-based contact probability maps. G3-Genes Genom. Genet. 4, 1339–1346 (2014).
    Google Scholar 
    59.Burton, J. N. et al. Chromosome-scale scaffolding of de novo genome assemblies based on chromatin interactions. Nat. Biotechnol. 31, 1119–1125 (2013).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    60.Servant, N. et al. HiC-Pro: an optimized and flexible pipeline for Hi-C data processing. Genome Biol. 16, 1–11 (2015).
    Google Scholar 
    61.Koren, S. et al. Canu: scalable and accurate long-read assembly via adaptive k-mer weighting and repeat separation. Genome Res. 27, 722–736 (2017).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    62.Walker, B. J. et al. Pilon: an integrated tool for comprehensive microbial variant detection and genome assembly improvement. PLoS One 9, e112963 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    63.Chakraborty, M., Baldwin-Brown, J. G., Long, A. D. & Emerson, J. J. Contiguous and accurate de novo assembly of metazoan genomes with modest long read coverage. Nucleic Acids Res. 44, e147 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    64.Chin, C. S. et al. Phased diploid genome assembly with single-molecule real-time sequencing. Nat. Methods 13, 1050–1054 (2016).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    65.Xiao, C. L. et al. MECAT: fast mapping, error correction, and de novo assembly for single-molecule sequencing reads. Nat. Methods 14, 1072–1074 (2017).CAS 
    PubMed 

    Google Scholar 
    66.Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows−Wheeler transform. Bioinformatics 25, 1754–1760 (2009).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    67.Worley, K. C. et al. Improving genomes using long reads and PBJelly 2. In International Plant & Animal Genome Conference XXI (2014).68.Krzywinski, M. et al. Circos: An information aesthetic for comparative genomics. Genome Res. 19, 1639–1645 (2009).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    69.Burge, C. & Karlin, S. Prediction of complete gene structures in human genomic DNA. J. Mol. Biol. 268, 78–94 (1997).CAS 
    PubMed 

    Google Scholar 
    70.Stanke, M. & Waack, S. Gene prediction with a hidden Markov model and a new intron submodel. Bioinformatics 19, ii215–ii225 (2003).PubMed 

    Google Scholar 
    71.Korf, I. Gene finding in novel genomes. BMC Bioinform. 5, 59 (2004).
    Google Scholar 
    72.Majoros, W. H., Pertea, M. & Salzberg, S. L. TigrScan and GlimmerHMM: two open source ab initio eukaryotic gene-finders. Bioinformatics 20, 2878–2879 (2004).CAS 
    PubMed 

    Google Scholar 
    73.Blanco, E., Parra, G. & Guigó, R. Using geneid to identify genes. Curr. Protoc. Bioinform. 18, 4.3.1–4.3.28 (2007).
    Google Scholar 
    74.Keilwagen, J. et al. Using intron position conservation for homology-based gene prediction. Nucleic Acids Res. 44, e89–e89 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    75.Campbell, M. A., Haas, B. J., Hamilton, J. P., Mount, S. M. & Buell, C. R. Comprehensive analysis of alternative splicing in rice and comparative analyses with Arabidopsis. BMC Genomics 7, 327 (2006).PubMed 
    PubMed Central 

    Google Scholar 
    76.Haas, B. J. et al. Automated eukaryotic gene structure annotation using EVidenceModeler and the Program to Assemble Spliced Alignments. Genome Biol. 9, R7 (2008).PubMed 
    PubMed Central 

    Google Scholar 
    77.Lowe, T. M. & Eddy, S. R. tRNAscan-SE: a program for improved detection of transfer RNA genes in genomic sequence. Nucleic Acids Res. 25, 955–964 (1997).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    78.Griffiths-Jones, S. et al. Rfam: annotating non-coding RNAs in complete genomes. Nucleic Acids Res. 33, D121–D124 (2005).CAS 
    PubMed 

    Google Scholar 
    79.Griffiths-Jones, S., Grocock, R. J., Van Dongen, S., Bateman, A. & Enright, A. J. miRBase: microRNA sequences, targets, and gene nomenclature. Nucleic Acids Res. 34, D140–D144 (2006).CAS 
    PubMed 

    Google Scholar 
    80.Nawrocki, E. P. & Eddy, S. R. Infernal 1.1: 100-fold faster RNA homology searches. Bioinformatics 29, 2933–2935 (2013).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    81.Edgar, R. C. & Myers, E. W. PILER: Identification and classification of genomic repeats. Bioinformatics 21, i152–i158 (2005).CAS 
    PubMed 

    Google Scholar 
    82.Price, A. L., Jones, N. C. & Pevzner, P. A. De novo identification of repeat families in large genomes. Bioinformatics 21, i351–i358 (2005).CAS 
    PubMed 

    Google Scholar 
    83.Xu, Z. & Wang, H. LTR_FINDER: an efficient tool for the prediction of full-length LTR retrotransposons. Nucleic Acids Res. 35, W265–W268 (2007).PubMed 
    PubMed Central 

    Google Scholar 
    84.Han, Y. & Wessler, S. R. MITE-Hunter: a program for discovering miniature inverted-repeat transposable elements from genomic sequences. Nucleic Acids Res. 38, e199 (2010).PubMed 
    PubMed Central 

    Google Scholar 
    85.Jurka, J. et al. Repbase Update, a database of eukaryotic repetitive elements. Cytogenet Genome Res. 110, 462–467 (2005).CAS 
    PubMed 

    Google Scholar 
    86.Wicker, T. et al. A unified classification system for eukaryotic transposable elements. Nat. Rev. Genet. 8, 973–982 (2007).CAS 
    PubMed 

    Google Scholar 
    87.Tarailo-Graovac, M. & Chen, N. S. Using RepeatMasker to identify repetitive elements in genomic sequences. Curr. Protoc. Bioinform. 25, 4.10.1–4.10.14 (2009).
    Google Scholar 
    88.Wang, Y. et al. MCScanX: a toolkit for detection and evolutionary analysis of gene synteny and collinearity. Nucleic Acids Res. 40, e49 (2012).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    89.Birney, E., Clamp, M. & Durbin, R. GeneWise and Genomewise. Genome Res. 14, 988–995 (2004).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    90.She, R., Chu, J. S. C., Wang, K., Pei, J. & Chen, N. S. GenBlastA: enabling BLAST to identify homologous gene sequences. Genome Res. 19, 143–149 (2009).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    91.Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).CAS 
    PubMed 

    Google Scholar 
    92.Kanehisa, M. & Goto, S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27–30 (2000).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    93.Tatusov, R. L. et al. The COG database: new developments in phylogenetic classification of proteins from complete genomes. Nucleic Acids Res. 29, 22–28 (2001).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    94.Boeckmann, B. et al. The SWISS-PROT protein KnowledgeBase and its supplement TrEMBL in 2003. Nucleic Acids Res. 31, 365–370 (2003).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    95.Marchler-Bauer, A. et al. CDD: a Conserved Domain Database for the functional annotation of proteins. Nucleic Acids Res. 39, D225–D229 (2011).CAS 
    PubMed 

    Google Scholar 
    96.Conesa, A. et al. Blast2GO: a universal tool for annotation, visualization, and analysis in functional genomics research. Bioinformatics 21, 3674–3676 (2005).CAS 

    Google Scholar 
    97.Dimmer, E. C. et al. The UniProt-GO annotation database in 2011. Nucleic Acids Res. 40, D565–D570 (2012).CAS 
    PubMed 

    Google Scholar 
    98.Bairoch, A. PROSITE-a dictionary of sites and patterns in proteins. Nucleic Acids Res. 19, 2241–2245 (1991).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    99.Attwood, T. K. & Beck, M. E. Prints-a protein motif fingerprint database. Protein Eng. Des. Sel. 7, 841–848 (1994).CAS 

    Google Scholar 
    100.Zdobnov, E. M. & Apweiler, R. InterProScan-an integration platform for the signature-recognition methods in InterPro. Bioinformatics 17, 847–848 (2001).CAS 
    PubMed 

    Google Scholar 
    101.Gough, J. & Chothia, C. SUPERFAMILY: HMMs representing all proteins of known structure. SCOP sequence searches, alignments, and genome assignments. Nucleic Acids Res. 30, 268–272 (2002).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    102.Haft, D. H., Selengut, J. D. & White, O. The TIGRFAMs database of protein families. Nucleic Acids Res. 31, 371–373 (2003).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    103.Thomas, P. D. et al. PANTHER: a browsable database of gene products organized by biological function, using curated protein family and subfamily classification. Nucleic Acids Res. 31, 334–341 (2003).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    104.Letunic, I. et al. SMART 4.0: towards genomic data integration. Nucleic Acids Res. 32, D142–D144 (2004).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    105.Wu, C. H. et al. PIRSF: family classification system at the Protein Information Resource. Nucleic Acids Res. 32, D112–D114 (2004).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    106.Bru, C. et al. The ProDom database of protein domain families: more emphasis on 3D. Nucleic Acids Res. 33, D212–D215 (2005).CAS 
    PubMed 

    Google Scholar 
    107.Finn, R. D. et al. Pfam: clans, web tools, and services. Nucleic Acids Res. 34, D247–D251 (2006).CAS 
    PubMed 

    Google Scholar 
    108.Lima, T. et al. HAMAP: a database of completely sequenced microbial proteome sets and manually curated microbial protein families in UniProtKB/Swiss-Prot. Nucleic Acids Res. 37, D471–D478 (2009).CAS 
    PubMed 

    Google Scholar 
    109.Lees, J. et al. Gene3D: a domain-based resource for comparative genomics, functional annotation, and protein network analysis. Nucleic Acids Res. 40, D465–D471 (2012).CAS 
    PubMed 

