More stories

  • in

    A spatial analysis of seagrass habitat and community diversity in the Great Barrier Reef World Heritage Area

    1.Halpern, B. S. et al. A global map of human impact on marine ecosystems. Science 319, 948–952. https://doi.org/10.1126/science.1149345 (2008).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    2.Wilson, K. A. et al. Conserving biodiversity efficiently: What to do, where, and when. PLoS Biol. 5, 1850–1861. https://doi.org/10.1371/journal.pbio.0050223 (2007).CAS 
    Article 

    Google Scholar 
    3.Carr, M. H. et al. Comparing marine and terrestrial ecosystems: Implications for the design of coastal marine reserves. Ecol. Appl. 13, 90–107. https://doi.org/10.1890/1051-0761(2003)013[0090:CMATEI]2.0.CO;2 (2003).Article 

    Google Scholar 
    4.Coles, R. G. et al. The Great Barrier Reef World Heritage Area seagrasses: Managing this iconic Australian ecosystem resource for the future. Estuar. Coast. Shelf Sci. 153, A1–A12. https://doi.org/10.1016/j.ecss.2014.07.020 (2015).ADS 
    Article 

    Google Scholar 
    5.Beger, M. et al. Incorporating asymmetric connectivity into spatial decision making for conservation. Conserv. Lett. 3, 359–368. https://doi.org/10.1111/j.1755-263X.2010.00123.x (2010).Article 

    Google Scholar 
    6.Brodie, J. & Waterhouse, J. A critical review of environmental management of the ‘not so Great’ Barrier Reef. Estuar. Coast. Shelf Sci. 104, 1–22. https://doi.org/10.1016/j.ecss.2012.03.012 (2012).ADS 
    Article 

    Google Scholar 
    7.Collier, C. J. et al. An evidence-based approach for setting desired state in a complex Great Barrier Reef seagrass ecosystem: A case study from Cleveland Bay. Environ. Sustain. Indicators 7, 100042. https://doi.org/10.1016/j.ecolind.2012.04.005 (2020).Article 

    Google Scholar 
    8.Commonwealth of Australia. Reef 2050 Long-Term Sustainability Plan. http://www.environment.gov.au/system/files/resources/d98b3e53-146b-4b9c-a84a-2a22454b9a83/files/reef-2050-long-term-sustainability-plan.pdf (2015). (Accessed 09 June 2021).9.Commonwealth of Australia. Reef 2050 Long-Term Sustainability Plan—July 2018. https://www.environment.gov.au/system/files/resources/35e55187-b76e-4aaf-a2fa-376a65c89810/files/reef-2050-long-term-sustainability-plan-2018.pdf (2018). (Accessed 09 June 2021).10.Tulloch, V. J. et al. Linking threat maps with management to guide conservation investment. Biol. Cons. 245, 108527. https://doi.org/10.1016/j.biocon.2020.108527 (2020).Article 

    Google Scholar 
    11.Greene, H. G., Bizzarro, J. J., O’Connell, V. M. & Brylinsky, C. K. Construction of digital potential marine benthic habitat maps using a coded classification scheme and its application. Mapp. Seafloor Habitat Characterization Geol. Assoc. Canada Special Paper 47, 145–159 (2007).
    Google Scholar 
    12.Grech, A. et al. Spatial patterns of seagrass dispersal and settlement. Divers. Distrib. 22, 1150–1162. https://doi.org/10.1111/ddi.12479 (2016).Article 

    Google Scholar 
    13.Young, M. & Carr, M. Assessment of habitat representation across a network of marine protected areas with implications for the spatial design of monitoring. PLoS ONE 10, e0116200. https://doi.org/10.1371/journal.pone.0116200 (2015).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    14.Foley, M. M. et al. Guiding ecological principles for marine spatial planning. Mar. Policy 34, 955–966. https://doi.org/10.1016/j.marpol.2010.02.001 (2010).Article 

    Google Scholar 
    15.Diggon, S. et al. The marine plan partnership: Indigenous community-based marine spatial planning. Mar. Policy. https://doi.org/10.1016/j.marpol.2019.04.014 (2019).Article 

    Google Scholar 
    16.Kenchington, R. & Day, J. Zoning, a fundamental cornerstone of effective Marine Spatial Planning: Lessons learnt from the Great Barrier Reef, Australia. J. Coast. Conserv. 15, 271–278. https://doi.org/10.1007/s11852-011-0147-2 (2011).Article 

    Google Scholar 
    17.Noble, M. M., Harasti, D., Pittock, J. & Doran, B. Understanding the spatial diversity of social uses, dynamics, and conflicts in marine spatial planning. J. Environ. Manage. 246, 929–940. https://doi.org/10.1016/j.jenvman.2019.06.048 (2019).Article 
    PubMed 

    Google Scholar 
    18.Jayathilake, D. R. M. & Costello, M. J. A modelled global distribution of the seagrass biome. Biol. Cons. 226, 120–126. https://doi.org/10.1016/j.biocon.2018.07.009 (2018).Article 

    Google Scholar 
    19.den Hartog, C. & Kuo, J. Seagrasses: Biology, Ecology and Conservation Ch. 1 1–23 (Springer Netherlands, 2006).
    Google Scholar 
    20.Green, E. P. & Short, F. T. World Atlas of Seagrasses (University of California Press, 2003).
    Google Scholar 
    21.Short, F. T. et al. Extinction risk assessment of the world’s seagrass species. Biol. Cons. 144, 1961–1971. https://doi.org/10.1016/j.biocon.2011.04.010 (2011).Article 

    Google Scholar 
    22.Coles, R., McKenzie, L., De’ath, G., Roelofs, A. & Long, W. L. Spatial distribution of deepwater seagrass in the inter-reef lagoon of the Great Barrier Reef World Heritage Area. Mar. Ecol. Prog. Ser. 392, 57–68. https://doi.org/10.3354/meps08197 (2009).ADS 
    Article 

    Google Scholar 
    23.McKenzie, L. J. et al. The global distribution of seagrass meadows. Environ. Res. Lett. 15, 074041. https://doi.org/10.1088/1748-9326/ab7d06 (2020).ADS 
    Article 

    Google Scholar 
    24.Hemminga, M. A. & Duarte, C. M. Seagrass Ecology (Cambridge University Press, 2000).Book 

    Google Scholar 
    25.Lamb, J. B. et al. Seagrass ecosystems reduce exposure to bacterial pathogens of humans, fishes, and invertebrates. Science 355, 731–733. https://doi.org/10.1126/science.aal1956 (2017).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    26.Coles, R. G., Lee Long, W. J., Watson, R. A. & Derbyshire, K. J. Distribution of seagrasses, and their fish and penaeid prawn communities, in Cairns Harbour, a tropical estuary, Northern Queensland, Australia. Mar. Freshw. Res. 44, 193–210. https://doi.org/10.1071/MF9930193 (1993).Article 

    Google Scholar 
    27.de los Santos, C. B. et al. Seagrass ecosystem services: Assessment and scale of benefits. Out Blue Value Seagrasses Environ. People. 19–21 (2020).
    28.Marsh, H., O’Shea, T. J. & Reynolds, J. E. III. Ecology and Conservation of the Sirenia: Dugongs and Manatees Vol. 18 (Cambridge University Press, 2011).Book 

    Google Scholar 
    29.Scott, A. L. et al. The role of herbivory in structuring tropical seagrass ecosystem service delivery. Front. Plant Sci. 9, 1–10. https://doi.org/10.3389/fpls.2018.00127 (2018).Article 

    Google Scholar 
    30.Fourqurean, J. W. et al. Seagrass ecosystems as a globally significant carbon stock. Nat. Geosci. 5, 505–509. https://doi.org/10.1038/ngeo1477 (2012).ADS 
    CAS 
    Article 

    Google Scholar 
    31.Carter, A., Taylor, H. & Rasheed, M. Torres Strait Mapping: Seagrass Consolidation, 2002–2014 Vol. 47 (James Cook University, 2014).
    Google Scholar 
    32.Lee Long, W. J., Mellors, J. E. & Coles, R. G. Seagrasses between Cape York and Hervey Bay, Queensland, Australia. Austr. J. Mar. Freshw. Res. 44, 19–32. https://doi.org/10.1071/MF9930019 (1993).Article 

    Google Scholar 
    33.Maxwell, P. et al. Seagrasses of Moreton Bay Quandamooka: Diversity, ecology and resilience. in Moreton Bay Quandamooka & Catchment: Past, Present, and Future (eds I. R. Tibbetts et al.) 279–298 (Moreton Bay Foundation Ltd, 2019).
    34.Lambert, V. M. et al. Connecting targets for catchment sediment loads to ecological outcomes for seagrass using multiple lines of evidence. Mar. Pollut. Bull. https://doi.org/10.1016/j.marpolbul.2021.112494 (2021).Article 
    PubMed 

    Google Scholar 
    35.McKenna, S. A. et al. Declines of seagrasses in a tropical harbour, North Queensland, Australia, are not the result of a single event. J. Biosci. 40, 389–398. https://doi.org/10.1007/s12038-015-9516-6 (2015).Article 
    PubMed 

    Google Scholar 
    36.Collier, C. J., Waycott, M. & McKenzie, L. J. Light thresholds derived from seagrass loss in the coastal zone of the northern Great Barrier Reef, Australia. Ecol. Indicators 23, 211–219. https://doi.org/10.1016/j.ecolind.2012.04.005 (2012).Article 

    Google Scholar 
    37.York, P. et al. Dynamics of a deep-water seagrass population on the Great Barrier Reef: Annual occurrence and response to a major dredging program. Sci. Rep. 5, 13167. https://doi.org/10.1038/srep13167 (2015).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    38.Grech, A., Coles, R. & Marsh, H. A broad-scale assessment of the risk to coastal seagrasses from cumulative threats. Mar. Policy 35, 560–567. https://doi.org/10.1016/j.marpol.2011.03.003 (2011).Article 

    Google Scholar 
    39.Brodie, J. & Pearson, R. G. Ecosystem health of the Great Barrier Reef: Time for effective management action based on evidence. Estuar. Coast. Shelf Sci. 183, 438–451. https://doi.org/10.1016/j.ecss.2016.05.008 (2016).ADS 
    Article 

    Google Scholar 
    40.York, P. H. et al. Identifying knowledge gaps in seagrass research and management: An Australian perspective. Mar. Environ. Res. 127, 163–172. https://doi.org/10.1016/j.marenvres.2016.06.006 (2017).CAS 
    Article 
    PubMed 

    Google Scholar 
    41.Carruthers, T. J. B. et al. Seagrass habitats of Northeast Australia: Models of key processes and controls. Bull. Mar. Sci. 71, 1153–1153 (2002).
    Google Scholar 
    42.Waycott, M., Longstaff, B. J. & Mellors, J. Seagrass population dynamics and water quality in the Great Barrier Reef region: A review and future research directions. Mar. Pollut. Bull. 51, 343–350. https://doi.org/10.1016/j.marpolbul.2005.01.017 (2005).CAS 
    Article 
    PubMed 

    Google Scholar 
    43.Grech, A. & Coles, R. G. An ecosystem-scale predictive model of coastal seagrass distribution. Aquat. Conserv.-Mar. Freshw. Ecosyst. 20, 437–444. https://doi.org/10.1002/aqc.1107 (2010).Article 

    Google Scholar 
    44.Carter, A. et al. Synthesizing 35 years of seagrass spatial data from the Great Barrier Reef World Heritage Area, Queensland, Australia. Limnol. Oceanogr. Lett. https://doi.org/10.1002/lol2.10193 (2021).Article 

    Google Scholar 
    45.Beaman, R. J. High-Resolution Depth Model for the Great Barrier Reef—30 m. Dataset. http://pid.geoscience.gov.au/dataset/115066 (2017). (Accessed 10 March 2020).46.Bishop-Taylor, R., Sagar, S., Lymburner, L. & Beaman, R. Between the tides: Modelling the elevation of Australia’s exposed intertidal zone at continental scale. Estuar. Coast. Shelf Sci. 223, 115–128. https://doi.org/10.1016/j.ecss.2019.03.006 (2019).ADS 
    Article 

    Google Scholar 
    47.Geoscience Australia. Intertidal Extents Model 25m. v. 2.0.0. Dataset. https://ecat.ga.gov.au/geonetwork/srv/eng/catalog.search?node=srv#/metadata/7d6f3432-5f93-45ee-8d6c-14b26740048a (2017). (Accessed 10 March 2021).48.Steven, A. D. et al. eReefs: An operational information system for managing the Great Barrier Reef. J. Operat. Oceanogr. 12, S12–S28. https://doi.org/10.1080/1755876X.2019.1650589 (2019).Article 

    Google Scholar 
    49.Baird, M. E. et al. CSIRO environmental modelling suite (EMS): Scientific description of the optical and biogeochemical models (vB3p0). Geosci. Model Dev. 13, 4503–4553. https://doi.org/10.5194/gmd-13-4503-2020 (2020).ADS 
    CAS 
    Article 

    Google Scholar 
    50.Baird, M. E. et al. Remote-sensing reflectance and true colour produced by a coupled hydrodynamic, optical, sediment, biogeochemical model of the Great Barrier Reef, Australia: Comparison with satellite data. Environ. Model. Softw. 78, 79–96. https://doi.org/10.1016/j.envsoft.2015.11.025 (2016).Article 

    Google Scholar 
    51.Margvelashvili, N. et al. Simulated fate of catchment-derived sediment on the Great Barrier Reef shelf. Mar. Pollut. Bull. 135, 954–962. https://doi.org/10.1016/j.marpolbul.2018.08.018 (2018).CAS 
    Article 
    PubMed 