    Google Scholar 
    110.Emms, D. M. & Kelly, S. OrthoFinder: phylogenetic orthology inference for comparative genomics. Genome Biol. 20, 238 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    111.Mi, H. Y., Muruganujan, A., Ebert, D., Huang, X. S. & Thomas, P. D. PANTHER version 14: more genomes, a new PANTHER GO-slim, and improvements in enrichment analysis tools. Nucleic Acids Res. 47, D419–D426 (2019).CAS 

    Google Scholar 
    112.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 
    PubMed Central 

    Google Scholar 
    113.Puttick, M. N. MCMCtreeR: functions to prepare MCMCtree analyses and visualize posterior ages on trees. Bioinformatics 35, 5321–5322 (2019).CAS 
    PubMed 

    Google Scholar 
    114.Yang, Z. H. PAML: a program package for phylogenetic analysis by maximum likelihood. Bioinformatics 13, 555–556 (1997).CAS 

    Google Scholar 
    115.Han, M. V., Thomas, G. W. C., Lugo-Martinez, J. & Hahn, M. W. Estimating gene gain and loss rates in the presence of error in genome assembly and annotation using CAFE 3. Mol. Biol. Evol. 30, 1987–1997 (2013).CAS 
    PubMed 

    Google Scholar 
    116.Larkin, M. A. et al. ClustalW and ClustalX version 2. Bioinformatics 23, 2947–2948 (2007).CAS 
    PubMed 

    Google Scholar 
    117.Subramanian, B., Gao, S., Lercher, M. J., Hu, S. & Chen, W. H. Evolview v3: a webserver for visualization, annotation, and management of phylogenetic trees. Nucleic Acids Res 47, W270–W275 (2019).CAS 
    PubMed 
    PubMed Central 

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

    Google Scholar 
    119.Kim, D., Landmead, B. & Salzberg, S. L. HISAT: a fast spliced aligner with low memory requirements. Nat. Methods 12, 357–360 (2015).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    120.Pertea, M. et al. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat. Biotechnol. 33, 290–295 (2015).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    121.Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    122.Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinform. 12, 323 (2011).CAS 

    Google Scholar  More

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    Two million species catalogued by 500 experts

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    11 January 2022

    Two million species catalogued by 500 experts

    Mark John Costello

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    R. Edward DeWalt

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    Thomas M. Orrell

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    Olaf Banki

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    Mark John Costello

    Nord University, Bodø, Norway.

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    R. Edward DeWalt

    University of Illinois, Champaign, Illinois, USA.

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    Thomas M. Orrell

    Smithsonian Institution, Washington DC, USA.

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    Olaf Banki

    Naturalis Biodiversity Centre, Leiden, the Netherlands.

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    More than two million accepted species are now listed in the open-access Catalogue of Life (go.nature.com/3ym3h2g). This achievement addresses a major impediment to the management of biodiversity data by presenting an almost complete index of accepted names and known synonyms.

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    Nature 601, 191 (2022)
    doi: https://doi.org/10.1038/d41586-022-00010-z

    Competing Interests
    The authors declare no competing interests.

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    EU Nature Restoration Law needs ambitious and binding targets

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    11 January 2022

    EU Nature Restoration Law needs ambitious and binding targets

    Kris Decleer

     ORCID: http://orcid.org/0000-0001-9621-8925

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    Jordi Cortina-Segarra

     ORCID: http://orcid.org/0000-0002-8231-3793

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    Aveliina Helm

     ORCID: http://orcid.org/0000-0003-2338-4564

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    Kris Decleer

    Research Institute for Nature and Forest, Brussels, Belgium.

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    Jordi Cortina-Segarra

    University of Alicante, Alicante, Spain.

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    Aveliina Helm

    University of Tartu, Tartu, Estonia.

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    Initiatives by the European Commission to restore the continent’s degraded areas (J. Cortina-Segarra et al. Nature 535, 231; 2016) have proved disappointing. As the United Nations Decade on Ecosystem Restoration gathers momentum, the commission is preparing a law that has legally binding targets. To underscore the urgency, some 1,400 European scientists and 30 expert networks and institutions have signed a declaration by the Society for Ecological Restoration Europe (see go.nature.com/3st6k88).

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    Nature 601, 191 (2022)
    doi: https://doi.org/10.1038/d41586-022-00011-y

    Competing Interests
    The authors declare no competing interests.

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    The metabolic cost of turning right side up in the Mediterranean spur-thighed tortoise (Testudo graeca)

    1.Lyson, T. R. et al. Origin of the unique ventilatory apparatus of turtles. Nat. Commun. 5(5211), 1–11. https://doi.org/10.1038/ncomms6211 (2014).CAS 
    Article 

    Google Scholar 
    2.Gans, C. & Hughes, G. The mechanism of lung ventilation in the tortoise Testudo graeca Linné. J. Exp. Biol. 47(1), 1–20 (1967).CAS 
    Article 
    PubMed 

    Google Scholar 
    3.Jackson, D. C., Singer, J. H. & Downey, P. T. Oxidative cost of breathing in the turtle Chrysemys picta bellii. Am. J. Physiol. 261, R1325–R1328 (1991).CAS 
    PubMed 

    Google Scholar 
    4.Landberg, T., Mailhot, J. D. & Brainerd, E. L. Lung ventilation during treadmill locomotion in a semi-aquatic turtle, Trachemys scripta. J. Exp. Zool. 311A, 551–562. https://doi.org/10.1002/jez.478 (2009).Article 

    Google Scholar 
    5.Ruhr, I., Rose, K., Sellers, W., Crossley, D. II. & Codd, J. Turning turtle: Scaling relationships and self-righting ability in Chelydra serpentina. Proc. R. Soc. B. 288, 20210213. https://doi.org/10.1098/rspb.2021.0213 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    6.Pritchard, P. C. H. Encyclopaedia of Turtles (TFH, 1979).
    Google Scholar 
    7.Carr, A. Handbook of Turtles: The Turtles of the United States, Canada, and Baja California (Cornell University Press, 1952).
    Google Scholar 
    8.Rivera, G. Ecomorphological variation in shell shape of the freshwater turtle Pseudemys concinna inhabiting different aquatic flow regimes. Int. Comp. Biol. 48(6), 769–787. https://doi.org/10.1093/icb/icn088 (2008).Article 

    Google Scholar 
    9.McNeill Alexander, R. Gaits of mammals and turtles. J. R. Soc. Jpn. 11(3), 314–319 (1993).Article 

    Google Scholar 
    10.Zani, P. A. & Kram, R. Low metabolic cost of locomotion in ornate box turtles, Terrapene ornate. J. Exp. Biol. 211, 3671–3676. https://doi.org/10.1242/jeb.019869 (2008).Article 
    PubMed 

    Google Scholar 
    11.Sellers, W. I., Rose, K. A. R., Crossley, D. A. II. & Codd, J. R. Inferring cost of transport from whole-body kinematics in three sympatric turtle species with different locomotor habits. Comp. Biochem. Physiol. A. 247, 110739. https://doi.org/10.1016/j.cbpa.2020.110739 (2020).CAS 
    Article 

    Google Scholar 
    12.Chiari, Y., van der Meijden, A., Caccone, A., Claude, J. & Gilles, B. Self-righting potential and the evolution of shell shape in Galápagos tortoises. Sci. Rep. 7(1), 1–8. https://doi.org/10.1038/s41598-017-15787-7 (2017).CAS 
    Article 

    Google Scholar 
    13.Woledge, R. C. The energetics of tortoise muscle. J. Physiol. 197(3), 685–707 (1968).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    14.Steyermark, A. C. & Spotila, J. R. Body temperature and maternal identity affect snapping turtle (Chelydra serpentina) righting response. Copeia 4, 1050–1057. https://doi.org/10.1643/0045-8511(2001)001[1050:BTAMIA]2.0.CO;2 (2001).Article 

    Google Scholar 
    15.Rubin, A. M., Blob, R. W. & Mayerl, C. J. Biomechanical factors influencing successful self-righting in the Pleurodire turtle, Emydura subglobosa. J. Exp. Biol. 221, jeb182642. https://doi.org/10.1242/jeb.182642 (2018).Article 
    PubMed 

    Google Scholar 
    16.Penn, D. & Brockmann, H. J. Age-biased stranding and righting in male horseshoe crabs, Limulus polyphemus. Anim. Behav. 49, 1531–1539. https://doi.org/10.1016/003-3472(95)90074-8 (1995).Article 

    Google Scholar 
    17.Bonnet, X. et al. Sexual dimorphism in steppe tortoises (Testudo horsfieldii): Influence of the environment and sexual selection on body shape and mobility. Biol. J. Linn. Soc. 72, 357–372. https://doi.org/10.1006/bjls.2000.0504 (2001).Article 

    Google Scholar 
    18.Zuffi, M. A. L. & Corti, C. Aspects of population ecology of Testudo hermanni hermanni from Asinara Island, NW Sardinia (Italy, Western Mediterranean Sea): Preliminary data. Amphib-Reptil. 24, 441–447 (2003).Article 

    Google Scholar 
    19.Domokos, G. & Várkonyi, P. L. Geometry and self-righting of turtles. Proc. R. Soc. B. 275(1630), 11–17. https://doi.org/10.1098/rspb.2007.1188 (2008).Article 
    PubMed 

    Google Scholar 
    20.Mann, G. K. H., O’Riain, M. J. & Hofmeyr, M. D. Shaping up to fight: Sexual selection influences body shape and size in the fighting tortoise (Chersina angulata). J. Zool. 269, 373–379. https://doi.org/10.1111/j.1469-7998.2006.00079x (2006).Article 

    Google Scholar 
    21.Golubović, A., Bonnet, X., Djordjević, S., Djurakic, M. & Tomović, L. Variations in righting behavior across Hermann’s tortoise populations. J. Zool. 291, 69–75. https://doi.org/10.1111/jzo.12047 (2013).Article 

    Google Scholar 
    22.Golubović, A., Andelkovic, M., Arsovski, D., Bonnet, X. & Tomović, L. Locomotor performances reflect habitat constraints in an armoured species. Behav. Ecol. Sociobiol. 71, 93. https://doi.org/10.1007/s00265-017-2318-0 (2017).Article 

    Google Scholar 
    23.Ashe, V. M. The righting reflex in turtles: A description and comparison. Psychol. Sci. 20, 150–152. https://doi.org/10.3758/BF03335647 (1970).Article 

    Google Scholar 
    24.Golubović, A., Tomović, L. & Ivanović, A. Geometry of self-righting: The case of Hermann’s tortoises. Zool. Anz. 254, 99–105. https://doi.org/10.1016/j.jcz.2014.12.003 (2015).Article 

    Google Scholar 
    25.Finkler, M. S. Influence of water availability during hatching on hatchling size, body composition, desiccation tolerance, and terrestrial locomotor performance in the snapping turtle, Chelydra serpentina. Physiol. Biochem. Zool. 72, 714–722. https://doi.org/10.1086/316711 (1999).CAS 
    Article 
    PubMed 

    Google Scholar 
    26.Stojadinović, D., Milošević, D. & Crnobrnja-Isailović, J. Righting time versus shell size and shape dimorphism in adult Hermann’s tortoises: Field observations meet theoretical predictions. Anim. Biol. 63(4), 381–396. https://doi.org/10.1163/15707563-00002420 (2013).Article 

    Google Scholar 
    27.Delmas, V., Baudry, E., Girondot, M. & Prevot-Julliard, A.-C. The righting reflex as a fitness indicator in freshwater turtles. Biol. J. Linn. Soc. 91, 99–109. https://doi.org/10.1111/j.1095-8312/2007.00780.x (2007).Article 

    Google Scholar 
    28.Burger, J. Behavior of hatchling diamondback terrapins (Malaclemys terrapin) in the field. Copeia 1976, 742. https://doi.org/10.2307/1443457 (1976).Article 

    Google Scholar 
    29.Landberg, T., Mailhot, J. D. & Brainerd, E. L. Lung ventilation during treadmill locomotion in a terrestrial turtle, Terrapene carolina. J. Exp. Biol. 206, 3391–3404. https://doi.org/10.1242/jeb.00553 (2003).Article 
    PubMed 

    Google Scholar 
    30.Gaunt, A. S. & Gans, C. Mechanics of respiration in the snapping turtle, Chelydra serpentina (Linné). J. Morph. 128, 195–227. https://doi.org/10.1002/jmor.1051280205 (1969).Article 

    Google Scholar 
    31.Lambertz, M., Böhme, W. & Perry, S. F. The anatomy of the respiratory system in Platysternon megacephalum Gray, 1831 (Testudines: Crytodira) and related species, and its phylogenetic implications. Comp. Biochem. Physiol. 156, 330–336. https://doi.org/10.1016/j.cbpa.2009.12.016 (2010).CAS 
    Article 

    Google Scholar 
    32.de Souza, R. B. B. & Klein, W. The influence of the post-pulmonary septum and submersion on the pulmonary mechanics of Trachemys scripta (Cryptodira: Emydidae). J. Exp. Biol. 224(12), 242386. https://doi.org/10.1242/jeb.242386 (2021).Article 

    Google Scholar 
    33.Jodice, P. G. R., Epperson, D. M. & Visser, G. H. Daily energy expenditure in free-ranging gopher tortoises (Gopherus polyphemus). Copeia 2006(1), 129–136. https://doi.org/10.1643/0045-8511(2006)006[0129:DEEIFG]2.0.CO;2 (2006).Article 

    Google Scholar 
    34.Zera, A. J. & Harshman, L. G. The physiology of life history trade-offs in animals. Ann. Rev. Ecol. Syst. 32, 95–126. https://doi.org/10.1146/annurev.ecolsys.32.081501.114006 (2001).Article 

    Google Scholar 
    35.Shadmehr, R., Huang, H. J. & Ahmed, A. A. A representation of effort in decision-making and motor control. Curr. Biol. 26, 1929–1934. https://doi.org/10.1016/j.cub.2016.05.065 (2016).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    36.Shepard, E. L. C. et al. Energy landscapes shapes animal movement ecology. Am. Nat. 182(3), 298–312. https://doi.org/10.1086/671257 (2013).Article 
    PubMed 

    Google Scholar 
    37.Baudinette, R. V., Miller, A. M. & Sarre, M. P. Aquatic and terrestrial locomotory energetics in a toad and a turtle: A search for generalisations among ectotherms. Physiol. Biochem. Zool. 73(6), 672–682. https://doi.org/10.1086/318101 (2000).CAS 
    Article 
    PubMed 

    Google Scholar 
    38.Hailey, A. & Coulson, I. M. Measurement of time budgets from continuous observation of thread-trailed tortoises (Kinixys spekii). Herp. J. 9, 15–20 (1999).
    Google Scholar 
    39.Kram, R. & Taylor, C. R. Energetics of running: A new perspective. Nature 346, 265–267. https://doi.org/10.1038/346265a0 (1990).CAS 
    Article 
    ADS 
    PubMed 

    Google Scholar 
    40.Taylor, C. R. Relating mechanics and energetics during exercise. Adv. Vet. Sci. Comp. Med. 38A, 181–215 (1994).CAS 
    PubMed 

    Google Scholar 
    41.Cavagna, G. A. & Kaneko, M. Mechanical work and efficiency in level walking and running. J. Physiol. 268(2), 467–481. https://doi.org/10.1113/jphysiol.1977.sp011866 (1977).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    42.Carrier, D. R., Deban, S. M. & Fischbein, T. Locomotor function of the pectoral girdle “muscular sling” in trotting dogs. J. Exp. Biol. 209, 2224–2237. https://doi.org/10.1242/jeb.02236 (2006).Article 
    PubMed 

    Google Scholar 
    43.Heglund, N. C. & Cavagna, G. A. Efficiency of vertebrate locomotory muscles. J. Exp. Biol. 115, 283–292. https://doi.org/10.1242/jeb.115.1.283 (1985).CAS 
    Article 
    PubMed 

    Google Scholar 
    44.Barclay, C. J. The basis of difference in thermodynamic efficiency among skeletal muscles. Clin. Exp. Pharm. Physiol. 44(12), 1279–1286. https://doi.org/10.1111/1440-1681.12850 (2017).MathSciNet 
    CAS 
    Article 

    Google Scholar 
    45.Nwoye, L. O. & Goldspink, G. Biochemical efficiency and intrinsic shortening speed in selected fast and slow muscles. Experientia 37, 856–857. https://doi.org/10.1007/BF1985678 (1981).CAS 
    Article 
    PubMed 

    Google Scholar 
    46.Lambert, M. Temperature, activity and field sighting in the Mediterranean spur-thighed or common garden tortoise Testudo graeca. Biol. Conserv. 21, 39–54. https://doi.org/10.1016/0006-3207(81)90067-7 (1981).Article 

    Google Scholar 
    47.Tracy, R., Zimmerman, L., Tracy, C., Bradley, K. & Castle, K. Rates of food passage in the digestive tract of young desert tortoises: Effects of body size and diet quality. Chelonian Conserv. Biol. 5(2), 269–273. https://doi.org/10.2744/1071-8443(2006)5[269:ROFPIT]2.0.co;2 (2006).Article 

    Google Scholar 
    48.Huey, R. & Kingsolver, J. Evolution of thermal sensitivity of ectotherm performance. Trends Ecol. Evol. 4(5), 131–135. https://doi.org/10.1016/0169-5347(89)90211-5 (1989).CAS 
    Article 
    PubMed 

    Google Scholar 
    49.Lailvaux, S. & Irschick, D. Effects of temperature and sex on jump performance and biomechanics in the lizard Anolis carolinensis. Funct. Ecol. 21(3), 534–543. https://doi.org/10.1111/j.1365-2435.2007.01263.x (2007).Article 

    Google Scholar 
    50.Lighton, J. Measuring Metabolic Rates: A Manual for Scientists (Oxford University Press, 2008).Book 

    Google Scholar 
    51.Brody, S. Bioenergetics and Growth (Reinhold, 1945).
    Google Scholar  More

  • in

    The 2018 European heatwave led to stem dehydration but not to consistent growth reductions in forests

    1.Rahmstorf, S. & Coumou, D. Increase of extreme events in a warming world. Proc. Natl Acad. Sci. 108, 17905–17909 (2011).CAS 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    2.Barriopedro, D., Fischer, E. M., Luterbacher, J., Trigo, R. M. & Garcia-Herrera, R. The Hot Summer of 2010: Redrawing the temperature record map of Europe. Science 332, 220–224 (2011).CAS 
    PubMed 
    ADS 

    Google Scholar 
    3.Fischer, E. M. & Knutti, R. Detection of spatially aggregated changes in temperature and precipitation extremes. Geophys. Res. Lett. 41, 547–554 (2014).ADS 

    Google Scholar 
    4.Della-Marta, P. M., Haylock, M. R., Luterbacher, J. & Wanner, H. Doubled length of western European summer heat waves since 1880. J. Geophys. Res. 112, D15103 (2007).ADS 

    Google Scholar 
    5.Zscheischler, J. et al. Future climate risk from compound events. Nat. Clim. Chang. 8, 469–477 (2018).ADS 