    Google Scholar 
    52.Griffiths, L. L., Connolly, R. M. & Brown, C. J. Critical gaps in seagrass protection reveal the need to address multiple pressures and cumulative impacts. Ocean Coast. Manag. https://doi.org/10.1016/j.ocecoaman.2019.104946 (2020).Article 

    Google Scholar 
    53.Unsworth, R. K. F. et al. Global challenges for seagrass conservation. Ambio 48, 801–815. https://doi.org/10.1007/s13280-018-1115-y (2019).Article 
    PubMed 

    Google Scholar 
    54.Grech, A. et al. Predicting the cumulative effect of multiple disturbances on seagrass connectivity. Glob. Change Biol. 24, 3093–3104. https://doi.org/10.1111/gcb.14127 (2018).ADS 
    Article 

    Google Scholar 
    55.Fernandes, L. et al. A process to design a network of marine no-take areas: Lessons from the Great Barrier Reef. Ocean Coast. Manag. 52, 439–447. https://doi.org/10.1016/j.ocecoaman.2009.06.004 (2009).Article 

    Google Scholar 
    56.Bainbridge, Z. et al. Fine sediment and particulate organic matter: A review and case study on ridge-to-reef transport, transformations, fates, and impacts on marine ecosystems. Mar. Pollut. Bull. 135, 1205–1220. https://doi.org/10.1016/j.marpolbul.2018.08.002 (2018).CAS 
    Article 
    PubMed 

    Google Scholar 
    57.Tol, S. J. et al. Long distance biotic dispersal of tropical seagrass seeds by marine mega-herbivores. Sci. Rep. 7, 4458. https://doi.org/10.1038/s41598-017-04421-1 (2017).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    58.Rasheed, M. A., McKenna, S. A., Carter, A. B. & Coles, R. G. Contrasting recovery of shallow and deep water seagrass communities following climate associated losses in tropical north Queensland, Australia. Mar. Pollut. Bull. 83, 491–499. https://doi.org/10.1016/j.marpolbul.2014.02.013 (2014).CAS 
    Article 
    PubMed 

    Google Scholar 
    59.Collier, C. & Waycott, M. Temperature extremes reduce seagrass growth and induce mortality. Mar. Pollut. Bull. 83, 483–490. https://doi.org/10.1016/j.marpolbul.2014.03.050 (2014).CAS 
    Article 
    PubMed 

    Google Scholar 
    60.Adams, M. P. et al. Predicting seagrass decline due to cumulative stressors. Environ. Modell. Softw. https://doi.org/10.1016/j.envsoft.2020.104717 (2020).Article 

    Google Scholar 
    61.Taylor, H. A. & Rasheed, M. A. Impacts of a fuel oil spill on seagrass meadows in a subtropical port, Gladstone, Australia—The value of long-term marine habitat monitoring in high risk areas. Mar. Pollut. Bull. 63, 431–437. https://doi.org/10.1016/j.marpolbul.2011.04.039 (2011).CAS 
    Article 
    PubMed 

    Google Scholar 
    62.Fraser, M. W. et al. Effects of dredging on critical ecological processes for marine invertebrates, seagrasses and macroalgae, and the potential for management with environmental windows using Western Australia as a case study. Ecol. Ind. 78, 229–242. https://doi.org/10.1016/j.ecolind.2017.03.026 (2017).Article 

    Google Scholar 
    63.Wolanski, E. Physical Oceanographic Processes of the Great Barrier Reef (CRC Press, 1994).
    Google Scholar 
    64.Hopley, D., Smithers, S. G. & Parnell, K. E. The Geomorphology of the Great Barrier Reef: Development, Diversity, and Change (Cambridge University Press, 2007).Book 

    Google Scholar 
    65.Hopley, D. The Queensland coastline: attributes and issues. in Queensland: A Geographical Interpretation (ed J. H. Holmes) 73–94 (Booralong Publications, 1986).66.McKenzie, L. J. et al. Marine Monitoring Program: Annual report for inshore seagrass monitoring 2017–2018. http://hdl.handle.net/11017/3488 (Great Barrier Reef Marine Park Authority, 2019). (Accessed 23 December 2020).67.Van De Wetering, C., Reason, C., Rasheed, M., Wilkinson, J. & York, P. Port of Abbot Point Long-Term Seagrass Monitoring Program—2019 Vol. 53 (James Cook University, 2020).
    Google Scholar 
    68.Van De Wetering, C., Carter, A. & Rasheed, M. Seagrass Habitat of Mourilyan Harbour: Annual Monitoring Report—2019 Vol. 51 (James Cook University, 2020).
    Google Scholar 
    69.McKenna, S. et al. Port of Townsville Seagrass Monitoring Program: 2019 (James Cook University, 2020).
    Google Scholar 
    70.York, P. & Rasheed, M. Annual Seagrass Monitoring in the Mackay-Hay Point Region—2019 Vol. 51 (James Cook University, 2020).
    Google Scholar 
    71.Reason, C., McKenna, S. & Rasheed, M. Seagrass Habitat of Cairns Harbour and Trinity Inlet: Cairns Shipping Development Program and Annual Monitoring Report 2019 Vol. 54 (James Cook University, 2020).
    Google Scholar 
    72.Smith, T., Chartrand, K., Wells, J., Carter, A. & Rasheed, M. Seagrasses in Port Curtis and Rodds Bay 2019 Annual Long-Term Monitoring and Whole Port Survey Vol. 71 (Centre for Tropical Water & Aquatic Ecosystem Research (TropWATER) Publication 20/02, James Cook University, 2020).
    Google Scholar 
    73.Chartrand, K. M., Szabó, M., Sinutok, S., Rasheed, M. A. & Ralph, P. J. Living at the margins: The response of deep-water seagrasses to light and temperature renders them susceptible to acute impacts. Mar. Environ. Res. 136, 126–138. https://doi.org/10.1016/j.marenvres.2018.02.006 (2018).CAS 
    Article 
    PubMed 

    Google Scholar 
    74.Dyall, A. et al. Queensland Coastal Waterways Geomorphic Habitat Mapping, Version 2 (1:100 000 scale digital data). http://catalogue.aodn.org.au/geonetwork/srv/eng/metadata.show?uuid=a05f7892-c344-7506-e044-00144fdd4fa6 (2004). (Accessed 05 October 2020).75.Heap, A. D. & Harris, P. T. Geomorphology of the Australian margin and adjacent seafloor. Aust. J. Earth Sci. 55, 555–585. https://doi.org/10.1080/08120090801888669 (2008).ADS 
    Article 

    Google Scholar 
    76.Breiman, L. Random forests. Mach. Learn. 45, 5–32. https://doi.org/10.1023/A:1010933404324 (2001).Article 
    MATH 

    Google Scholar 
    77.Liaw, A. & Wiener, M. Classification and regression by randomForest. R News 2, 18–22 (2002).
    Google Scholar 
    78.R Foundation for Statistical Computing. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).
    Google Scholar 
    79.plotmo: Plot a Model’s Residuals, Response, and Partial Dependence Plots. R package version 3.5.7 (2020).80.caret: Classification and Regression Training. R package version 6.0-86 (2020).81.Zuur, A. F., Ieno, E. N. & Elphick, C. S. A protocol for data exploration to avoid common statistical problems. Methods Ecol. Evolut. 1, 3–14. https://doi.org/10.1111/j.2041-210X.2009.00001.x (2010).Article 

    Google Scholar 
    82.raster: Geographic Data Analysis and Modeling. R package version 3.3-13 (2020).83.Pebesma, E. Simple features for R: Standardized support for spatial vector data. R J. 10, 439–446 (2018).Article 

    Google Scholar 
    84.De’ath, G. Multivariate partitioning. The mvpart Package version 1.1-1. Archive form on CRAN, https://cran.r-project.org. (2004).85.De’ath, G. Multivariate regression trees: a new technique for modeling species–environment relationships. Ecology 83, 1105–1117 (2002).
    Google Scholar  More

  • in

    Link knowledge and action networks to tackle disasters

    CORRESPONDENCE
    16 November 2021

    Link knowledge and action networks to tackle disasters

    Jim Falk

    0
    ,

    Rita R. Colwell

    1
    ,

    Charles F. Kennel

    2
    &

    Cherry A. Murray

    3

    Jim Falk

    University of Melbourne, Melbourne, Australia.

    View author publications

    You can also search for this author in PubMed
     Google Scholar

    Rita R. Colwell

    University of Maryland, College Park, USA.

    View author publications

    You can also search for this author in PubMed
     Google Scholar

    Charles F. Kennel

    Scripps Institution of Oceanography, San Diego, USA.

    View author publications

    You can also search for this author in PubMed
     Google Scholar

    Cherry A. Murray

    University of Arizona, Tuscon, USA.

    View author publications

    You can also search for this author in PubMed
     Google Scholar

    Twitter

    Facebook

    Email

    Earth’s climate, ecological and human systems could converge into a comprehensive crisis within our children’s lifetimes, driven by factors such as inequality, inadequate health infrastructure and food insecurity (see consensus statement, J. Falk et al. Sustain. Sci. https://doi.org/g5bd; 2021). As the COVID-19 pandemic has revealed, national military and economic security provide inadequate protection against global catastrophes.