    Google Scholar 
    6.Teskey, R. et al. Responses of tree species to heat waves and extreme heat events. Plant, Cell Environ. 38, 1699–1712 (2015).
    Google Scholar 
    7.Ciais, P. et al. Europe-wide reduction in primary productivity caused by the heat and drought in 2003. Nature 437, 529–533 (2005).CAS 
    PubMed 
    ADS 

    Google Scholar 
    8.Steppe, K., Sterck, F. & Deslauriers, A. Diel growth dynamics in tree stems: linking anatomy and ecophysiology. Trends Plant Sci. 20, 335–343 (2015).CAS 
    PubMed 

    Google Scholar 
    9.Peters, R. L. et al. Turgor—a limiting factor for radial growth in mature conifers along an elevational gradient. N. Phytol. 229, 213–229 (2021).CAS 

    Google Scholar 
    10.Meinzer, F. C., Johnson, D. M., Lachenbruch, B., McCulloh, K. A. & Woodruff, D. R. Xylem hydraulic safety margins in woody plants: Coordination of stomatal control of xylem tension with hydraulic capacitance. Funct. Ecol. 23, 922–930 (2009).
    Google Scholar 
    11.Anderegg, W. R. L., Berry, J. A. & Field, C. B. Linking definitions, mechanisms, and modeling of drought-induced tree death. Trends Plant Sci. 17, 693–700 (2012).CAS 
    PubMed 

    Google Scholar 
    12.Martínez‐Vilalta, J., Anderegg, W. R. L., Sapes, G. & Sala, A. Greater focus on water pools may improve our ability to understand and anticipate drought‐induced mortality in plants. N. Phytol. 223, 22–32 (2019).
    Google Scholar 
    13.Zweifel, R., Haeni, M., Buchmann, N. & Eugster, W. Are trees able to grow in periods of stem shrinkage? N. Phytol. 211, 839–849 (2016).
    Google Scholar 
    14.Dietrich, L., Zweifel, R. & Kahmen, A. Daily stem diameter variations can predict the canopy water status of mature temperate trees. Tree Physiol. 38, 941–952 (2018).PubMed 

    Google Scholar 
    15.Zweifel, R. et al. Why trees grow at night. N. Phytol. 231, 2174–2185 (2021).
    Google Scholar 
    16.Buras, A., Rammig, A. & Zang, C. S. Quantifying impacts of the 2018 drought on European ecosystems in comparison to 2003. Biogeosciences 17, 1655–1672 (2020).ADS 

    Google Scholar 
    17.Bastos, A. et al. Direct and seasonal legacy effects of the 2018 heat wave and drought on European ecosystem productivity. Sci. Adv. 6, eaba2724 (2020).CAS 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    18.Peters, W., Bastos, A., Ciais, P. & Vermeulen, A. A historical, geographical, and ecological perspective on the 2018 European summer drought. Philos. Trans. R. Soc. B Biol. Sci. 375, 20190505 (2020).
    Google Scholar 
    19.Albergel, C. et al. Monitoring and Forecasting the Impact of the 2018 Summer Heatwave on Vegetation. Remote Sens. 11, 520 (2019).ADS 

    Google Scholar 
    20.Smith, N. E. et al. Spring enhancement and summer reduction in carbon uptake during the 2018 drought in northwestern. Eur. Philos. Trans. R. Soc. B Biol. Sci. 375, 20190509 (2020).CAS 

    Google Scholar 
    21.Brun, P. et al. Large‐scale early‐wilting response of Central European forests to the 2018 extreme drought. Glob. Chang. Biol. 26, 7021–7035 (2020).PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    22.Ramonet, M. et al. The fingerprint of the summer 2018 drought in Europe on ground-based atmospheric CO2 measurements. Philos. Trans. R. Soc. B Biol. Sci. 375, 20190513 (2020).CAS 

    Google Scholar 
    23.Bastos, A. et al. Impacts of extreme summers on European ecosystems: A comparative analysis of 2003, 2010 and 2018. Philos. Trans. R. Soc. B Biol. Sci. 375, 20190507 (2020).CAS 

    Google Scholar 
    24.Lin, Y.-S. et al. Optimal stomatal behaviour around the world. Nat. Clim. Chang. 5, 459–464 (2015).CAS 
    ADS 

    Google Scholar 
    25.Schuldt, B. et al. A first assessment of the impact of the extreme 2018 summer drought on Central European forests. Basic Appl. Ecol. 45, 86–103 (2020).
    Google Scholar 
    26.Rita, A. et al. The impact of drought spells on forests depends on site conditions: The case of 2017 summer heat wave in southern Europe. Glob. Chang. Biol. 26, 851–863 (2020).PubMed 
    ADS 

    Google Scholar 
    27.Hanewinkel, M., Cullmann, D. A., Schelhaas, M.-J., Nabuurs, G.-J. & Zimmermann, N. E. Climate change may cause severe loss in the economic value of European forest land. Nat. Clim. Chang. 3, 203–207 (2013).ADS 

    Google Scholar 
    28.Larysch, E., Stangler, D. F., Nazari, M., Seifert, T. & Kahle, H.-P. Xylem phenology and growth response of European beech, silver fir and scots pine along an elevational gradient during the extreme drought year 2018. Forests 12, 75 (2021).
    Google Scholar 
    29.Rohner, B., Kumar, S., Liechti, K., Gessler, A. & Ferretti, M. Tree vitality indicators revealed a rapid response of beech forests to the 2018 drought. Ecol. Indic. 120, 106903 (2021).
    Google Scholar 
    30.Scharnweber, T., Smiljanic, M., Cruz-García, R., Manthey, M. & Wilmking, M. Tree growth at the end of the 21st century – the extreme years 2018/19 as template for future growth conditions. Environ. Res. Lett. 15, 074022 (2020).ADS 

    Google Scholar 
    31.Kowalska, N. et al. Analysis of floodplain forest sensitivity to drought. Philos. Trans. R. Soc. B Biol. Sci. 375, 20190518 (2020).CAS 

    Google Scholar 
    32.Zweifel, R. et al. Baumwasserdefizite erreichten im Sommer 2018 Höchstwerte–war das aus dem All erkennbar. Schweiz Z. Forstwes. 171, 302–305 (2020).
    Google Scholar 
    33.Cuny, H. E. et al. Woody biomass production lags stem-girth increase by over one month in coniferous forests. Nat. Plants 1, 15160 (2015).CAS 
    PubMed 

    Google Scholar 
    34.D’Orangeville, L. et al. Drought timing and local climate determine the sensitivity of eastern temperate forests to drought. Glob. Chang. Biol. 24, 2339–2351 (2018).PubMed 
    ADS 

    Google Scholar 
    35.Delpierre, N., Berveiller, D., Granda, E. & Dufrêne, E. Wood phenology, not carbon input, controls the interannual variability of wood growth in a temperate oak forest. N. Phytol. 210, 459–470 (2016).CAS 

    Google Scholar 
    36.Babst, F. et al. Above-ground woody carbon sequestration measured from tree rings is coherent with net ecosystem productivity at five eddy-covariance sites. N. Phytol. 201, 1289–1303 (2014).CAS 

    Google Scholar 
    37.Zweifel, R. et al. Determinants of legacy effects in pine trees – implications from an irrigation‐stop experiment. N. Phytol. 227, 1081–1096 (2020).CAS 

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

    Google Scholar 
    39.Zweifel, R., Zimmermann, L. & Newbery, D. M. Modeling tree water deficit from microclimate: An approach to quantifying drought stress. Tree Physiol. 25, 147–156 (2005).CAS 
    PubMed 

    Google Scholar 
    40.Gleason, S. M. et al. Weak tradeoff between xylem safety and xylem-specific hydraulic efficiency across the world’s woody plant species. N. Phytol. 209, 123–136 (2016).CAS 

    Google Scholar 
    41.Duursma, R. A. et al. On the minimum leaf conductance: its role in models of plant water use, and ecological and environmental controls. N. Phytol. 221, 693–705 (2019).
    Google Scholar 
    42.Poyatos, R., Aguadé, D. & Martínez-Vilalta, J. Below-ground hydraulic constraints during drought-induced decline in Scots pine. Ann. Sci. 75, 100 (2018).
    Google Scholar 
    43.Johnson, D. M., McCulloh, K. A., Woodruff, D. R. & Meinzer, F. C. Hydraulic safety margins and embolism reversal in stems and leaves: Why are conifers and angiosperms so different? Plant Sci. 195, 48–53 (2012).CAS 
    PubMed 

    Google Scholar 
    44.Brodribb, T. J., McAdam, S. A. M., Jordan, G. J. & Martins, S. C. V. Conifer species adapt to low-rainfall climates by following one of two divergent pathways. Proc. Natl Acad. Sci. U.S.A 111, 14489–14493 (2014).CAS 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    45.Choat, B. et al. Global convergence in the vulnerability of forests to drought. Nature 491, 752–755 (2012).CAS 
    PubMed 
    ADS 

    Google Scholar 
    46.Drake, J. E. et al. Trees tolerate an extreme heatwave via sustained transpirational cooling and increased leaf thermal tolerance. Glob. Chang. Biol. 24, 2390–2402 (2018).PubMed 
    ADS 

    Google Scholar 
    47.Anderegg, W. R. L., Trugman, A. T., Badgley, G., Konings, A. G. & Shaw, J. Divergent forest sensitivity to repeated extreme droughts. Nat. Clim. Chang. 10, 1091–1095 (2020).ADS 