    Access options

    Access through your institution

    Change institution

    Buy or subscribe

    /* style specs start */
    style{display:none!important}.LiveAreaSection-193358632 *{align-content:stretch;align-items:stretch;align-self:auto;animation-delay:0s;animation-direction:normal;animation-duration:0s;animation-fill-mode:none;animation-iteration-count:1;animation-name:none;animation-play-state:running;animation-timing-function:ease;azimuth:center;backface-visibility:visible;background-attachment:scroll;background-blend-mode:normal;background-clip:borderBox;background-color:transparent;background-image:none;background-origin:paddingBox;background-position:0 0;background-repeat:repeat;background-size:auto auto;block-size:auto;border-block-end-color:currentcolor;border-block-end-style:none;border-block-end-width:medium;border-block-start-color:currentcolor;border-block-start-style:none;border-block-start-width:medium;border-bottom-color:currentcolor;border-bottom-left-radius:0;border-bottom-right-radius:0;border-bottom-style:none;border-bottom-width:medium;border-collapse:separate;border-image-outset:0s;border-image-repeat:stretch;border-image-slice:100%;border-image-source:none;border-image-width:1;border-inline-end-color:currentcolor;border-inline-end-style:none;border-inline-end-width:medium;border-inline-start-color:currentcolor;border-inline-start-style:none;border-inline-start-width:medium;border-left-color:currentcolor;border-left-style:none;border-left-width:medium;border-right-color:currentcolor;border-right-style:none;border-right-width:medium;border-spacing:0;border-top-color:currentcolor;border-top-left-radius:0;border-top-right-radius:0;border-top-style:none;border-top-width:medium;bottom:auto;box-decoration-break:slice;box-shadow:none;box-sizing:border-box;break-after:auto;break-before:auto;break-inside:auto;caption-side:top;caret-color:auto;clear:none;clip:auto;clip-path:none;color:initial;column-count:auto;column-fill:balance;column-gap:normal;column-rule-color:currentcolor;column-rule-style:none;column-rule-width:medium;column-span:none;column-width:auto;content:normal;counter-increment:none;counter-reset:none;cursor:auto;display:inline;empty-cells:show;filter:none;flex-basis:auto;flex-direction:row;flex-grow:0;flex-shrink:1;flex-wrap:nowrap;float:none;font-family:initial;font-feature-settings:normal;font-kerning:auto;font-language-override:normal;font-size:medium;font-size-adjust:none;font-stretch:normal;font-style:normal;font-synthesis:weight style;font-variant:normal;font-variant-alternates:normal;font-variant-caps:normal;font-variant-east-asian:normal;font-variant-ligatures:normal;font-variant-numeric:normal;font-variant-position:normal;font-weight:400;grid-auto-columns:auto;grid-auto-flow:row;grid-auto-rows:auto;grid-column-end:auto;grid-column-gap:0;grid-column-start:auto;grid-row-end:auto;grid-row-gap:0;grid-row-start:auto;grid-template-areas:none;grid-template-columns:none;grid-template-rows:none;height:auto;hyphens:manual;image-orientation:0deg;image-rendering:auto;image-resolution:1dppx;ime-mode:auto;inline-size:auto;isolation:auto;justify-content:flexStart;left:auto;letter-spacing:normal;line-break:auto;line-height:normal;list-style-image:none;list-style-position:outside;list-style-type:disc;margin-block-end:0;margin-block-start:0;margin-bottom:0;margin-inline-end:0;margin-inline-start:0;margin-left:0;margin-right:0;margin-top:0;mask-clip:borderBox;mask-composite:add;mask-image:none;mask-mode:matchSource;mask-origin:borderBox;mask-position:0% 0%;mask-repeat:repeat;mask-size:auto;mask-type:luminance;max-height:none;max-width:none;min-block-size:0;min-height:0;min-inline-size:0;min-width:0;mix-blend-mode:normal;object-fit:fill;object-position:50% 50%;offset-block-end:auto;offset-block-start:auto;offset-inline-end:auto;offset-inline-start:auto;opacity:1;order:0;orphans:2;outline-color:initial;outline-offset:0;outline-style:none;outline-width:medium;overflow:visible;overflow-wrap:normal;overflow-x:visible;overflow-y:visible;padding-block-end:0;padding-block-start:0;padding-bottom:0;padding-inline-end:0;padding-inline-start:0;padding-left:0;padding-right:0;padding-top:0;page-break-after:auto;page-break-before:auto;page-break-inside:auto;perspective:none;perspective-origin:50% 50%;pointer-events:auto;position:static;quotes:initial;resize:none;right:auto;ruby-align:spaceAround;ruby-merge:separate;ruby-position:over;scroll-behavior:auto;scroll-snap-coordinate:none;scroll-snap-destination:0 0;scroll-snap-points-x:none;scroll-snap-points-y:none;scroll-snap-type:none;shape-image-threshold:0;shape-margin:0;shape-outside:none;tab-size:8;table-layout:auto;text-align:initial;text-align-last:auto;text-combine-upright:none;text-decoration-color:currentcolor;text-decoration-line:none;text-decoration-style:solid;text-emphasis-color:currentcolor;text-emphasis-position:over right;text-emphasis-style:none;text-indent:0;text-justify:auto;text-orientation:mixed;text-overflow:clip;text-rendering:auto;text-shadow:none;text-transform:none;text-underline-position:auto;top:auto;touch-action:auto;transform:none;transform-box:borderBox;transform-origin:50% 50% 0;transform-style:flat;transition-delay:0s;transition-duration:0s;transition-property:all;transition-timing-function:ease;vertical-align:baseline;visibility:visible;white-space:normal;widows:2;width:auto;will-change:auto;word-break:normal;word-spacing:normal;word-wrap:normal;writing-mode:horizontalTb;z-index:auto;-webkit-appearance:none;-moz-appearance:none;-ms-appearance:none;appearance:none;margin:0}.LiveAreaSection-193358632{width:100%}.LiveAreaSection-193358632 .login-option-buybox{display:block;width:100%;font-size:17px;line-height:30px;color:#222;padding-top:30px;font-family:Harding,Palatino,serif}.LiveAreaSection-193358632 .additional-access-options{display:block;font-weight:700;font-size:17px;line-height:30px;color:#222;font-family:Harding,Palatino,serif}.LiveAreaSection-193358632 .additional-login >li:not(:first-child)::before{transform:translateY(-50%);content:”;height:1rem;position:absolute;top:50%;left:0;border-left:2px solid #999}.LiveAreaSection-193358632 .additional-login >li:not(:first-child){padding-left:10px}.LiveAreaSection-193358632 .additional-login >li{display:inline-block;position:relative;vertical-align:middle;padding-right:10px}.BuyBoxSection-683559780{display:flex;flex-wrap:wrap;flex:1;flex-direction:row-reverse;margin:-30px -15px 0}.BuyBoxSection-683559780 .box-inner{width:100%;height:100%}.BuyBoxSection-683559780 .readcube-buybox{background-color:#f3f3f3;flex-shrink:1;flex-grow:1;flex-basis:255px;background-clip:content-box;padding:0 15px;margin-top:30px}.BuyBoxSection-683559780 .subscribe-buybox{background-color:#f3f3f3;flex-shrink:1;flex-grow:4;flex-basis:300px;background-clip:content-box;padding:0 15px;margin-top:30px}.BuyBoxSection-683559780 .title-readcube{display:block;margin:0;margin-right:20%;margin-left:20%;font-size:24px;line-height:32px;color:#222;padding-top:30px;text-align:center;font-family:Harding,Palatino,serif}.BuyBoxSection-683559780 .title-buybox{display:block;margin:0;margin-right:29%;margin-left:29%;font-size:24px;line-height:32px;color:#222;padding-top:30px;text-align:center;font-family:Harding,Palatino,serif}.BuyBoxSection-683559780 .title-asia-buybox{display:block;margin:0;margin-right:5%;margin-left:5%;font-size:24px;line-height:32px;color:#222;padding-top:30px;text-align:center;font-family:Harding,Palatino,serif}.BuyBoxSection-683559780 .asia-link{color:#069;cursor:pointer;text-decoration:none;font-size:1.05em;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:1.05em6}.BuyBoxSection-683559780 .access-readcube{display:block;margin:0;margin-right:10%;margin-left:10%;font-size:14px;color:#222;padding-top:10px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .access-asia-buybox{display:block;margin:0;margin-right:5%;margin-left:5%;font-size:14px;color:#222;padding-top:10px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .access-buybox{display:block;margin:0;margin-right:30%;margin-left:30%;font-size:14px;color:#222;opacity:.8px;padding-top:10px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .price-buybox{display:block;font-size:30px;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;padding-top:30px;text-align:center}.BuyBoxSection-683559780 .price-from{font-size:14px;padding-right:10px;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .issue-buybox{display:block;font-size:13px;text-align:center;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:19px}.BuyBoxSection-683559780 .no-price-buybox{display:block;font-size:13px;line-height:18px;text-align:center;padding-right:10%;padding-left:10%;padding-bottom:20px;padding-top:30px;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif}.BuyBoxSection-683559780 .vat-buybox{display:block;margin-top:5px;margin-right:20%;margin-left:20%;font-size:11px;color:#222;padding-top:10px;padding-bottom:15px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:17px}.BuyBoxSection-683559780 .button-container{display:block;padding-right:20px;padding-left:20px}.BuyBoxSection-683559780 .button-container >a:hover,.Button-505204839:hover,.Button-1078489254:hover{text-decoration:none}.BuyBoxSection-683559780 .readcube-button{background:#fff;margin-top:30px}.BuyBoxSection-683559780 .button-asia{background:#069;border:1px solid #069;border-radius:0;cursor:pointer;display:block;padding:9px;outline:0;text-align:center;text-decoration:none;min-width:80px;margin-top:75px}.BuyBoxSection-683559780 .button-label-asia,.ButtonLabel-3869432492,.ButtonLabel-3296148077{display:block;color:#fff;font-size:17px;line-height:20px;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;text-align:center;text-decoration:none;cursor:pointer}.Button-505204839,.Button-1078489254{background:#069;border:1px solid #069;border-radius:0;cursor:pointer;display:block;padding:9px;outline:0;text-align:center;text-decoration:none;min-width:80px;margin-top:10px}.Button-505204839 .readcube-label,.Button-1078489254 .readcube-label{color:#069}
    /* style specs end */Subscribe to JournalGet full journal access for 1 year$199.00only $3.90 per issueSubscribeAll prices are NET prices. VAT will be added later in the checkout.Tax calculation will be finalised during checkout.Rent or Buy articleGet time limited or full article access on ReadCube.from$8.99Rent or BuyAll prices are NET prices.

    Additional access options:

    Log in

    Learn about institutional subscriptions

    Nature 599, 372 (2021)
    doi: https://doi.org/10.1038/d41586-021-03419-0

    Competing Interests
    The authors declare no competing interests.

    Related Articles

    See more letters to the editor

    Subjects

    Climate change

    Biodiversity

    Society

    Latest on:

    Climate change

    COP26: Meet the scientists behind the crucial climate summit
    News Q&A 15 NOV 21

    ‘COP26 hasn’t solved the problem’: scientists react to UN climate deal
    News 14 NOV 21

    My international-student group helped me through a hurricane
    Career Column 12 NOV 21

    Biodiversity

    COP26 climate pledges: What scientists think so far
    News 05 NOV 21

    The answer to the biodiversity crisis is not more debt
    Editorial 26 OCT 21

    Illegal mining in the Amazon hits record high amid Indigenous protests
    News 30 SEP 21

    Society

    Presidents of Royal Society live long lives
    Correspondence 16 NOV 21

    Caltech confronted its racist past. Here’s what happened
    News Feature 10 NOV 21

    Scientists: don’t feed the doubt machine
    World View 02 NOV 21

    Jobs

    Open rank: Research Associate, Post-Doctoral Fellow or Research Scientist Positions

    OSU Department of Pediatrics (Nationwide Children’s Hospital)
    Columbus, OH, United States

    Doctoral Research Associates

    University of Münster (WWU)
    Münster, Germany

    Project Manager

    University of Oxford
    Oxford, United Kingdom

    Postdoctor

    University of Gothenburg (GU)
    Gothenburg, Sweden More

  • in

    Effect of different management techniques on bird taxonomic groups on rice fields in the Republic of Korea

    1.Elphick, C. S. Why study birds in rice fields?. Waterbirds 33(sp1), 1–7. https://doi.org/10.1675/063.033.s101 (2010).MathSciNet 
    Article 

    Google Scholar 
    2.Machado, I. F. & Maltchik, L. Can management practices in rice fields contribute to amphibian conservation in southern Brazilian wetlands?. Aquat. Conserv. Mar. Freshw. Ecosyst. 20(1), 39–46 (2010).
    Google Scholar 
    3.Benton, T. G., Vickery, J. A. & Wilson, J. D. Farmland biodiversity: Is habitat heterogeneity the key?. Trends Ecol. Evol. 18(4), 182–188. https://doi.org/10.1016/S0169-5347(03)00011-9 (2003).Article 

    Google Scholar 
    4.Shuford, W. D., Humphrey, J. M. & Nur, N. Breeding status of the Black tern in California. West. Birds 32, 189–217 (2001).
    Google Scholar 
    5.Sánchez-Guzmán, J. M. et al. Identifying new buffer areas for conserving waterbirds in the Mediterranean basin: The importance of the rice fields in Extremadura, Spain. Biodivers. Conserv. 16(12), 3333–3344. https://doi.org/10.1007/s10531-006-9018-9 (2007).Article 

    Google Scholar 
    6.Lane, S. J. & Fujioka, M. The impact of changes in irrigation practices on the distribution of foraging egrets and herons (Ardeidae) in the rice fields of central Japan. Biol. Conserv. 83(2), 221–230. https://doi.org/10.1016/S0006-3207(97)00054-2 (1998).Article 

    Google Scholar 
    7.Bambaradeniya, C. N. B. et al. Biodiversity associated with an irrigated rice agro-ecosystem in Sri Lanka. Biodivers. Conserv. 13(9), 1715–1753. https://doi.org/10.1023/B:BIOC.0000029331.92656.de (2004).Article 

    Google Scholar 
    8.Donald, P. F. Biodiversity impacts of some agricultural commodity production systems. Conserv. Biol. 18(1), 17–38. https://doi.org/10.1111/j.1523-1739.2004.01803.x (2004).Article 

    Google Scholar 
    9.Steffen, W. et al. Sustainability. Planetary boundaries: Guiding human development on a changing planet. Science 347(6223), 1259855. https://doi.org/10.1126/science.1259855 (2015).CAS 
    Article 
    PubMed 

    Google Scholar 
    10.Ramankutty, N. et al. Trends in global agricultural land use: Implications for environmental health and food security. Annu. Rev. Plant Biol. 69, 789–815. https://doi.org/10.1146/annurev-arplant-042817-040256,Pubmed:29489395 (2018).CAS 
    Article 
    PubMed 

    Google Scholar 
    11.Le Féon, V. et al. Intensification of agriculture, landscape composition and wild bee communities: A large scale study in four European countries. Agric. Ecosyst. Environ. 137(1–2), 143–150. https://doi.org/10.1016/j.agee.2010.01.015 (2010).Article 

    Google Scholar 
    12.Donal, P. F., Gree, R. E. & Heath, M. F. Agricultural intensification and the collapse of Europe’s farmland bird populations. Proc. Biol. Sci. 268(1462), 25–29. https://doi.org/10.1098/rspb.2000.1325,Pubmed:12123294 (2001).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    13.Gregory, R. D. et al. Developing indicators for European birds. Philos. Trans. R. Soc. Lond. B Biol. Sci. 360(1454), 269–288. https://doi.org/10.1098/rstb.2004.1602 (2005).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    14.Beketov, M. A., Kefford, B. J., Schäfer, R. B. & Liess, M. Pesticides reduce regional biodiversity of stream invertebrates. Proc. Natl. Acad. Sci. U S A 110(27), 11039–11043. https://doi.org/10.1073/pnas.1305618110 (2013).ADS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    15.Geiger, F. et al. Persistent negative effects of pesticides on biodiversity and biological control potential on European farmland. Basic Appl. Ecol. 11(2), 97–105. https://doi.org/10.1016/j.baae.2009.12.001 (2010).CAS 
    Article 

    Google Scholar 
    16.Van Dijk, T. C., Van Staalduinen, M. A. & Van der Sluijs, J. P. Macro-invertebrate decline in surface water polluted with Imidacloprid. PLoS ONE 8(5), e62374. https://doi.org/10.1371/journal.pone.0062374,Pubmed:23650513 (2013).ADS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    17.Katayama, N., Baba, Y. G., Kusumoto, Y. & Tanaka, K. A review of post-war changes in rice farming and biodiversity in Japan. Agric. Syst. 132, 73–84. https://doi.org/10.1016/j.agsy.2014.09.001 (2015).Article 

    Google Scholar 
    18.Maeda, T. Patterns of bird abundance and habitat use in rice fields of the Kanto Plain, central Japan. Ecol. Res. 16(3), 569–585. https://doi.org/10.1046/j.1440-1703.2001.00418.x (2001).Article 

    Google Scholar 
    19.Nam, H. K., Choi, S. H., Choi, Y. S. & Yoo, J. C. Patterns of waterbirds abundance and habitat use in rice fields. Korean J. Environ. Agric. 31(4), 359–367. https://doi.org/10.5338/KJEA.2012.31.4.359 (2012).Article 

    Google Scholar 
    20.Choi, S. H., Nam, H. K. & Yoo, J. C. Characteristics of population dynamics and habitat use of shorebirds in rice fields during spring migration. Korean J. Environ. Agric. 33(4), 334–343. https://doi.org/10.5338/KJEA.2014.33.4.334 (2014).Article 

    Google Scholar 
    21.Elphick, C. S., Taft, O. & Lourenço, P. M. Management of rice fields for birds during the non-growing season. Waterbirds 33(sp1), 181–192. https://doi.org/10.1675/063.033.s114 (2010).Article 