    Google Scholar 
    48.Leuzinger, S., Zotz, G., Asshoff, R. & Korner, C. Responses of deciduous forest trees to severe drought in Central Europe. Tree Physiol. 25, 641–650 (2005).PubMed 

    Google Scholar 
    49.Brinkmann, N., Eugster, W., Zweifel, R., Buchmann, N. & Kahmen, A. Temperate tree species show identical response in tree water deficit but different sensitivities in sap flow to summer soil drying. Tree Physiol. 36, 1508–1519 (2016).PubMed 

    Google Scholar 
    50.Rosengren, U. et al. Functional biodiversity aspects on the nutrient sustainability in forests-Importance of root distribution. J. Sustain. 21, 77–100 (2006).
    Google Scholar 
    51.Salomón, R. L., Limousin, J.-M., Ourcival, J.-M., Rodríguez-Calcerrada, J. & Steppe, K. Stem hydraulic capacitance decreases with drought stress: implications for modelling tree hydraulics in the Mediterranean oak Quercus ilex. Plant. Cell Environ. 40, 1379–1391 (2017).PubMed 

    Google Scholar 
    52.Mencuccini, M. et al. Leaf economics and plant hydraulics drive leaf: wood area ratios. N. Phytol. 224, 1544–1556 (2019).
    Google Scholar 
    53.Guerrero-Ramírez, N. R. et al. Global root traits (GRooT). Database Glob. Ecol. Biogeogr. 30, 25–37 (2021).
    Google Scholar 
    54.Kattge, J. et al. TRY plant trait database—enhanced coverage and open access. Glob. Chang. Biol. 26, 119–188 (2020).PubMed 
    ADS 

    Google Scholar 
    55.van der Maaten, E. et al. Species distribution models predict temporal but not spatial variation in forest growth. Ecol. Evol. 7, 2585–2594 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    56.Körner, C. No need for pipes when the well is dry—a comment on hydraulic failure in trees. Tree Physiol. 39, 695–700 (2019).PubMed 

    Google Scholar 
    57.Walthert, L. et al. From the comfort zone to crown dieback: Sequence of physiological stress thresholds in mature European beech trees across progressive drought. Sci. Total Environ. 753, 141792 (2021).CAS 
    PubMed 
    ADS 

    Google Scholar 
    58.Preisler, Y., Tatarinov, F., Grünzweig, J. M. & Yakir, D. Seeking the “point of no return” in the sequence of events leading to mortality of mature trees. Plant. Cell Environ. 44, 1315–1328 (2021).CAS 
    PubMed 

    Google Scholar 
    59.Poyatos, R. et al. Global transpiration data from sap flow measurements: The SAPFLUXNET database. Earth Syst. Sci. Data 13, 2607–2649 (2021).ADS 

    Google Scholar 
    60.Steppe, K., von der Crone, J. S. & De Pauw, D. J. W. TreeWatch.net: A water and carbon monitoring and modeling network to assess instant tree hydraulics and carbon status. Front. Plant Sci. 7, 993 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    61.Sass-Klaassen, U. et al. A tree-centered approach to assess impacts of extreme climatic events on forests. Front. Plant Sci. 7, 1–6 (2016).
    Google Scholar 
    62.Cailleret, M. et al. A synthesis of radial growth patterns preceding tree mortality. Glob. Chang. Biol. 23, 1675–1690 (2017).PubMed 
    ADS 

    Google Scholar 
    63.Sparks, A. H., Hengl, T. & Nelson, A. GSODR: Global summary daily weather data in R. J. Open Source Softw. 2, 177 (2017).ADS 

    Google Scholar 
    64.Muñoz-Sabater, J. et al. ERA5-Land: An improved version of the ERA5 reanalysis land component. in Joint ISWG and LSA-SAF Workshop IPMA. 26–28 (2018).65.Granier, A. et al. Evidence for soil water control on carbon and water dynamics in European forests during the extremely dry year: 2003. Agric. Meteorol. 143, 123–145 (2007).
    Google Scholar 
    66.Karger, D. N. et al. Climatologies at high resolution for the earth’s land surface areas. Sci. Data 4, 170122 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    67.Cornes, R. C., van der Schrier, G., van den Besselaar, E. J. M. & Jones, P. D. An ensemble version of the E-OBS temperature and precipitation data sets. J. Geophys. Res. Atmos. 123, 9391–9409 (2018).
    Google Scholar 
    68.Frich, P. et al. Observed coherent changes in climatic extremes during the second half of the twentieth century. Clim. Res. 19, 193–212 (2002).ADS 

    Google Scholar 
    69.Alexander, L. V. et al. Global observed changes in daily climate extremes of temperature and precipitation. J. Geophys. Res. Atmos. 111, 1–22 (2006).
    Google Scholar 
    70.Knüsel, S., Peters, R. L., Haeni, M., Wilhelm, M. & Zweifel, R. Processing and extraction of seasonal tree physiological parameters from stem radius time series. Forests 12, 765 (2021).
    Google Scholar 
    71.Hedges, L. V., Gurevitch, J. & Curtis, P. S. The meta-analysis of response ratios in experimental ecology. Ecology 80, 1150–1156 (1999).
    Google Scholar 
    72.Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
    Google Scholar 
    73.R. Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing. (2019). More

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    A taxonomic, genetic and ecological data resource for the vascular plants of Britain and Ireland