    Google Scholar 
    22.Ibáñez, C., Curcó, A., Riera, X., Ripoll, I. & Sánchez, C. Influence on birds of rice field management practices during the growing season: A review and an experiment. Waterbirds 33(sp1), 167–180. https://doi.org/10.1675/063.033.s113 (2010).Article 

    Google Scholar 
    23.Sato, N. & Maruyama, N. Foraging site preference of intermediate egrets Egretta intermedia during the breeding season in the eastern part of the Kanto Plain, Japan. J. Yamashina Inst. Ornithol. 28(1), 19-34_1. https://doi.org/10.3312/jyio1952.28.19 (1996).Article 

    Google Scholar 
    24.Nam, H. K., Choi, Y. S., Choi, S. H. & Yoo, J. C. Distribution of waterbirds in rice fields and their use of foraging habitats. Waterbirds 38(2), 173–183. https://doi.org/10.1675/063.038.0206 (2015).Article 

    Google Scholar 
    25.Azuma, A. & Takeuchi, K. Relationships between population density of frogs and environmental conditions in Yatsu-habitat. J. Jpn. Inst. Landsc. Archit. 62(5), 573–576 (1999).Article 

    Google Scholar 
    26.Mullié, W. C. et al. The impact of Furadan 3g (carbofuran) applications on aquatic macroinvertebrates in irrigated rice in Senegal. Arch. Environ. Contam. Toxicol. 20(2), 177–182. https://doi.org/10.1007/BF01055902 (1991).Article 

    Google Scholar 
    27.Tourenq, C., Sadoul, N., Beck, N., Mesléard, F. & Martin, J. L. Effects of cropping practices on the use of rice fields by waterbirds in the Camargue, France. Agric. Ecosyst. Environ. 95(2–3), 543–549. https://doi.org/10.1016/S0167-8809(02)00203-7 (2003).Article 

    Google Scholar 
    28.Mesléard, F., Garnero, S., Beck, N. & Rosecchi, E. Uselessness and indirect negative effects of an insecticide on rice field invertebrates. C. R. Biol. 328(10–11), 955–962. https://doi.org/10.1016/j.crvi.2005.09.003,Pubmed:16286085 (2005).Article 
    PubMed 

    Google Scholar 
    29.Osten, J. R. V., Soares, A. M. & Guilhermino, L. Black-bellied whistling duck (Dendrocygna autumnalis) brain cholinesterase characterization and diagnosis of anticholinesterase pesticide exposure in wild populations from Mexico. Environ. Toxicol. Chem. 24(2), 313–317. https://doi.org/10.1897/03-646.1,Pubmed:15719990 (2005).Article 
    PubMed 

    Google Scholar 
    30.Katayama, N. et al. Organic farming and associated management practices benefit multiple wildlife taxa: A large-scale field study in rice paddy landscapes. J. Appl. Ecol. 56, 1970–1981. https://doi.org/10.1111/1365-2664.13446 (2019).Article 

    Google Scholar 
    31.Parsons, K. C., Mineau, P. & Renfrew, R. B. Effects of pesticide use in rice fields on birds. Waterbirds 33(sp1), 193–218. https://doi.org/10.1675/063.033.s115 (2010).Article 

    Google Scholar 
    32.Choi, G., Nam, H. K., Son, S. J., Seock, M. & Yoo, J. C. The impact of agricultural activities on habitat use by the Wood sandpiper and Common greenshank in rice fields. Ornithol. Sci. 20(1), 27–37 (2021).Article 

    Google Scholar 
    33.Choi, G., Nam, H. K., Son, S. J., Do, M. S. & Yoo, J. C. Effects of Pesticide Use on the Distributions of Grey Herons (Ardea cinerea) and Great Egrets (Ardea alba) in Rice Fields of the Republic of Korea. Zool. Sci. 38, 162–169. https://doi.org/10.2108/zs200079 (2021).Article 

    Google Scholar 
    34.Lourenço, P. M. & Piersma, T. Stopover ecology of Black-tailed Godwits Limosa limosa in Portuguese rice fields: A guide on where to feed in winter. Bird Study 55(2), 194–202. https://doi.org/10.1080/00063650809461522 (2008).Article 

    Google Scholar 
    35.Fujioka, M., Lee, S. D., Kurechi, M. & Yoshida, H. Bird use of rice fields in Korea and Japan. Waterbirds 33(sp1), 8–29. https://doi.org/10.1675/063.033.s102 (2010).Article 

    Google Scholar 
    36.Stafford, J. D., Kaminski, R. M. & Reinecke, K. J. Avian foods, foraging and habitat conservation in world rice fields. Waterbirds 33(sp1), 133–150. https://doi.org/10.1675/063.033.s110 (2010).Article 

    Google Scholar 
    37.Harwood, J. D., Sunderland, K. D. & Symondson, W. O. C. Living where the food is: web location by linyphiid spiders in relation to prey availability in winter wheat. J. Appl. Ecol. 38(1), 88–99. https://doi.org/10.1046/j.1365-2664.2001.00572.x (2001).Article 

    Google Scholar 
    38.Morris, A. J., Bradbury, R. B. & Wilson, J. D. Determinants of patch selection by yellowhammers Emberiza citrinella foraging in cereal crops. Aspects Appl. Biol. 67, 43–50 (2002).
    Google Scholar 
    39.Han, M. S. et al. Characteristics of benthic invertebrates in organic and conventional paddy field. Korean J. Environ. Agric. 32(1), 17–23. https://doi.org/10.5338/KJEA.2013.32.1.17 (2013).ADS 
    Article 

    Google Scholar 
    40.Dalzochio, M. S., Baldin, R., Stenert, C. & Maltchik, L. Can organic and conventional agricultural systems affect wetland macroinvertebrate taxa in rice fields?. Basic Appl. Ecol. 17(3), 220–229. https://doi.org/10.1016/j.baae.2015.10.009 (2016).Article 

    Google Scholar 
    41.Lourenço, P. M. & Piersma, T. Waterbird densities in south European rice fields as a function of rice management. Ibis 151(1), 196–199. https://doi.org/10.1111/j.1474-919X.2008.00881.x (2009).Article 

    Google Scholar 
    42.Dias, R. A., Blanco, D. E., Goijman, A. P. & Zaccagnini, M. E. Density, habitat use, and opportunities for conservation of shorebirds in rice fields in southeastern South America. Condor Ornithol. Appl. 116(3), 384–393. https://doi.org/10.1650/CONDOR-13-160.1 (2014).Article 

    Google Scholar 
    43.Kim, Y. H., Kang, S. M., Khan, A. L., Lee, J. H. & Lee, I. J. Aspect of weed occurrence by methods of weed control in rice field. Korean J. Weed Sci. 31(1), 89–95. https://doi.org/10.5660/KJWS.2011.31.1.089 (2011).Article 

    Google Scholar 
    44.Shin, H. S. et al. Monthly change of the length-weight relationship of the loach (Misgurnus anguillicaudatus) population in paddy fields by farming practices. Korean J. Environ. Biol. 36(1), 1–10. https://doi.org/10.11626/KJEB.2018.36.1.001 (2018).ADS 
    Article 

    Google Scholar 
    45.Elphick, C. S. & Oring, L. W. Winter management of Californian rice fields for waterbirds. J. Appl. Ecol. 35(1), 95–108. https://doi.org/10.1046/j.1365-2664.1998.00274.x (1998).Article 

    Google Scholar 
    46.Pernollet, C. A., Cavallo, F., Simpson, D., Gauthier-Clerc, M. & Guillemain, M. Seed density and waterfowl use of rice fields in Camargue, France. J. Wild. Manag. 81(1), 96–111. https://doi.org/10.1002/jwmg.21167 (2017).Article 

    Google Scholar 
    47.Firth, A. G. et al. Low external input sustainable agriculture: Winter flooding in rice fields increases bird use, fecal matter and soil health, reducing fertilizer requirements. Agric. Ecosyst. Environ. 300, 106962. https://doi.org/10.1016/j.agee.2020.106962 (2020).CAS 
    Article 

    Google Scholar 
    48.Manley, S. W., Kaminski, R. M., Reinecke, K. J. & Gerard, P. D. Waterbird foods in winter-managed ricefields in Mississippi. J. Wildl. Manag. 68(1), 74–83. https://doi.org/10.2193/0022-541X(2004)068[0074:WFIWRI]2.0.CO;2 (2004).Article 

    Google Scholar 
    49.Fraixedas, S., Burgas, D., Robson, D., Camps, J. & Barriocanal, C. Benefits of the European Agri-environment schemes for wintering lapwings: A case study from rice fields in the Mediterranean region. Waterbirds 43(1), 86–93. https://doi.org/10.1675/063.043.0109 (2020).Article 

    Google Scholar 
    50.Tourenq, C. et al. Spatial relationships between tree-nesting heron colonies and rice fields in the Camargue, France. Auk 121(1), 192–202. https://doi.org/10.1093/auk/121.1.192 (2004).Article 

    Google Scholar 
    51.Rural Research Institute. Management Effect of Environmentally-Friendly Agriculture Pilot Site: A Case Study on Project Office of Daeho Environment (Korea Rural Community Corporation, 2008).
    Google Scholar 
    52.Kohonen, T. Self-organized formation of topologically correct feature maps. Biol. Cybern. 43(1), 59–69. https://doi.org/10.1007/BF00337288 (1982).MathSciNet 
    Article 
    MATH 

    Google Scholar 
    53.Chon, T. S. Self-organizing maps applied to ecological sciences. Ecol. Inform. 6, 50–61. https://doi.org/10.1016/j.ecoinf.2010.11.002 (2011).Article 

    Google Scholar 
    54.Park, Y. S., Céréghino, R., Compin, A. & Lek, S. Applications of artificial neural networks for patterning and predicting aquatic insect species richness in running waters. Ecol. Modell. 160(3), 265–280. https://doi.org/10.1016/S0304-3800(02)00258-2 (2003).Article 

    Google Scholar 
    55.Akande, A., Costa, A. C., Mateu, J. & Henriques, R. Geospatial analysis of extreme weather events in Nigeria (1985–2015) using self-organizing maps. Adv. Meteorol. https://doi.org/10.1155/2017/8576150 (2017).Article 

    Google Scholar 
    56.Park, Y. S., Chung, Y. J. & Moon, Y. S. Hazard ratings of pine forests to a pine wilt disease at two spatial scales (individual trees and stands) using self-organizing map and random forest. Ecol. Model. 13, 40–46. https://doi.org/10.1016/j.ecoinf.2012.10.008 (2013).Article 

    Google Scholar 
    57.Chon, T. S., Park, Y. S., Moon, K. H. & Cha, E. Y. Patternizing communities by using an artificial neural network. Ecol. Model. 90, 69–78. https://doi.org/10.1016/0304-3800(95)00148-4 (1996).Article 

    Google Scholar 
    58.Vesanto, J., Himberg, J., Alhoniemi, E. & Parhankangas, J. SOM Toolbox for MATLAB 5. Technical Report a57. SOM Toolbox Team, Helsinki University of Technology, Finland, 1–60. (2000). http://www.cis.hut.fi/projects/somtoolbox.59.Moran, P. A. P. Notes on continuous stochastic phenomena. Biometrika 37(1–2), 17–23. https://doi.org/10.1093/biomet/37.1-2.17 (1950).MathSciNet 
    CAS 
    Article 
    PubMed 
    MATH 

    Google Scholar 
    60.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019).
    Google Scholar 
    61.Wehrens, R. & Kruisselbrink, J. Flexible self-organizing maps in Kohonen 3.0. J. Stat. Soft. 87(7), 1–18. https://doi.org/10.18637/jss.v087.i07 (2018).Article 

    Google Scholar 
    62.Oksanen, J. et al. Vegan: Community Ecology Package. R package version 2.4–4, https://CRAN.R-project.org/package=vegan (2017).63.Bates, D., Maechler, M. & Bolker, B. lme4: Linear Mixed-Effects Models Using S4 Classes. R package version 0.999375-42, http://cran.r-project.org/package=lme4 (2014).64.Rousset, F. & Ferdy, J.-B. Testing environmental and genetic effects in the presence of spatial autocorrelation. Ecography 37, 781–790. https://doi.org/10.1111/ecog.00566 (2014).Article 

    Google Scholar  More

  • in

    X-ray computed tomography (CT) and ESEM-EDS investigations of unusual subfossilized juniper cones

    1.Mohamed, W. & El-Rifai, E. An integrated approach for the documentation and virtual reconstruction of metal fragments. In Seventh World Archaeological Congress-WAC 7, Dead Sea, Jordan (2013).2.Birks, H. H. Plant macrofossil introduction. Encycl. Quat. Sci. 3, 2266–2288 (2007).
    Google Scholar 
    3.van der Veen, M. In The Science of Roman History (ed. Scheidel, W.) 53–94 (Princeton University Press, 2018).
    Google Scholar 
    4.Stanley, J.-D. Submergence and burial of ancient coastal sites on the subsiding Nile delta margin, Egypt. Méditer. Rev. Géogr. Pays Méditer./J. Mediter. Geogr. 104, 65–73 (2005).
    Google Scholar 
    5.Zhao, X. et al. Holocene climate change and its influence on early agriculture in the Nile Delta, Egypt. Palaeogeogr. Palaeoclimatol. Palaeoecol. 547, 109702. https://doi.org/10.1016/j.palaeo.2020.109702 (2020).Article 

    Google Scholar 
    6.Sestini, G. Nile Delta: A review of depositional environments and geological history. Geol. Soc. Lond. Spec. Publ. 41, 99–127 (1989).ADS 