    The broad categories of data included in the repository are summarized in Online-only Table 2 and visualized in Fig. 2. Each category is explained in greater detail below, while full details together with accompanying notes are given in the repository (Database_structure.csv) and in Supplementary File 1. Online-only Table 2 gives an overview of data coverage per category, both across all species and for native species separately. A complete list of data sources is available in Supplementary File 2.Fig. 2Visualization of the attributes presented in the database.Full size imageGeneration of the species listTaxon names listed in the most recent and widely accepted New Flora of the British Isles’ index12 were digitized via the Optical Character Recognition Software ReadirisTM 17 (IRIS). Results from the digitization were transferred into a spreadsheet and obvious recognition errors were fixed. The resulting table contained 5,687 taxa and associated taxonomic authorities. A total of 360 unnamed hybrids were excluded, as well as species noted to have only questionable or unconfirmed records, leaving 5,038 species. Forty-one intergeneric hybrid species, 827 entries relating to (notho)subspecies, (notho)varieties, cultivars and forma were also removed along with 720 named hybrids. Species that were included by Stace12 but which he considered not to be part of the flora (i.e. listed as ‘other species’ and ‘other genera’, e.g. genus Tragus or Coreopsis verticillata) were also excluded. Seven species that were labelled ‘extinct’ in the flora were included as there were indications that the species might be in the process of reintroduction (e.g. Bromus interruptus, Bupleurum falcatum and Schoenoplectus pungens). Extinct native and archaeophyte species without any signs of reintroduction (e.g. Dryopteris remota) are also listed but no additional data are provided and they are not included in calculations of completeness of data (Online-only Table 2). The final number of extant species listed here is therefore 3,209 (comprising 1,468 natives, 1,690 aliens and 51 species with unknown status), plus 18 formally extinct species (natives and archaeophytes not seen in the study region since 1999). Species names and taxonomic authorities were revised according to the 2021 reprint of the New Flora of the British Isles, communicated to us by C.A.S. ahead of publication. Genera with less well-defined species – for example due to apomixis – contain additional information on subgenera, sections, and aggregates, as per Stace12. Since misidentifications are common in these groups, we include a column termed ‘unclear_species_marker’ that allows for these species to be quickly identified and excluded from analyses if appropriate. Such genera are often incompletely listed in our database since most microspecies are not sufficiently well defined.TaxonomyNomenclature of the list was checked by Global Names Resolver in the R package ‘taxize’20,21, using the International Plant Names Index (IPNI)22 as the data source, to remove any digitisation errors. Resolved names were used to determine accepted higher taxonomic hierarchy (family, order) again using taxize, with the National Center for Biotechnology Information (NCBI) database. Species that could not be resolved by the Global Names Resolver or did not yield matches in the NCBI database for their higher taxonomic ranks were manually checked for name matches in the World Checklist of Vascular Plants (WCVP)17. Species within the original species list that were found to be identical to a different spelling in WCVP were retained in the database. In such instances, and when slight spelling differences occurred, the columns ‘taxon_name‘ and ‘taxon_name_WCVP‘ differ. To improve clarity, each species is presented here with its unique identification number according to the WCVP (listed as ‘kew_id’) together with three additional columns (i.e. WCVP.URL, POWO.URL and IPNI.URL) which contain hyperlinks to the freely accessible taxon description websites of the (WCVP)17, Plants of the World Online (POWO)23 and (IPNI)22, respectively. Thus, while the taxon names used in the database correspond to those used by Stace12, changes in the accepted species name since publication can be traced in columns ‘taxonomic_status’ and ‘accepted_kew_id’. The family classification of WCVP follows APG IV24 for angiosperms, Christenhusz et al. (2011)25 for gymnosperms and Christenhusz & Chase (2014)26 for ferns and lycopods.Native statusWe offer three different datasets which describe the status of a species as native or non-native, and its level of establishment in BI. The first is extracted from Stace (2019)12, the second contains the status codes used in PLANTATT10 and the unpublished ALIENATT (pers. comm. author K.J.W.) dataset, and the third is extracted from Alien Plants13. The status from Stace12 and Stace & Crawley13 assigns a species to either native or alien status, with aliens subdivided into archaeophytes and neophytes at different levels of establishment (e.g. denizen, colonist etc., see Online-only Table 1). Status codes from the BSBI can be either AC (alien casual), AN (neophyte), AR (archaeophyte), N (native), NE (native endemic) or NA (native status doubtful).Functional traitsData for five ecologically relevant functional traits (i.e. seed mass, specific leaf area [SLA], leaf area, leaf dry matter content [LDMC] and vegetative height) were downloaded from public data available in the TRY database27 (for specific authors see Supplementary File 1 and Supplementary File 2). Averages were calculated using the available measurements downloaded for each species, excluding rows where the measurement was 0. In addition, the maximum vegetative height for each species is given, where available.Realized niche descriptionRealized niche descriptions based on assessments made on plants living in BI are given in the form of Ellenberg indicator values18, as published in PLANTATT10. Ellenberg indicator values place each species along an environmental gradient (e.g. light or salinity) by assigning a number on an ordinal scale, depending on the species preference for the specific gradient (Online-only Table 2). This information is often used to gain insights into environmental changes based on species occurrences28. For species listed under a previously accepted name in PLANTATT, the information was associated with the accepted synonym in Stace (2019)12. Due to the low coverage of PLANTATT for non-native species included in our list, we additionally include Ellenberg indicator values based on Central European assessments, as made available by Döring29. Each Ellenberg category is listed in a separate column, keeping the information from both data sources separate to avoid confounding of assessments based on two different regions (i.e. Britain and Ireland versus Central Europe).Life strategyTo characterize the life strategy of a species, we used the CSR scheme developed by Grime19, which classifies each species as either a competitor (C), stress tolerator (S), ruderal (R) or a combination of these (e.g. CS, SR). CSR classifications were obtained from the Electronic Comparative Plant Ecology database30. Due to the low coverage of available CSR assessments for species in our database (i.e. data available for just 460 out of 3,209 species) we imputed CSR strategies for a further 981 species using available functional trait data, following the method proposed by Pierce et al.31. The functional leaf traits required for this method – i.e. specific leaf area, leaf area, leaf dry matter content – were obtained from the TRY database27. Pre-existing30 and newly imputed CSR strategies are listed in separate columns.Growth form, succulence and life-formPlant growth form descriptions were obtained from the TRY database27 and filtered for those entries given by specific contributors (Online-only Table 2) to maintain consistent use of growth form categories. Information on whether a species was considered to be a succulent was obtained by screening the entire growth form information obtained from the TRY database for the phrase ‘succulence’ or ‘succulent’.Species life-form categories according to Raunkiaer32 were determined for each species in our dataset with regard to the typical life-form of the species as it grows in BI (pers. comm. M.J.M.C.).Associated biome and originInformation given in the Ecoflora database3 for the biome that each species is associated with was matched to the species names according to Stace12. The recognized biome categories follow Preston & Hill33 and are ‘Arctic montane’, ‘Boreal Montane’, ‘Boreo-Arctic Montane’, ‘Boreo-Temperate’, ‘Mediterranean’, ‘Mediterranean-Atlantic’, ‘Southern Temperate’, ‘Temperate’, ‘Wide Boreal’ and ‘Wide Temperate’.For non-native species, the assumed origin (i.e. the region that plants were most likely to have been introduced to BI from, rather than the full non-BI distribution of a species) was adapted from Stace12 into a brief description of their country or region of origin. In addition, these descriptions were manually allocated to the TDWG level 1 regions listed in the World Geographical Scheme for Recording Plant Distributions (WGSRPD, TDWG)34.Species distributionsDistribution metrics for each species are given as the number of 10-km square hectads in BI with records for the species in question within a specified time window. The data were derived from the BSBI Distribution Database35 and were extracted for each species, dividing the study region into Great Britain (incl. Isle of Man), Ireland and the Channel Islands, as previously partitioned for data available in PLANTATT10. The database was queried using species and hectads for grouping, showing only records ‘matching or within 2 km of county boundary’ and excluding ‘do-not-map-flagged occurrences’. The data were not corrected for sampling bias and should therefore only be used as an indication of trends.Hybrid propensityData on hybridization is provided for 641 species, obtained from the Hybrid flora of the British Isles36 which enumerates every hybrid reported in BI up until 2015 (pers. comm. M.R.B.). Each entry was transcribed manually, and then filtered to exclude (a) hybrids that have been recorded, but not formed in the British Isles, (b) triple hybrids (mainly reported for the genus Salix), (c) doubtful records, (d) hybrids between subspecific ranks, and (e) hybrids where at least one parent is not native (only archaeophytes included). This left 821 hybrid combinations for data aggregation. The metric chosen here is hybrid propensity, which is a per-species metric of how many other species a focal species hybridizes with (sensu Whitney et al., 201037). A scaled hybrid propensity metric is also given which was calculated by weighting the hybrid propensity score by the number of intrageneric combinations for a given genus, to account for the greater opportunities of hybridization in larger genera.DNA barcodesDNA barcode sequences for plant species present in BI are currently available for 1,413 species in our database. The information was derived from a dataset of rbcL, matK and ITS2 sequences compiled for the UK flora generated by the National Botanic Garden of Wales and the Royal Botanic Garden Edinburgh38,39 (pers. comm. L.J. and N.D.V.). The data are given as a hyperlink to the record’s page on the Barcode of Life Data Systems (BOLD40) which includes the DNA barcode sequences as well as scans of the herbarium specimen and information on the sample’s collection. Most species have multiple record pages associated with them, due to the sampling of more than one individual. We include a maximum of three BOLD accessions per species; the full range of individuals sampled can be accessed via the original publications38,39. DNA barcodes are almost exclusively available for native species. Future releases of our database will increase the coverage of the non-native flora significantly. Where species in the BOLD database are attributed to a species name that is considered synonymous with another name in our list, the hyperlink is matched to the latest nomenclature12. 1,421 species have at least one sequence associated with them and 935 species have sequence data for all three sequences (rbcL, matK and ITS2).Genome size and chromosome numbersGenome size data for 2,117 specimens (at least one measurement per species) were obtained from various sources. Measurements for a total of 467 species were newly estimated using plant material of known BI origin, often sourced  from the Millennium Seedbank of the Royal Botanic Gardens, Kew (RBG Kew)41. The measurements were made by flow cytometry using seeds or seedlings and following an established protocol42. Information on the extraction buffers and calibration standard species used are available in the file GS_Kew_BI.csv, along with peak CV values of the measurements as a quality control. Where more than one measurement is reported per species, the measurements were made on plant material from different populations or using different buffers. Previously published data for additional species were obtained from reports on the Czech flora43, the Dutch flora44, and prime values listed in the Plant DNA C-values database45,46. Since significant intraspecific differences in genome size between plant material from different geographical origins have previously been described, predominantly due to cytotype diversity in ploidy level47, genome size measurements from previously published sources were assessed with regard to the origin of the material. The column ‘from_BI_material’ (GS_BI.csv, BI_main.csv) allows users to filter for measurements made on material from BI to exclude a potential bias. The information was obtained from the original publication source of each measurement.Chromosome numbers for 1,410 species (at least one chromosome number per species) determined exclusively from material collected in BI were obtained from an extensive dataset compiled by R.J.G. from various published studies, unpublished theses and personal communications from trusted sources. The counts were made between 1898 and 2017, with a large proportion stemming from efforts to achieve greater coverage of the flora by a team of cytologists based at the University of Leicester and headed by R.J.G. Part of the dataset was previously incorporated into the BSBI’s data catalogue5 but has since undergone revisions to incorporate new information and changes in taxonomy. The dataset contained many measurements at subspecies level which were allocated to the species level taxon in our list. This served to include as much of the often considerable infraspecific variation as possible. Since some species for which chromosome counts have been reported elsewhere are lacking chromosome counts from British or Irish material, they are absent from this dataset. To fill such gaps, we also present chromosome numbers from reports on the Czech flora43, the Dutch flora44, and the Plant DNA C-values database45,46. More

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    Seasonal pattern of food habits of large herbivores in riverine alluvial grasslands of Brahmaputra floodplains, Assam

    1.Krebs, C. J. Ecological Methodology 2nd edn. (Addison Welsey Educational Publishers Inc, 1999).
    Google Scholar 
    2.Tewari, R. & Rawat, G. S. Studies on the food and feeding habits of Swamp Deer (Rucervus duvaucelii duvaucelii) in Jhilmil Jheel conservation reserve, Haridwar, Uttarakhand, India. ISRN Zool. 2013, 1–6. https://doi.org/10.1155/2013/278213 (2013).Article 

    Google Scholar 
    3.Brodeur, R. D., Smith, B. E., McBride, R. S., Heintz, R. & Farley, E. New perspectives on the feeding ecology and trophic dynamics of fishes. Environ. Biol. Fishes. 100, 293–297. https://doi.org/10.1007/s10641-017-0594-1 (2017).Article 

    Google Scholar 
    4.Vesey-FitzGerald, D. F. Grazing succession among East African game animals. J. Mammal. 41, 161–172. https://doi.org/10.2307/1376351 (1960).Article 

    Google Scholar 
    5.Lamprey, H. F. Ecological separation of the large mammal species in the Tarangire game reserve, Tanganyika. Afr. J. Ecol. 1, 63–92. https://doi.org/10.1111/j.1365-2028.1963.tb00179.x (1963).Article 

    Google Scholar 
    6.Ahrestani, F. S. Asian Eden Large Herbivore Ecology in India (Wageningen University, 2009).
    Google Scholar 
    7.Bell, R. H. V. The use of herb layer by grazing ungulates in the Serengeti. In Animal Populations in Relation to their Food Resources (eds. Watson, A.) 111–124 (Blackwell Science, 1970).8.Jarman, P. The social organisation of antelopes in relation to their ecology. Behaviour 48, 215–267. https://doi.org/10.1163/156853974X00345 (1974).Article 

    Google Scholar 
    9.Hofmann, R. R. & Stewart, D. R. M. Grazer of browser: A classification based on the stomach structure and feeding habits of East African ruminants. Mammalia 36, 226–240 (1972).Article 

    Google Scholar 
    10.Bell, R. H. V. A grazing ecosystem in the Serengeti. Sci. Am. 225, 86–93 (1971).ADS 
    Article 

    Google Scholar 
    11.Kleiber, M. The Fire of Life. An Introduction to Animal Energetics (Krieger, 1932).
    Google Scholar 
    12.Demment, M. W. & Van Soest, P. J. A nutritional explanation for body-size patterns of ruminant and nonruminant herbivores. Am. Nat. 125, 641–672. https://doi.org/10.1086/284369 (1985).Article 

    Google Scholar 
    13.Hofmann, R. R. The Ruminant Stomach: Stomach Structure and Feeding Habits of East African Game Ruminants. East African Monograph in Biology, vol. 2, 1–364 (E.A. Lit. Bureau, 1973).14.Ahrestani, F. S., Heitkönig, I. M., Matsubayashi, H. & Prins, H. H. Grazing and browsing by large herbivores in South and Southeast Asia. In The Ecology of Large Herbivores in South and Southeast Asia, (eds. Ahrestani, F. S. & Sankaran, M.) 99–120. (Springer, 2016).15.Geist, V. On the relationship of social evolution and ecology in Ungulates. Am. Zool. 14, 205–220. https://doi.org/10.1093/icb/14.1.205 (1974).Article 

    Google Scholar 
    16.Clauss, M., Steuer, P., Müller, D. W. H., Codron, D. & Hummel, J. Herbivory and body size: Allometries of diet quality and gastrointestinal physiology, and implications for herbivore ecology and dinosaur gigantism. PLoS One 8, e68714. https://doi.org/10.1371/journal.pone.0068714 (2013).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    17.Ahrestani, F. S., Heitkönig, I. M. & Prins, H. H. Diet and habitat-niche relationships within an assemblage of large herbivores in a seasonal tropical forest. J. Trop. Ecol. 28, 385–394. https://doi.org/10.1017/S0266467412000302 (2012).Article 

    Google Scholar 
    18.Pradhan, N. M., Wegge, P., Moe, S. R. & Shrestha, A. K. Feeding ecology of two endangered sympatric mega-herbivores: Asian elephant Elephas maximus and greater one-horned rhinoceros Rhinoceros unicornis in lowland Nepal. Wildl. Biol. 14, 147–154. https://doi.org/10.2981/0909-6396(2008)14[147:feotes]2.0.co;2 (2008).Article 

    Google Scholar 
    19.McNaughton, S. J. & Georgiadis, N. J. Ecology of African grazing and browsing mammals. Annu. Rev. Ecol. Syst. 17, 39–66. https://doi.org/10.1146/annurev.es.17.110186.000351 (1986).Article 

    Google Scholar 
    20.Owen-Smith, R. N. Adaptive Herbivore Ecology: From Resources to Populations in Variable Environments. Adaptive Herbivore Ecology (Cambridge University Press, 2002). https://doi.org/10.1017/CBO9780511525605.21.Olff, H., Ritchie, M. E. & Prins, H. H. T. Global environmental controls of diversity in large herbivores. Nature 415, 901–904. https://doi.org/10.1038/415901a (2002).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    22.Bailey, D. W. & Provenza, F. D. Mechanisms determining large-herbivore distribution. In Resource Ecology, vol. 23 (eds. Prins, H. H. T. & Van Langevelde, F.) 7–28 (Springer, 2008). https://doi.org/10.1007/978-1-4020-6850-8_2.23.Prins, H. H. T. & Van Langevelde, F. Assembling a diet from different places. In Resource Ecology, vol. 23 (eds. Prins, H. H. T. & Van Langevelde, F.) 129–155 (Springer, 2008). https://doi.org/10.1007/978-1-4020-6850-8_12.24.Fryxell, J. M. et al. Landscape scale, heterogeneity, and the viability of Serengeti grazers. Ecol. Lett. 8, 328–335. https://doi.org/10.1111/j.1461-0248.2005.00727.x (2005).Article 

    Google Scholar 
    25.Du Toit, J., Rogers, K. & Biggs, H. The Kruger Experience: Ecology and Management of Savanna Heterogeneity, vol. 29 (Island Press, 2003).26.Ripple, W. J. et al. Collapse of the world’s largest herbivores. Sci. Adv. 1, e1400103. https://doi.org/10.1126/sciadv.1400103 (2015).ADS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    27.Menon, V. Indian Mammals: A Field Guide. (Hachette India, 2014).28.Reddy, C. S., Jha, C. S., Diwakar, P. G. & Dadhwal, V. K. Nationwide classification of forest types of India using remote sensing and GIS. Environ. Monit. Assess. 187, 777. https://doi.org/10.1007/s10661-015-4990-8 (2015).CAS 
    Article 
    PubMed 

    Google Scholar 
    29.Wegge, P., Shrestha, A. K. & Moe, S. R. Dry season diets of sympatric ungulates in lowland Nepal: Competition and facilitation in alluvial tall grasslands. Ecol. Res. 21, 698–706. https://doi.org/10.1007/s11284-006-0177-7 (2006).Article 

    Google Scholar 
    30.WWF. Living Planet: Report 2016. Risk and Resilience in a New Era. (World Wide Fund for Nature International, 2016).31.Gebremedhin, B. et al. DNA metabarcoding reveals diet overlap between the endangered walia ibex and domestic goats: Implications for conservation. PLoS One 11, e0159133. https://doi.org/10.1371/journal.pone.0159133 (2016).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    32.Spooner, F. E., Pearson, R. G. & Freeman, R. Rapid warming is associated with population decline among terrestrial birds and mammals globally. Glob. Change Biol. 24, 4521–4531. https://doi.org/10.1111/gcb.14361 (2018).ADS 
    Article 

    Google Scholar 
    33.Texeira, M., Baldi, G. & Paruelo, J. An exploration of direct and indirect drivers of herbivore reproductive performance in arid and semi-arid rangelands by means of structural equation models. J. Arid Environ. 81, 26–34. https://doi.org/10.1016/j.jaridenv.2012.01.017 (2012).ADS 
    Article 

    Google Scholar 
    34.Kupika, O. L., Gandiwa, E., Kativu, S. & Nhamo, G. Impacts of climate change and climate variability on wildlife resources in southern Africa: Experience from selected protected areas in Zimbabwe. In Selected Studies in Biodiversity, (eds. Şen, B. & Grillo, O.) 1–23 (IntechOpen, 2018). https://doi.org/10.5772/intechopen.70470.35.Joyce, C. B., Simpson, M. & Casanova, M. Future wet grasslands: Ecological implications of climate change. Ecosyst. Health Sustain. 2, e01240. https://doi.org/10.1002/ehs2.1240 (2016).Article 

    Google Scholar 
    36.Vasu, N. K., & Singh, G. Grasslands of Kaziranga National Park: Problems and approaches for management. In Ecology and Management of Grassland Habitats in India, vol. 17 (eds. Rawat, G. S., Adhikari, B. S.) 104–113 (Wildlife Institute of India, 2015).37.Dublin, H. T. Vegetation dynamics in the Serengeti-Mara ecosystem: The role of elephants, fire, and other factors. In Serengeti II: Dynamics, Management, and Conservation of an Ecosystem, (eds. Sinclair, A. R. E. & Arcese, P.) 71–90 (University of Chicago Press, 1995).38.Sinclair, A. R. E. Equilibria in plant–herbivore interactions. In Serengeti II: Dynamics, Management, and Conservation of an Ecosystem, (eds. Sinclair, A. R. E. & Arcese, P.) 91–113 (University of Chicago Press, 1995).39.Augustine, D. J. & McNaughton, S. J. Ungulate effects on the functional species composition of plant communities: Herbivore selectivity and plant tolerance. J. Wildl. Manag. 62, 1165. https://doi.org/10.2307/3801981 (1998).Article 

    Google Scholar 
    40.Schmitt, M. H. & Shrader, A. M. Browser population-woody vegetation relationships in Savannas. In Savanna Woody Plants and Large Herbivores (eds. Scogings, F. P. & Sankaran, M.) 245–278 (Wiley, 2020). https://doi.org/10.1002/9781119081111.ch9.41.Konwar, P., Saikia, M. K. & Saikia, P. K. Abundance of food plant species and food habits of Rhinoceros unicornis Linn. in Pobitora Wildlife Sanctuary, Assam, India. J. Threat. Taxa. 1, 457–460. https://doi.org/10.11609/jott.o1640.457-60 (2009).Article 

    Google Scholar 
    42.Bhatta, R. Ecology and Conservation of Great Indian One-horned Rhino (Rhinoceros unicornis) in Pobitora Wildlife Sanctuary, Assam, India (Gauhati University, 2011).
    Google Scholar 
    43.Hazarika, B. C. & Saikia, P. K. Food habit and feeding patterns of great indian one-horned rhinoceros (Rhinoceros unicornis) in Rajiv Gandhi Orang National Park, Assam, India. ISRN Zool. 2012, 1–11. https://doi.org/10.5402/2012/259695 (2012).Article 

    Google Scholar 
    44.Dutta, D. K., Bora, P. J., Mahanta, R., Sharma, A. & Swargowari, A. Seasonal variations in food plant preferences of reintroduced Rhinos Rhinoceros unicornis (Mammalia: Perrissodactyla: Rhinocerotidae) in Manas National Park, Assam, India. J. Threat. Taxa. 8, 9525–9536. https://doi.org/10.11609/jott.2486.8.13.9525-9536 (2016).Article 