    Google Scholar 
    7.Stanley, D. J. & Warne, A. G. Nile Delta: Recent geological evolution and human impact. Science 260, 628–634 (1993).ADS 
    CAS 
    PubMed 

    Google Scholar 
    8.Pennington, B. T., Sturt, F., Wilson, P., Rowland, J. & Brown, A. G. The fluvial evolution of the Holocene Nile Delta. Quatern. Sci. Rev. 170, 212–231. https://doi.org/10.1016/j.quascirev.2017.06.017 (2017).ADS 
    Article 

    Google Scholar 
    9.Björdal, C., Nilsson, T. & Daniel, G. Microbial decay of waterlogged archaeological wood found in Sweden applicable to archaeology and conservation. Int. Biodeterior. Biodegrad. 43, 63–73. https://doi.org/10.1016/S0964-8305(98)00070-5 (1999).Article 

    Google Scholar 
    10.Douterelo, I., Goulder, R. & Lillie, M. Soil microbial community response to land-management and depth, related to the degradation of organic matter in English wetlands: Implications for the in situ preservation of archaeological remains. Appl. Soil. Ecol. 44, 219–227. https://doi.org/10.1016/j.apsoil.2009.12.009 (2010).Article 

    Google Scholar 
    11.Weiss, E. & Kislev, M. E. Plant remains as a tool for reconstruction of the past environment, economy, and society: Archaeobotany in Israel. Israel J. Earth Sci. 56, 163–173 (2007).
    Google Scholar 
    12.Birks, H. J. B. Challenges in the presentation and analysis of plant-macrofossil stratigraphical data. Veg. Hist. Archaeobotany 23, 309–330 (2014).
    Google Scholar 
    13.Mauquoy, D., Hughes, P. & Van Geel, B. A protocol for plant macrofossil analysis of peat deposits. Mires Peat 7, 1–5 (2010).
    Google Scholar 
    14.Jacomet, S., Kreuz, A. & Rösch, M. Archäobotanik: Aufgaben Methoden, und Ergebnisse vegetations-und agrargeschichtlicher Forschung (Ulmer, 1999).
    Google Scholar 
    15.Jacomet, S. Plant macrofossil methods and studies: Use in environmental archaeology. In Encyclopedia of quaternary science 2384–2412 (Elsevier, Amsterdam, 2007).
    Google Scholar 
    16.Takahashi, M., Crane, P. R. & Ando, H. Fossil flowers and associated plant fossils from the Kamikitaba locality (Ashizawa Formation, Futaba Group, lower Coniacian, upper Cretaceous) of Northeast Japan. J. Plant. Res. 112, 187–206. https://doi.org/10.1007/PL00013872 (1999).Article 

    Google Scholar 
    17.Poppinga, S. et al. Hygroscopic motions of fossil conifer cones. Sci. Rep. 7, 40302. https://doi.org/10.1038/srep40302 (2017).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    18.Crepet, W. L., Nixon, K. C., Grimaldi, D. & Riccio, M. A mosaic Lauralean flower from the Early Cretaceous of Myanmar. Am. J. Bot. 103, 290–297. https://doi.org/10.3732/ajb.1500393 (2016).Article 
    PubMed 

    Google Scholar 
    19.Feng, Z., Röβler, R., Annacker, V. & Yang, J.-Y. Micro-CT investigation of a seed fern (probable medullosan) fertile pinna from the Early Permian Petrified Forest in Chemnitz, Germany. Gondwana Res. 26, 1208–1215. https://doi.org/10.1016/j.gr.2013.08.005 (2014).ADS 
    Article 

    Google Scholar 
    20.Gee, C. T., Dayvault, R. D., Stockey, R. A. & Tidwell, W. D. Greater palaeobiodiversity in conifer seed cones in the Upper Jurassic Morrison Formation of Utah, USA. Palaeobiodivers. Palaeoenviron. 94, 363–375. https://doi.org/10.1007/s12549-014-0160-1 (2014).Article 

    Google Scholar 
    21.Herrera, F. et al. A new voltzian seed cone from the Early Cretaceous of Mongolia and its implications for the evolution of ancient conifers. Int. J. Plant Sci. 176, 791–809. https://doi.org/10.1086/683060 (2015).Article 

    Google Scholar 
    22.Rozefelds, A. et al. Traditional and computed tomographic (CT) techniques link modern and Cenozoic fruits of Pleiogynium (Anacardiaceae) from Australia. Alcheringa 39, 24–39. https://doi.org/10.1080/03115518.2014.951916 (2015).Article 

    Google Scholar 
    23.Su, T., Wilf, P., Huang, Y., Zhang, S. & Zhou, Z. Peaches Preceded Humans: Fossil Evidence from SW China. Sci. Rep. 5, 16794. https://doi.org/10.1038/srep16794 (2015).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    24.Nishida, H. The frontier of fossil plant studies. Gakujutu Geppou 54, 1142–1144 (2001).
    Google Scholar 
    25.Collinson, M. E. et al. X-ray micro-computed tomography (micro-CT) of pyrite-permineralized fruits and seeds from the London Clay Formation (Ypresian) conserved in silicone oil: A critical evaluation. Botany 94, 697–711. https://doi.org/10.1139/cjb-2016-0078 (2016).CAS 
    Article 

    Google Scholar 
    26.Dilcher, D. L. & Manchester, S. R. Investigations of angiosperms from the Eocene of North America: A fruit belonging to the Euphorbiaceae. Tertiary Res. 9, 45–58 (1987).
    Google Scholar 
    27.Koch, B. E. & Friedrich, W. L. StereoskopischeRntgen-aufnahmen von fossilenFrüchten. Bull. Geol. Soc. Denmark. 21, 358–367 (1972).
    Google Scholar 
    28.Debussche, M. & Isenmann, P. Fleshy fruit characters and the choices of bird and mammal seed dispersers in a Mediterranean region. Oikos 56, 327–338 (1989).
    Google Scholar 
    29.Esteves, C. F., Costa, J. M., Vargas, P., Freitas, H. & Heleno, R. H. On the limited potential of Azorean fleshy fruits for oceanic dispersal. PLoS ONE 10, e0138882. https://doi.org/10.1371/journal.pone.0138882 (2015).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    30.Manniche, L. Sacred Luxuries: Fragrance, Aromatherapy, and Cosmetics in Ancient Egypt (Cornell University Press, 1999).
    Google Scholar 
    31.Kendall, P. Trees for life Discover the forest, Mythology & folklore, Juniper (Iris Publisher, 2005).
    Google Scholar 
    32.Waltz, L. R. The Herbal Encyclopedia: A Practical Guide to the Many Uses of Herbs (iUniverse, 2004).
    Google Scholar 
    33.Tunon, H., Olavsdotter, C. & Bohlin, L. Evaluation of anti-inflammatory activity of some Swedish medicinal plants. Inhibition of prostaglandin biosynthesis and PAF-induced exocytosis. J. Ethnopharmacol. 48, 61–76 (1995).CAS 
    PubMed 

    Google Scholar 
    34.Modnicki, D. & Łabędzka, J. Estimation of the total phenolic compounds in juniper sprouts (Juniperus communis, Cupressaceae) from different places at the kujawsko-pomorskie province. Herba Pol. 55, 127–132 (2009).CAS 

    Google Scholar 
    35.Longe, J. L. The Gale Encyclopedia of Alternative Medicine Vol. 3 (Thomson Gale ((Thomson Gale, A Part of The Thomson Corporation), London, 2005).
    Google Scholar 
    36.Wurges, J. Juniper. In The Gale Encyclopedia of Alternative Medicine (ed. Longe, J. L.) (Thomson/Gale, 2005).
    Google Scholar 
    37.Larson, E. Dangerous Tastes: The Story of Spices. Northeast. Nat. 9, 124 (2002).
    Google Scholar 
    38.Dalby, A. Dangerous Tastes: The Story of Spices (University of California Press, 2000).
    Google Scholar 
    39.Lorman, J. Greek Life 76–77 (Gregory House, 1997).
    Google Scholar 
    40.El-Bana, M., Shaltout, K., Khalafallah, A. & Mosallam, H. Ecological status of the Mediterranean Juniperus phoenicea L. relicts in the desert mountains of North Sinai, Egypt. Flora 205, 171–178. https://doi.org/10.1016/j.flora.2009.04.004 (2010).Article 

    Google Scholar 
    41.Moustafa, A. et al. Ecological Prominence of Juniperus phoenicea L. growing in Gebel Halal, North Sinai, Egypt. Catrina 15, 11–23 (2016).
    Google Scholar 
    42.Dalby, A. Siren Feasts: A History of Food and Gastronomy in Greece (Routledge, 1997).
    Google Scholar 
    43.Klimko, M. et al. Morphological variation of Juniperus oxycedrus subsp. oxycedrus (Cupressaceae) in the Mediterranean region. Flora 202, 133–147. https://doi.org/10.1016/j.flora.2006.03.006 (2007).Article 

    Google Scholar 
    44.Farjon, A. A Monograph of Cupressaceae and Sciadopitys (Royal Botanic Gardens, 2005).
    Google Scholar 
    45.Farjon, A. A Handbook of the World’s Conifers (2 vols.) Vol. 1 (Brill, 2010).
    Google Scholar 
    46.Avci, M. & Zielinski, J. Juniperus oxycedrus f. yaltirikiana (Cupressaceae): A new form from NW Turkey. Phytol. Balcanica 14, 37–40 (2008).
    Google Scholar 
    47.Browicz, K. & Ielioski, J. Chorology of Trees and Shrubs in Southwest Asia and Adjacent Regions (PWN, 1984).
    Google Scholar 
    48.Adams, R. P. Junipers of the World: The Genus Juniperus (Trafford Publishing, 2014).
    Google Scholar 
    49.Liphschitz, N., Waisel, Y. & Lev-Yadun, S. Dendrochronological investigations in Iran. Tree-Ring. Bull. 39, 39–45 (1979).
    Google Scholar 
    50.Douaihy, B. et al. Morphological versus molecular markers to describe variability in Juniperus excelsa subsp. excelsa (Cupressaceae). AoB Plants https://doi.org/10.1093/aobpla/pls013 (2012).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    51.Khajjak, M. H. et al. Seed and cone biometry of Juniperus excelsa from three Provenances in Balochistan. Int. J. Biosci. 10, 345–355. https://doi.org/10.12692/ijb/10.1.345-355 (2017).Article 

    Google Scholar 
    52.Klimko, M. et al. Morphological variation of Juniperus oxycedrus subsp oxycedrus (Cupressaceae) in the Mediterranean region. Flora 202, 133–147. https://doi.org/10.1016/j.flora.2006.03.006 (2007).Article 

    Google Scholar 
    53.Schulz, C., Jagel, A. & Stützel, T. Cone morphology in Juniperus in the light of cone evolution in Cupressaceae s.l. Flora 198, 161–177. https://doi.org/10.1078/0367-2530-00088 (2003).Article 

    Google Scholar 
    54.Arista, M., Ortiz, P. L. & Talavera, S. Reproductive cycles of two allopatric subspecies of Juniperus oxycedrus (Cupressaceae). Flora 196, 114–120. https://doi.org/10.1016/S0367-2530(17)30026-9 (2001).Article 

    Google Scholar 
    55.Juan, R., Pastor, J., Fernández, I. & Diosdado, J. C. Relationships between mature cone traits and seed viability in Juniperus oxycedrus L. subsp macrocarpa (Sm.) Ball (Cupressaceae). Acta Biol. Cracov. Bot 45, 69–78 (2003).
    Google Scholar 
    56.Ward, L. & Shellswell, C. Looking After Juniper, Ecology, Conservation and Folklore (Plantlife Press, 2017).
    Google Scholar 
    57.García, D., Zamora, R., Gómez, J. M., Jordano, P. & Hódar, J. A. Geographical variation in seed production, predation and abortion in Juniperus communis throughout its range in Europe. J. Ecol. 88, 435–446. https://doi.org/10.1046/j.1365-2745.2000.00459.x (2000).Article 

    Google Scholar 
    58.Grzeskowiak, M. & Bednorz, L. Zmiennosc morfologiczna szyszkojagod jalowca pospolitego Juniperus communis L. subsp. communis w Nadlesnictwie Kaliska [Bory Tucholskie]. Roczniki Akademii Rolniczej w Poznaniu. Botanika 5, 71–78 (2002).
    Google Scholar 
    59.Shahi, A., Movafeghi, A., Hekmat-Shoar, H., Neishabouri, A. & Iranipour, S. Demographic study of Juniperus communis L. on Mishu-Dagh altitudes in North West of Iran. Asian J. Plant Sci. 6, 1080–1087. https://doi.org/10.3923/ajps.2007.1080.1087 (2007).Article 

    Google Scholar 
    60.Thomas, P. A., El-Barghathi, M. & Polwart, A. Biological flora of the British Isles: Juniperus communis L. J. Ecol. 95, 1404–1440. https://doi.org/10.1111/j.1365-2745.2007.01308.x (2007).Article 

    Google Scholar 
    61.McCartan, S. A. & Gosling, P. G. Guidelines for seed collection and stratification of common juniper (Juniperus communis L.). Tree Plant. Notes 56, 24–29 (2013).
    Google Scholar 
    62.García, D., Zamora, R., Gómez, J. M. & Hódar, J. A. Annual variability in reproduction of Juniperus communis L. in a Mediterranean mountain: Relationship to seed predation and weather. Écoscience 9, 251–255. https://doi.org/10.1080/11956860.2002.11682711 (2002).Article 