    Google Scholar 
    45.Brahmachary, R. L., Rakshit, B. & Mallik, B. Further attempts to determine the food habits of the Indian Rhinoceros at Kaziranga. J. Bombay Nat. Hist. Soc. 71, 295–299 (1974).
    Google Scholar 
    46.Banerjee, G. Habitat Use by the Great Indian Rhinoceros (Rhinoceros Unicornis) and Other Sympatric Large Herbivores in Kaziranga National Park, Assam, India (Wildlife Institute of India, 2001).
    Google Scholar 
    47.Patar, K. C. Behavioural Patterns of the One Horned Indian Rhinoceros (Spectrum Publication Guwahati, 2005).
    Google Scholar 
    48.Bawri, M. & Saikia, P. K. Preliminary study on the food plant species of Endangered Asiatic wild water buffalo Bubalus arnee Kerr in Kaziranga National Park, Assam India. NeBIO. 5, 49–55 (2014).
    Google Scholar 
    49.Sukumar, R. Ecology of the Asian elephant in southern India. I. Movement and habitat utilization patterns. J. Trop. Ecol. 5, 1–18. https://doi.org/10.1017/S0266467400003175 (1989).Article 

    Google Scholar 
    50.Schaller, G. B. The Deer and the Tiger. A Study of Wildlife in India, (University of Chicago Press, 1967). https://doi.org/10.7208/chicago/9780226736570.001.0001.51.Dhungel, S. K. & O’Gara, B. W. Ecology of the Hog Deer in Royal Chitwan National Park, Nepal. Wildl. Monogr. 119, 3–40. https://doi.org/10.2307/3830632 (1991).Article 

    Google Scholar 
    52.Johnsingh, A. J. T. & Manjrekar, N. Mammals of South Asia, 2 (Universities Press, 2016).
    Google Scholar 
    53.Sukumar, R. Ecology of the Asian elephant in southern India. II. Feeding habits and crop raiding patterns. J. Trop. Ecol. 6, 33–53. https://doi.org/10.1017/S0266467400004004 (1990).Article 

    Google Scholar 
    54.Baskaran, N., Balasubramanian, M., Swaminathan, S. & Desai, A. A. Feeding ecology of the Asian elephant Elephas maximus Linnaeus in the Nilgiri Biosphere Reserve, southern India. J. Bombay Nat. Hist. Soc. 107, 3–13 (2010).
    Google Scholar 
    55.Tuboi, C. & Hussain, S. A. Factors affecting forage selection by the endangered Eld’s deer and hog deer in the floating meadows of Barak-Chindwin Basin of North-east India. Mamm. Biol. 81, 53–60. https://doi.org/10.1016/j.mambio.2014.10.006 (2016).Article 

    Google Scholar 
    56.Kelton, S. D. & Skipworth, J. P. Food of sambar deer (Cervus unicolor) in a Manawatu (New Zealand) flax swamp. N. Z. J. Ecol. 10, 149–152 (1987).
    Google Scholar 
    57.Semiadi, G., Barry, T. N., Muir, P. D. & Hodgson, J. Dietary preferences of sambar (Cervus unicolor) and red deer (Cervus elaphus) offered browse, forage legume and grass species. J. Agric. Sci. 125, 99–107. https://doi.org/10.1017/S0021859600074554 (1995).Article 

    Google Scholar 
    58.Johnsingh, A. J. T. & Sankar, K. Food plants of chital, sambar and cattle on Mundanthurai Plateau, Tamil Nadu, south India. Mammalia 55, 57–66. https://doi.org/10.1515/mamm.1991.55.1.57 (1991).Article 

    Google Scholar 
    59.Steinheim, G., Wegge, P., Fjellstad, J. I., Jnawali, S. R. & Weladji, R. B. Dry season diets and habitat use of sympatric Asian elephants (Elephas maximus) and greater one-horned rhinoceros (Rhinocerus unicornis) in Nepal. J. Zool. 265, 377–385. https://doi.org/10.1017/S0952836905006448 (2005).Article 

    Google Scholar 
    60.Bakker, E. S., Ritchie, M. E., Olff, H., Milchunas, D. G. & Knops, J. M. H. Herbivore impact on grassland plant diversity depends on habitat productivity and herbivore size. Ecol. Lett. 9, 780–788. https://doi.org/10.1111/j.1461-0248.2006.00925.x (2006).Article 
    PubMed 

    Google Scholar 
    61.Edwards, G. R. & Crawley, M. J. Herbivores, seed banks and seedling recruitment in mesic grassland. J. Ecol. 87, 423–435. https://doi.org/10.1046/j.1365-2745.1999.00363.x (1999).Article 

    Google Scholar 
    62.Marquis, R. J. The role of herbivores in terrestrial trophic cascades. In: Trophic Cascades: Predators, Prey and the Changing Dynamics of Nature, (eds. Terborgh, J. & Estes, J. A.) 109–123, (Island Press, 2010).63.Parikh, G. L. et al. The influence of plant defensive chemicals, diet composition, and winter severity on the nutritional condition of a free-ranging, generalist herbivore. Oikos 126, 1–8. https://doi.org/10.1111/oik.03359 (2017).Article 

    Google Scholar 
    64.Yadava, M. K. Kaziranga National Park: Detailed Report on Issues and Possible Solutions of Long-Term Protection of the Greater One-horned Rhinoceros in Kaziranga National Park Pursuant to the Order of the Hon’ble Guwahati High Court. 1–402 (Government of Assam, India, 2014).65.Champion, H. G. & Seth, S. K. A Revised Survey of the Forest Types of India (Govt. of India Press, 1968).
    Google Scholar 
    66.Sharma, G. Studies on the mammalian diversity of Kaziranga National Park, Assam, India with their conservation status. J. New Biol. Rep. 7, 15–19 (2018).CAS 

    Google Scholar 
    67.Shrestha, R., Wegge, P. & Koirala, R. A. Summer diets of wild and domestic ungulates in Nepal Himalaya. J. Zool. 266, 111–119. https://doi.org/10.1017/S0952836905006527 (2005).Article 

    Google Scholar 
    68.Sparks, D. R. & Malechek, J. C. Estimating percentage dry weight in diets using a microscopic technique. J. Range Manag. 21, 264–265. https://doi.org/10.2307/3895829 (1968).Article 

    Google Scholar 
    69.Satkopan, S. Key to identification of plant remains in animal dropping. J. Bombay Nat. Hist. Soc. 69, 139–150 (1972).
    Google Scholar 
    70.Johnson, M. K., Wofford, H. H. & Pearson, H. A. Microhistological Techniques for Food Habits Analyses (U.S. Department of Agriculture, 1983).Book 

    Google Scholar 
    71.Jain, S. K. & Hajra, P. K. On the botany of Manas Wild Life Sanctuary in Assam. Bull. Bot. Surv. Ind. 17, 75–86 (1975).
    Google Scholar 
    72.Hajra, P. K. & Jain, S. K. Botany of Kaziranga and Manas (Surya International Publications, 1994).
    Google Scholar 
    73.Rahmani, A. R., Kasambe, R., Prabhu, S., Khot, R. & Bajaru, S. Biodiversity Studies at Kaziranga National Park. (2016).74.Vila, A. R., Galende, G. I. & Pastore, H. Feeding ecology of the endangered huemul (Hippocamelus bisulcus) in Los Alerces National Park, Argentina. Mastozool. Neotrop. 16, 423–431 (2009).
    Google Scholar 
    75.Borah, S. B., Sivasankar, T., Ramya, M. N. S. & Raju, P. L. N. Flood inundation mapping and monitoring in Kaziranga National Park, Assam using Sentinel-1 SAR data. Environ. Monit. Assess. https://doi.org/10.1007/s10661-018-6893-y (2018).Article 
    PubMed 

    Google Scholar 
    76.De Barba, M. et al. Comparing opportunistic and systematic sampling methods for non-invasive genetic monitoring of a small translocated brown bear population. J. Appl. Ecol. 47, 172–181. https://doi.org/10.1111/j.1365-2664.2009.01752 (2010).Article 

    Google Scholar 
    77.Jachmann, H. & Bell, R. H. V. The use of elephant droppings in assessing numbers, occupance and age structure: A refinement of the method. Afr. J. Ecol. 22, 127–141. https://doi.org/10.1111/j.1365-2028.1984.tb00686.x (1984).Article 

    Google Scholar 
    78.Chaturvedi, R. K. & Sankar, K. Laboratory Manual for the Physico-Chemical Analysis of Soil, Water and Plant (Wildlife Institute of India, 2006).
    Google Scholar 
    79.Colwell, R. K. & Elsensohn, J. E. EstimateS turns 20: Statistical estimation of species richness and shared species from samples, with non-parametric extrapolation. Ecography 37, 609–613. https://doi.org/10.1111/ecog.00814 (2014).Article 

    Google Scholar 
    80.Colwell, R. K. et al. Models and estimators linking individual-based and sample-based rarefaction, extrapolation and comparison of assemblages. J. Plant Ecol. 5, 3–21. https://doi.org/10.1093/jpe/rtr044 (2012).Article 

    Google Scholar 
    81.Dormann, C. F., Gruber, B. & Fründ, J. Introducing the bipartite package: Analysing ecological networks. R News 8, 8–11 (2008).
    Google Scholar 
    82.Barton, K. & Barton, M. K. Package ‘MuMIn’. R package version, 1 (2019).83.Harrell Jr, F. E. & Harrell Jr, M. F. E. Package ‘Hmisc’. CRAN2018, 2019, 235–236 (2019).84.Wei, T. et al. Package ‘corrplot’: Visualization of a correlation matrix. Statistician 56, 316–324 (2017).
    Google Scholar  More