    Google Scholar 
    63.Raatikainen, N. & Tanska, T. Cone and seed yields of the juniper (Juniperus communis) in southern and central Finland. Acta Bot. Fenn. 149, 27–39 (1993).
    Google Scholar 
    64.McCartan, S., Gosling, P. G. & Ives, L. Seed fill determination in common juniper (Juniperus communis L.). In Procdings of IUFRO Tree Seed Symposium, Recent Advances in Seed Physiology and Technology (eds Beardmore, T. L. & Simpson, J. D.) 65 (Fredricton, 2007).
    Google Scholar 
    65.McCartan, S. & Gosling, P. G. Exposed! Predicting filled and empty seeds in juniper with x-radiographs. Ecotype 38, 7 (2007).
    Google Scholar 
    66.Pers-Kamczyc, E., Tyrała-Wierucka, Ż, Rabska, M., Wrońska-Pilarek, D. & Kamczyc, J. The higher availability of nutrients increases the production but decreases the quality of pollen grains in Juniperus communis L. J. Plant Physiol. 248, 153156. https://doi.org/10.1016/j.jplph.2020.153156 (2020).CAS 
    Article 
    PubMed 

    Google Scholar 
    67.Verheyen, K. et al. Juniperus communis: Victim of the combined action of climate warming and nitrogen deposition?. Plant Biol. 11, 49–59. https://doi.org/10.1111/j.1438-8677.2009.00214.x (2009).CAS 
    Article 
    PubMed 

    Google Scholar 
    68.Kormuťák, A., Bolecek, P., Galgóci, M. & Gömöry, D. Longevity and germination of Juniperus communis L. pollen after storage. Sci. Rep. 11, 12755. https://doi.org/10.1038/s41598-021-90942-9 (2021).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    69.Yahaya, N., Lim, K. S., Noor, N. M., Othman, S. R. & Abdullah, A. Effects of clay and moisture content on soil-corrosion dynamic. Malays. J. Civ. Eng. 23, 24–32. https://doi.org/10.11113/mjce.v23.15809 (2011).Article 

    Google Scholar 
    70.Scott, D. A. (2002).71.Selwyn, L. S. ASM Handbook Volume 13C. Corrosion: Environments and Industries 306–322 (ASM International, 2006).
    Google Scholar 
    72.Ingo, G. M. et al. Large scale investigation of chemical composition, structure and corrosion mechanism of bronze archeological artefacts from Mediterranean basin. Appl. Phys. A 83, 513–520. https://doi.org/10.1007/s00339-006-3550-z (2006).ADS 
    CAS 
    Article 

    Google Scholar 
    73.Papadopoulou, O., Vassiliou, P., Grassini, S., Angelini, E. & Gouda, V. Soil-induced corrosion of ancient Roman brass: A case study. Mater. Corros. 67, 160–169. https://doi.org/10.1002/maco.201408115 (2016).CAS 
    Article 

    Google Scholar 
    74.Robbiola, L. & Portier, R. A global approach to the authentication of ancient bronzes based on the characterization of the alloy–patina–environment system. J. Cult. Herit. 7, 1–12. https://doi.org/10.1016/j.culher.2005.11.001 (2006).Article 

    Google Scholar 
    75.Vuai, S. A., Nakamura, K. & Tokuyama, A. Geochemical characteristics of runoff from acid sulfate soils in the northern area of Okinawa Island, Japan. Geochem. J. 37, 579–592 (2003).ADS 
    CAS 

    Google Scholar 
    76.Marani, D., Patterson, J. W. & Anderson, P. R. Alkaline precipitation and aging of Cu(II) in the presence of sulfate. Water Res. 29, 1317–1326. https://doi.org/10.1016/0043-1354(94)00286-G (1995).CAS 
    Article 

    Google Scholar 
    77.Baboian, R. Corrosion Tests and Standards: Application and Interpretation Vol. 20 (ASTM International, 2005).
    Google Scholar 
    78.Strandberg, H. Reactions of copper patina compounds—II. Influence of sodium chloride in the presence of some air pollutants. Atmos. Environ. 32, 3521–3526. https://doi.org/10.1016/S1352-2310(98)00058-2 (1998).ADS 
    CAS 
    Article 

    Google Scholar 
    79.Borkow, G. & Gabbay, J. Copper, an ancient remedy returning to fight microbial, fungal and viral infections. Curr. Chem. Biol. 3, 272–278 (2009).CAS 

    Google Scholar 
    80.Dollwet, H. Historic uses of copper compounds in medicine. Trace Elem. Med. 2, 80–87 (1985).
    Google Scholar 
    81.Milanino, R. Copper in medicine and personal care: A historical overview. In Copper and the Skin 149–160 (Informa Healthcare, 2006).
    Google Scholar 
    82.Robinson, M. Environmental archaeology: Approaches, techniques & applications. Antiquity 79, 229–230 (2005).
    Google Scholar 
    83.Milanesi, C. et al. Ultrastructural study of archaeological Vitis vinifera L. seeds using rapid-freeze fixation and substitution. Tissue Cell 41, 443–447. https://doi.org/10.1016/j.tice.2009.03.002 (2009).CAS 
    Article 
    PubMed 

    Google Scholar 
    84.Akahane, H., Furuno, T., Miyajima, H., Yoshikawa, T. & Yamamoto, S. Rapid wood silicification in hot spring water: An explanation of silicification of wood during the Earth’s history. Sed. Geol. 169, 219–228. https://doi.org/10.1016/j.sedgeo.2004.06.003 (2004).CAS 
    Article 

    Google Scholar 
    85.Leo, R. F. & Barghoorn, E. S. Silicification of wood. Bot. Mus. Leafl. Harv. Univ. 25, 1–47 (1976).CAS 

    Google Scholar 
    86.Hellawell, J. et al. Incipient silicification of recent conifer wood at a Yellowstone hot spring. Geochim. Cosmochim. Acta 149, 79–87. https://doi.org/10.1016/j.gca.2014.10.018 (2015).ADS 
    CAS 
    Article 

    Google Scholar  More

  • in

    Effect of biostimulants on the growth, yield and nutritional value of Capsicum annuum grown in an unheated plastic tunnel

    Plant and fruit characteristicsBiometric parameters of plantsThe analyzed cultivars (characterized by desirable morphological, physical and chemical properties, uniform ripening, suitability for mechanical harvesting, high productivity, resistance to diseases and pests) and the application of modern farming technologies can have significant effects on crop yields and quality and, in consequence, production profitability, which was also observed by other authors11,23,24,25.The influence of the combined application of biostimulants on the biometric parameters of plants in the analyzed cultivars of C. annuum is presented in Table 1. The analyzed biostimulants had no significant effect on the values of leaf greenness (SPAD), relative to the control treatment. In the tested cultivars, the mean values of this parameter ranged from 52.5 (cv. ‘Turbine F1’) to 58.8 (cv. ‘Cyklon’). In general, leaf greenness was not significantly affected by the treatment × cultivar interaction, although two homogeneous groups were identified: cvs. ‘Turbine F1’ and ‘Palivec’, and cvs. ‘Solario F1’, ‘Whitney F1’ and ‘Cyklon’.Table 1 Effect of the combined application of biostimulants on the biometric parameters (mean values ± standard deviations) of plants in the analyzed cultivars of Capsicum.Full size tableThe average weight of aboveground plant parts ranged from 112 g (cv. ‘Palivec’) to 248 g (cv. ‘Solario F1’), and average root weight ranged from 112 g (cv. ‘Palivec’) to 220 g (cv. ‘Cyklon’). These parameters were not significantly affected by the method of biostimulant application, but their values were highest in treatment I (combined application of BB Soil, BB Foliar and Multical). The analyzed C. annuum cultivars can be divided into three homogeneous groups based on the weight of aboveground plant parts, and into two homogeneous groups based on root weight, but the biostimulants exerted different effects on these parameters in each cultivar. The weight of aboveground plant parts was highest in cv. ‘Solario F1’ in treatments I and III, and lowest in cv. ‘Palivec’ in treatment III. Root weight was highest in cv. ‘Cyklon’ in treatment I, and lowest in cv. ‘Palivec’ in the control treatment.It can be concluded that the combined application of the tested biostimulants had a minor effect on the biometric parameters of pepper plants. In contrast, Thevanathan et al.26 and Bai et al.27 demonstrated that algal extracts had a considerable influence on plant height (35% increase) in pulses. Bilal28, Abou-Shlell et al.29 and Hamed30 found that the natural foliar nano-fertilizer Lithovit positively affected the vegetative growth of crop plants.YieldThe fruit yields of the analyzed C. annuum cultivars treated with biostimulants applied in different combinations are presented in Table 2. Similarly to the values of leaf greenness and biometric parameters of plants, early, marketable and total yields were determined mostly by varietal traits, whereas biostimulants exerted a minor effect. On average, ‘Whitney F1’ was the highest-yielding cultivar, and ‘Cyklon’ was the lowest-yielding cultivar. Sweet cultivars were characterized by higher yields than hot cultivars, and the best results were noted in treatment II (combined application of BB Soil, BB Foliar, Multical and MK5), although no significant differences were observed relative to the control treatment and the remaining experimental treatments. The early yield ranged from 0.2 kg·m−2 (cv. ‘Cyklon’) to 3.8 kg·m−2 (cv. ‘Whitney F1’), and ‘Cyklon’ and ‘Palivec’ (hot cultivars) were characterized by similar early yields. The marketable yield was lowest in cv. ‘Cyklon’ (3.1 kg·m−2) and highest in cv. ‘Turbine F1’ (7.3 kg·m−2). ‘Turbine F1’ and ‘Whitney F1’ were characterized by comparable marketable yields. Similar effects were observed with regard to total yield. An analysis of the values of marketable and total yields revealed that the percentage of marketable fruits was higher in hot cultivars (approx. 100% on average) than in sweet cultivars (approx. 93–99% on average), and it was lowest in cv. ‘Whitney F1’ in treatment II (combined application of BB Soil, BB Foliar, Multical and MK5)—around 88%. The analyzed C. annuum cultivars responded differently to the tested combinations of biostimulants in terms of yield, but they did not differ significantly in total fruit yield, although nine homogeneous groups were identified.Table 2 Effect of the combined application of biostimulants on fruit yield (mean values ± standard deviations) in the analyzed cultivars of Capsicum annuum.Full size tableA positive effect of titanium application on crop yields was also observed by Marcinek and Hetman31 in Sparaxis tricolor Ker Gawl, and by Grajkowski and Ochmian32 in raspberries. In a study of strawberries conducted by Michalski33, the effectiveness of titanium in plant nutrition varied across years. Dobromilska34 reported that the foliar application of titanium contributed to an increase in tomato yields and significantly enhanced the vegetative growth of tomato plants, including an increase in plant height, stem diameter and the number of leaves per plant. Normal vegetative growth and development contributes to improving crop quality, and genetic factors play a major role under identical growing conditions6.Biometric parameters of fruitThe biometric parameters of fruit in the analyzed C. annuum cultivars are presented in Table 3. Similarly to the previously described traits, the biometric parameters of pepper fruit were not significantly influenced by the tested biostimulants. The biometric parameters of fruits were affected by varietal traits, and differences were noted between sweet and hot cultivars. The fruits of sweet cultivars had higher weight, larger horizontal diameter, thicker skin and smaller vertical diameter, compared with hot cultivars. No significant treatment × cultivar interaction was found for the weight, vertical diameter or horizontal diameter of fruit, although several homogeneous groups could be identified based on the differences between cultivars.Table 3 Effect of the combined application of biostimulants on the biometric parameters (mean values ± standard deviations) of fruit in the analyzed cultivars of Capsicum annuum.Full size tableAverage fruit weight varied widely across cultivars, from 39 g (cv. ‘Cyklon’) to 224 g (cv. ‘Solario F1’). Hot cultivars (‘Cyklon’ and ‘Palivec’) formed a homogeneous group based on fruit weight. The fruit weight in hot cultivars of C. annuum was similar to that reported by Islam et al.24. Sweet and hot pepper cultivars differ also in fruit shape. The fruits of hot cultivars are long and narrow, whereas the fruits of sweet cultivars have similar horizontal and vertical dimeters. Sweet cultivars are similar in terms of vertical diameter, and they differ mostly in average horizontal diameter. Fruits with the smallest mean vertical diameter (9.1 cm) were harvested from plants of cv. ‘Turbine F1’, and fruits with the largest mean vertical diameter (14.6 cm) were harvested from plants of cv. ‘Palivec’. Fruits with the smallest mean horizontal diameter (2.4 cm) were harvested from plants of cv. ‘Palivec’, and fruits with the largest mean horizontal diameter (9.0 cm) were harvested from plants of cv. ‘Solario F1’.The fruits of sweet and hot C. annuum cultivars had pericarps of similar thickness. In hot cultivars, average skin thickness ranged from 2.9 mm (cv. ‘Palivec’) to 3.3 mm (cv. ‘Cyklon’), and in sweet cultivars—from 5.7 mm (cv.‘Turbine F1’) to 6.4 mm (cv. ‘Whitney F1’).Chemical composition of fruitThe proximate chemical composition of fruit in the analyzed C. annuum cultivars is presented in Table 4. The effects exerted by biostimulants on most chemical properties of pepper fruit (excluding L-ascorbic acid content) varied across cultivars. The applied biostimulants led to both an increase and a decrease in the content of the analyzed components in the studied cultivars. No significant differences in the concentrations of dry matter, total sugars, reducing sugars or L-ascorbic acid in pepper fruit were found between treatments. In comparison with the control treatment, significant differences were noted only for nitrate (V) levels in treatment I. The combined application of biostimulants led to an increase in the nitrate (V) content of fruit, which was nearly two-fold higher in treatment I than in the control group. The fruits of sweet cultivars had a lower content of dry matter, total sugars and L-ascorbic acid than the fruits of hot cultivars.Table 4 Effect of the combined application of biostimulants on the chemical composition (mean values ± standard deviations) of fruit in the analyzed cultivars of Capsicum annuum.Full size tableAverage dry matter content ranged from 6.4% (cv. ‘Whitney F1’) to 7.6% (cv. ‘Solario F1’) in sweet peppers, and from 11.6% (cv. ‘Cyklon’) to 12.3% (cv. ‘Cyklon’) in hot peppers. Sweet and hot cultivars of C. annuum formed separate homogeneous groups. The analyzed cultivars differed significantly in the total sugar content of fruit, which was lowest in cv. ‘Whitney F1’ and highest in cv. ‘Cyklon’. Average total sugar content ranged from 3.2 to 4.6 g∙100 g−1 fresh weight in sweet peppers, and from 6.9 to 8.4 g∙100 g−1 fresh weight in hot peppers. Cultivars ‘Whitney F1’, ‘Turbine F1’ and ‘Palivec’, and ‘Solario F1’ and ‘Palivec’ formed homogeneous groups based on the reducing sugar content of fruit, which ranged from 2.4 g∙100 g−1 fresh weight (cv. ‘Whitney F1’ and ‘Turbine F1’) to 5.1 g∙100 g−1 fresh weight (cv. ‘Cyklon’). Average L-ascorbic acid content 97 mg∙100 g−1 fresh weight in sweet peppers, and 107 mg∙100 g−1 fresh weight in hot peppers. Similarly to the dry matter content of fruit, separate homogeneous groups were formed by sweet and hot cultivars of C. annuum. The combined application of biostimulants caused an increase in the average nitrate (V) content of pepper fruit, which ranged from 136 mg N-NO3 kg−1 fresh weight (cv. ‘Palivec’) to 259 mg N-NO3 kg−1 fresh weight (cv. ‘Turbine F1’).According to Selahle et al.35, the taste of sweet peppers is determined by the content of sugars and organic acids. Taste is a complex phenomenon, and it is affected by environmental factors during plant growth36,37. From the nutritional perspective, the dry matter of pepper fruit consists of sugars, organic acids and other compounds with proven nutraceutical efficacy, including hydrophilic compounds such as ascorbic acid, flavonoids and phenolic acids, and lipophilic compounds such as carotenoids and tocopherols38,39,40,41. Fresh peppers are rich in valuable compounds including vitamins (in particular vitamin C), mineral salts, macronutrients and micronutrients42. According to Hallmann et al.43, pepper fruit contains on average 8.5–10.5 g 100 g fresh weight of dry matter, 3.6–6.6 g 100 g fresh weight of total sugars, 2.4–4.8 g 100 g fresh weight of reducing sugars, and 115–153 mg 100 g fresh weight of L-ascorbic acid, depending on cultivation method. Similar values were determined in the present study. The content of nitrates (V) depends on soil and climatic conditions, fertilization and plant species44, which were identical in all treatments in this study. The tested biostimulants exerted varied effects on the nutrient content of C. annuum fruit. The nitrate (V) content of fruit was higher in experimental treatments than in the control group, but the noted differences were significant only relative to treatment I where the maximum permissible level of 250 mg N-NO3 kg−1 fresh weight was exceeded43. ‘Turbine F1’, followed by ‘Solario F1’, were most prone to nitrate (V) accumulation in fruit. In this respect, the effect exerted by the biostimulants was undesirable.Correlations between the analyzed biometric parameters and chemical composition of fruitDue to the fact that the tested biostimulants exerted no clear-cut effects on the analyzed biometric parameters of C. annuum fruit, and for the sake of simplicity, the measurement data were pooled into two experimental groups of sweet and hot cultivars. The results of a correlation analysis of the above parameters are presented in Table 5. The absolute values of the correlation coefficient ranged from 0.012 (correlation between the L-ascorbic acid content and nitrate (V) content of fruit in sweet cultivars) to 0.932 (correlation between the weight and horizontal diameter of fruit in sweet cultivars). Significant correlations were noted in 36 cases out of 72 comparisons, whereas practical significance (coefficient of correlation minimum 0.4) was observed in 33 comparisons. The nitrate (V) content of fruit was least frequently correlated, and the horizontal diameter, total sugar content and reducing sugar content of fruit were most frequently correlated with the remaining parameters. The nature of relationships between the analyzed parameters was largely affected by the type of cultivar. Differences in the significance of correlation coefficients were found in 19 pairs of the compared traits, and differences in their direction (positive, negative) were observed in 11 pairs out of 36 comparisons. The significance and direction of correlations were consistent only with regard to horizontal diameter vs. the total sugar content and reducing sugar content, and skin thickness vs. reducing sugar content and L-ascorbic acid content. This implies that irrespective of cultivar, an increase in the horizontal diameter of fruit was associated with an increase in sugar content, and an increase in skin thickness was associated with an increase in the content of reducing sugars and L-ascorbic acid. Therefore, it can be assumed that the fruits characterized by a larger horizontal diameter and thicker skin are richer in nutrients.Table 5 Pearson’s coefficients of correlation between the analyzed parameters of Capsicum annuum fruit.Full size tableIn the group of fruit biometric parameters, the strongest correlation was found between the horizontal diameter and weight of fruit in sweet cultivars (coefficient of determination R2 = 0.87), and it was well described by a linear function (Fig. 1a). An increase in the horizontal diameter of fruit from around 4.7 cm to around 10.2 cm was accompanied by a proportional increase in fruit weight from around 53 g to around 254 g (by approx. 380%). Equations with the minimum value of the determination coefficient (0.4) were also derived for the correlations between the vertical diameter and weight of fruit, and between the horizontal diameter and skin thickness of fruit in hot cultivars (Figs. 1b and 1c). An increase in the vertical diameter of fruit by around 65% increased their weight by around 40%, and an increase in the horizontal diameter of fruit by around 120% increased their skin thickness by around 40%.Figure 1Relationships between the biometric parameters of Capsicum annuum fruit: (a) horizontal diameter and weight of sweet peppers, (b) vertical diameter and weight of hot peppers, (c) horizontal diameter and skin thickness of hot peppers.Full size imageThe biometric parameters and chemical composition of pepper fruit are strongly correlated (Figs. 2 and 3). Three and six equations with the minimum value of the determination coefficient (0.4) were derived for the correlations between fruit parameters in sweet and hot pepper cultivars, respectively. In sweet cultivars, the dry matter content of fruit was affected by their weight and horizontal diameter, and the noted relationships were directly proportional. An increase in fruit weight by around 360% (Fig. 2a) and an increase in the horizontal diameter of fruit by around 115% (Fig. 2c) increased their dry matter content by around 35%. An increase in the vertical diameter of fruit by around 60% increased their L-ascorbic acid content by around 25% (Fig. 2b). In hot cultivars, the chemical composition of fruit was most significantly influenced by horizontal diameter, followed by skin thickness. An increase in the horizontal diameter of fruit from around 2.0 cm to around 4.5 cm was accompanied by an increase in their total sugar content by around 50% (from approx. 6.5 g∙100 g−1 fresh weight to approx. 9.8 g∙100 g−1 fresh weight) (Fig. 3b), reducing sugar content—by around 100% (from approx. 3.0 g∙100 g−1 fresh weight to approx. 6.1 g∙100 g−1 fresh weight) (Fig. 3c) and nitrate (V) content—by around 80% (from approx. 125 mg N-NO3 kg−1 fresh weight to approx. 228 mg N-NO3 kg−1 fresh weight) (Fig. 3d). In turn, an increase in skin thickness (from approx. 2.2 mm to approx. 4.2 mm, by approx. 90%) was accompanied by an increase in reducing sugar content (Fig. 3e) and L-ascorbic acid content (from approx. 98 mg∙100 g−1 fresh weight to approx. 118 mg∙100 g−1 fresh weight, by approx. 20%) (Fig. 3f). An increase in the vertical diameter of fruit from around 9.8 cm to around 16.2 cm decreased their reducing sugar content by around 50% (Fig. 3a).Figure 2Relationships between the biometric parameters and chemical composition of fruit in sweet cultivars of Capsicum annuum: (a) weight and dry matter content, (b) vertical diameter and L-ascorbic acid content, (c) horizontal diameter and dry matter content.Full size imageFigure 3Relationships between the biometric parameters and chemical composition of fruit in hot cultivars of Capsicum annuum: (a) vertical diameter and reducing sugar content, (b) horizontal diameter and total sugar content, (c) horizontal diameter and reducing sugar content, (d) horizontal diameter and nitrate (V) content, (e) skin thickness and reducing sugar content, (f) skin thickness and L-ascorbic acid content.Full size imageIn the group of the chemical composition parameters of fruit, the strongest correlation was found between total sugar content and reducing sugar content (R2 = 0.72) in sweet cultivars (Fig. 4a), and between total sugar content and nitrate (V) content (R2 = 0.59) in hot cultivars (Fig. 4b). These relationships can be described by linear functions. An increase in the total sugar content of sweet peppers from around 2.0 g∙100 g−1 fresh weight to around 5.0 g∙100 g−1 fresh weight (by approx. 150%) was accompanied by an increase in reducing sugar content by around 150%, which indicates that the ratio between both sugar fractions remained unchanged. An increase in the total sugar content of hot peppers from around 5.8 g∙100 g−1 fresh weight to around 11.1 g∙100 g−1 fresh weight (by approx. 90%) was accompanied by an increase in nitrate (V) content from around 110 mg N-NO3 kg−1 fresh weight to around 250 mg N-NO3 kg−1 fresh weight (by approx. 120%).Figure 4Relationships between the chemical composition of Capsicum annuum fruit: (a) total sugar content and reducing sugar content of sweet peppers, (b) total sugar content and nitrate (V) content of hot peppers.Full size image More

  • in

    Forest fires and climate-induced tree range shifts in the western US

    1.von Humboldt, A. & Bonpland, A. Essay on the Geography of Plants (Univ. of Chicago Press, 1807).2.Woodward, F. I. Climate and Plant Distribution (Cambridge Univ. Press, 1987).3.Pausas, J. G. & Bond, W. J. Alternative biome states in terrestrial ecosystems. Trends Plant Sci. 25, 250–263 (2020).CAS 
    PubMed 

    Google Scholar 
    4.Kelly, A. E. & Goulden, M. L. Rapid shifts in plant distribution with recent climate change. Proc. Natl Acad. Sci. 105, 11823–11826 (2008).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    5.Koide, D., Yoshida, K., Daehler, C. C. & Mueller-Dombois, D. An upward elevation shift of native and non-native vascular plants over 40 years on the island of Hawai’i. J. Veg. Sci. 28, 939–950 (2017).
    Google Scholar 
    6.Thomas, C. D. Climate, climate change and range boundaries: climate and range boundaries. Divers. Distrib. 16, 488–495 (2010).
    Google Scholar 
    7.Lenoir, J. & Svenning, J.-C. Climate-related range shifts—a global multidimensional synthesis and new research directions. Ecography 38, 15–28 (2015).
    Google Scholar 
    8.Chen, I.-C., Hill, J. K., Ohlemuller, R., Roy, D. B. & Thomas, C. D. Rapid range shifts of species associated with high levels of climate warming. Science 333, 1024–1026 (2011).ADS 
    CAS 
    PubMed 

    Google Scholar 
    9.Grabherr, G., Gottfried, M. & Pauli, H. Climate change impacts in alpine environments: climate change impacts in alpine environments. Geogr. Compass 4, 1133–1153 (2010).
    Google Scholar 
    10.Zhu, K., Woodall, C. W. & Clark, J. S. Failure to migrate: lack of tree range expansion in response to climate change. Glob. Change Biol. 18, 1042–1052 (2012).ADS 

    Google Scholar 
    11.Im, S. T., Kharuk, V. I., Sukachev Institute of Forest SB RAS – subdivision of FSC KSC SB RAS; Siberian Federal University & Lee, V. G. Migration of the northern evergreen needleleaf timberline in Siberia in the 21st century. Sovrem. Probl. Distantsionnogo Zondirovaniya Zemli Iz Kosm. 17, 176–187 (2020).
    Google Scholar 
    12.Loarie, S. R. et al. The velocity of climate change. Nature 462, 1052–1055 (2009).ADS 
    CAS 
    PubMed 

    Google Scholar 
    13.Murphy, H. T., VanDerWal, J. & Lovett-Doust, J. Signatures of range expansion and erosion in eastern North American trees: signatures of range expansion and erosion. Ecol. Lett. 13, 1233–1244 (2010).PubMed 

    Google Scholar 
    14.Aitken, S. N., Yeaman, S., Holliday, J. A., Wang, T. & Curtis-McLane, S. Adaptation, migration or extirpation: climate change outcomes for tree populations: climate change outcomes for tree populations. Evol. Appl. 1, 95–111 (2008).PubMed 
    PubMed Central 

    Google Scholar 
    15.Corlett, R. T. & Westcott, D. A. Will plant movements keep up with climate change? Trends Ecol. Evol. 28, 482–488 (2013).PubMed 

    Google Scholar 
    16.Williams, M. I. & Dumroese, R. K. Preparing for climate change: forestry and assisted migration. J. For. 111, 287–297 (2013).
    Google Scholar 
    17.Anderson, J. T. & Wadgymar, S. M. Climate change disrupts local adaptation and favours upslope migration. Ecol. Lett. 23, 181–192 (2020).PubMed 

    Google Scholar 
    18.Svenning, J.-C. & Sandel, B. Disequilibrium vegetation dynamics under future climate change. Am. J. Bot. 100, 1266–1286 (2013).PubMed 

    Google Scholar 
    19.Anderson, R. P. When and how should biotic interactions be considered in models of species niches and distributions? J. Biogeogr. 44, 8–17 (2017).
    Google Scholar 
    20.Wilkinson, D. M. Mycorrhizal fungi and quaternary plant migrations. Glob. Ecol. Biogeogr. Lett. 7, 137 (1998).
    Google Scholar 
    21.Wilkinson, D. M. Plant colonization: are wind dispersed seeds really dispersed by birds at larger spatial and temporal scales? J. Biogeogr. 24, 61–65 (1997).
    Google Scholar 
    22.MacArthur, R. H. Geographical Ecology: Patterns in the Distribution of Species (Princeton Univ. Press, 1984).23.Pigot, A. L. & Tobias, J. A. Species interactions constrain geographic range expansion over evolutionary time. Ecol. Lett. 16, 330–338 (2013).PubMed 

    Google Scholar 
    24.Svenning, J.-C. et al. The influence of interspecific interactions on species range expansion rates. Ecography 37, 1198–1209 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    25.Liang, Y., Duveneck, M. J., Gustafson, E. J., Serra-Diaz, J. M. & Thompson, J. R. How disturbance, competition, and dispersal interact to prevent tree range boundaries from keeping pace with climate change. Glob. Chang. Biol. 24, e335–e351 (2018).ADS 
    PubMed 

    Google Scholar 
    26.Moorcroft, P. R., Pacala, S. W. & Lewis, M. A. Potential role of natural enemies during tree range expansions following climate change. J. Theor. Biol. 241, 601–616 (2006).ADS 
    MathSciNet 
    CAS 
    PubMed 
    MATH 

    Google Scholar 
    27.Moran, E. V. & Ormond, R. A. Simulating the interacting effects of intraspecific variation, disturbance, and competition on climate-driven range shifts in trees. PLoS ONE 10, e0142369 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    28.Stralberg, D. et al. Wildfire-mediated vegetation change in boreal forests of Alberta. Can. Ecosphere 9, e02156 (2018).
    Google Scholar 
    29.Alexander, J. M., Diez, J. M. & Levine, J. M. Novel competitors shape species’ responses to climate change. Nature 525, 515–518 (2015).ADS 
    CAS 
    PubMed 

    Google Scholar 
    30.Ettinger, A. & HilleRisLambers, J. Competition and facilitation may lead to asymmetric range shift dynamics with climate change. Glob. Chang. Biol. 23, 3921–3933 (2017).ADS 
    PubMed 

    Google Scholar 
    31.Caplat, P., Anand, M. & Bauch, C. Interactions between climate change, competition, dispersal, and disturbances in a tree migration model. Theor. Ecol. 1, 209–220 (2008).
    Google Scholar 
    32.Serra-Diaz, J. M., Scheller, R. M., Syphard, A. D. & Franklin, J. Disturbance and climate microrefugia mediate tree range shifts during climate change. Landsc. Ecol. 30, 1039–1053 (2015).
    Google Scholar 
    33.Urban, M. C., Tewksbury, J. J. & Sheldon, K. S. On a collision course: competition and dispersal differences create no-analogue communities and cause extinctions during climate change. Proc. R. Soc. B Biol. Sci. 279, 2072–2080 (2012).
    Google Scholar 
    34.Pausas, J. G. & Keeley, J. E. Wildfires as an ecosystem service. Front. Ecol. Environ. 17, 289–295 (2019).
    Google Scholar 
    35.Harvey, B. J., Donato, D. C. & Turner, M. G. High and dry: post-fire tree seedling establishment in subalpine forests decreases with post-fire drought and large stand-replacing burn patches: Drought and post-fire tree seedlings. Glob. Ecol. Biogeogr. 25, 655–669 (2016).
    Google Scholar 
    36.Coop, J. D. et al. Wildfire-driven forest conversion in western north American landscapes. BioScience 70, 659–673 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    37.Turner, M. G., Braziunas, K. H., Hansen, W. D. & Harvey, B. J. Short-interval severe fire erodes the resilience of subalpine lodgepole pine forests. Proc. Natl Acad. Sci. 116, 11319–11328 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    38.Stevens‐Rumann, C. S. et al. Evidence for declining forest resilience to wildfires under climate change. Ecol. Lett. 21, 243–252 (2018).PubMed 

    Google Scholar 
    39.Hanes, T. L. Succession after fire in the Chaparral of southern California. Ecol. Monogr. 41, 27–52 (1971).
    Google Scholar 
    40.McKenzie, D. A. & Tinker, D. B. Fire-induced shifts in overstory tree species composition and associated understory plant composition in Glacier National Park, Montana. Plant Ecol. 213, 207–224 (2012).
    Google Scholar 
    41.Walker, X. J., Mack, M. C. & Johnstone, J. F. Predicting ecosystem resilience to fire from tree ring analysis in black spruce forests. Ecosystems 20, 1137–1150 (2017).
    Google Scholar 
    42.Hart, S. J. et al. Examining forest resilience to changing fire frequency in a fire-prone region of boreal forest. Glob. Change Biol. 25, 869–884 (2019).ADS 

    Google Scholar 
    43.Davis, K. T. et al. Wildfires and climate change push low-elevation forests across a critical climate threshold for tree regeneration. Proc. Natl Acad. Sci. 116, 6193–6198 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    44.Abatzoglou, J. T., Williams, A. P. & Barbero, R. Global emergence of anthropogenic climate change in fire weather indices. Geophys. Res. Lett. 46, 326–336 (2019).ADS 

    Google Scholar 
    45.Enright, N. J., Fontaine, J. B., Bowman, D. M., Bradstock, R. A. & Williams, R. J. Interval squeeze: altered fire regimes and demographic responses interact to threaten woody species persistence as climate changes. Front. Ecol. Environ. 13, 265–272 (2015).
    Google Scholar 
    46.Dobrowski, S. Z. et al. Forest structure and species traits mediate projected recruitment declines in western US tree species: tree recruitment patterns in the western US. Glob. Ecol. Biogeogr. 24, 917–927 (2015).
    Google Scholar 
    47.Anderson, T. W. An Introduction to Multivariate Statistical Analysis (Wiley-Interscience, 2003).48.Keeley, J. E. Fire intensity, fire severity and burn severity: a brief review and suggested usage. Int. J. Wildland Fire 18, 116 (2009).
    Google Scholar 
    49.Tollefson, J. Quercus chrysolepis. https://www.fs.fed.us/database/feis/plants/tree/quechr/all.html (2008).50.Fryer, J. Quercus kelloggii. https://www.fs.fed.us/database/feis/plants/tree/quekel/all.html (2007).51.Meyer, R. Chrysolepis chrysophylla. https://www.fs.fed.us/database/feis/plants/tree/quekel/all.html (2012).52.Michelle, A. Pinus contorta var. latifolia. https://www.fs.fed.us/database/feis/plants/tree/pinconl/all.html (2003).53.Cope, A. Pinus contorta var. murrayana. https://www.fs.fed.us/database/feis/plants/tree/pinconm/all.html (1993).54.Cope, A. Pinus contorta var. contorta. https://www.fs.fed.us/database/feis/plants/tree/pinconc/all.html (1993).55.Rodman, K. C. et al. A trait‐based approach to assessing resistance and resilience to wildfire in two iconic North American conifers. J. Ecol. https://doi.org/10.1111/1365-2745.13480 (2020).56.Davis, K. T., Higuera, P. E. & Sala, A. Anticipating fire‐mediated impacts of climate change using a demographic framework. Funct. Ecol. 32, 1729–1745 (2018).
    Google Scholar 
    57.Gutzler, D. S. & Robbins, T. O. Climate variability and projected change in the western United States: regional downscaling and drought statistics. Clim. Dyn. 37, 835–849 (2011).
    Google Scholar 
    58.Leung, L. R. et al. Mid-century ensemble regional climate change scenarios for the western United States. Clim. Chang. 62, 75–113 (2004).
    Google Scholar 
    59.Allen, C. D. et al. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. Ecol. Manag. 259, 660–684 (2010).
    Google Scholar 
    60.Williams, A. P. et al. Temperature as a potent driver of regional forest drought stress and tree mortality. Nat. Clim. Chang. 3, 292–297 (2013).ADS 

    Google Scholar 
    61.Anderegg, W. R. L. et al. Climate-driven risks to the climate mitigation potential of forests. Science 368, eaaz7005 (2020).CAS 
    PubMed 

    Google Scholar 
    62.Lenoir, J., Gegout, J. C., Marquet, P. A., de Ruffray, P. & Brisse, H. A significant upward shift in plant species optimum elevation during the 20th century. Science 320, 1768–1771 (2008).ADS 
    CAS 
    PubMed 

    Google Scholar 
    63.R Core Team. R: A language and environment for statistical computing. (R Foundation for Statistical Computing, 2020).64.RStudio Team. RStudio: Integrated Development Environment for R. (RStudio, PBC, 2020).65.U.S. Forest Service. Forest Inventory and Analysis National Core Field Guide. https://www.fia.fs.fed.us/library/field-guides-methods-proc/docs/2017/core_ver7-2_10_2017_final.pdf (2017).66.U.S. EPA. Level I Ecoregions of North America Shapefile. (2010).67.Wang, T., Hamann, A., Spittlehouse, D. & Carroll, C. Locally downscaled and spatially customizable climate data for historical and future periods for north America. PLoS ONE 11, e0156720 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    68.Naimi, B., Hamm, N. A. S., Groen, T. A., Skidmore, A. K. & Toxopeus, A. G. Where is positional uncertainty a problem for species distribution modelling? Ecography 37, 191–203 (2014).
    Google Scholar 
    69.Broennimann, O. et al. Measuring ecological niche overlap from occurrence and spatial environmental data: measuring niche overlap. Glob. Ecol. Biogeogr. 21, 481–497 (2012).
    Google Scholar 
    70.Hill, A. avephill/wildfire-plant_RS: Forest fires and climate-induced tree range shifts in the western US. https://doi.org/10.5281/ZENODO.5555390 (2021). More

  • in

    Raised seasonal temperatures reinforce autumn Varroa destructor infestation in honey bee colonies

    1.IPCC. Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. IPCC (IPCC, 2014).2.Walther, G. R. et al. Ecological responses to recent climate change. Nature 416, 389–395 (2002).CAS 
    PubMed 
    ADS 

    Google Scholar 
    3.Parmesan, C. & Yohe, G. A globally coherent fingerprint of climate change impacts across natural systems. Nature 421, 37–42 (2003).CAS 
    PubMed 
    ADS 

    Google Scholar 
    4.Peñuelas, J. & Filella, I. Responses to a warming world. Science (80-). 294, 793–795 (2001).
    Google Scholar 
    5.Ockendon, N. et al. Mechanisms underpinning climatic impacts on natural populations: Altered species interactions are more important than direct effects. Glob. Change Biol. 20, 2221–2229 (2014).ADS 

    Google Scholar 
    6.Walther, G.-R. Community and ecosystem responses to recent climate change. Philos. Trans. R. Soc. B Biol. Sci. 365, 2019–2024 (2010).
    Google Scholar 
    7.Root, T. L. et al. Fingerprints of global warming on wild animals and plants. Nature 421, 57–60 (2003).CAS 
    PubMed 
    ADS 

    Google Scholar 
    8.Klein, A. M. et al. Importance of pollinators in changing landscapes for world crops. Proc. R. Soc. B Biol. Sci. 274, 303–313 (2007).
    Google Scholar 
    9.Vanbergen, A. J. et al. Threats to an ecosystem service: Pressures on pollinators. Front. Ecol. Environ. 11, 251–259 (2013).
    Google Scholar 
    10.Hung, K. L. J., Kingston, J. M., Albrecht, M., Holway, D. A. & Kohn, J. R. The worldwide importance of honey bees as pollinators in natural habitats. Proc. R. Soc. B Biol. Sci. 285, 20172140 (2018).
    Google Scholar 
    11.Watanabe, M. E. Pollination worries rise as honey bees decline. Science (80-). 265, 1170 (1994).CAS 
    ADS 

    Google Scholar 
    12.Chauzat, M.-P. et al. Demographics of the European apicultural industry. PLoS ONE 8, e79018 (2013).PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    13.Conte, Y. L. & Navajas, M. Climate change: Impact on honey bee populations and diseases. OIE Rev. Sci. Tech. 27, 485–510 (2008).
    Google Scholar 
    14.Le Conte, Y., Ellis, M. & Ritter, W. Varroa mites and honey bee health: Can Varroa explain part of the colony losses?. Apidologie 41, 353–363 (2010).
    Google Scholar 
    15.Nürnberger, F., Härtel, S. & Steffan-Dewenter, I. Seasonal timing in honey bee colonies: Phenology shifts affect honey stores and Varroa infestation levels. Oecologia 189, 1121–1131 (2019).PubMed 
    ADS 

    Google Scholar 
    16.Traynor, K. S. et al. Multiyear survey targeting disease incidence in US honey bees. Apidologie https://doi.org/10.1007/s13592-016-0431-0 (2016).Article 

    Google Scholar 
    17.Ramsey, S. D. et al. Varroa destructor feeds primarily on honey bee fat body tissue and not hemolymph. Proc. Natl. Acad. Sci. U. S. A. 116, 1792–1801 (2019).CAS 
    PubMed 
    PubMed Central 

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

    Google Scholar 
    19.Switanek, M., Crailsheim, K., Truhetz, H. & Brodschneider, R. Modelling seasonal effects of temperature and precipitation on honey bee winter mortality in a temperate climate. Sci. Total Environ. 579, 1581–1587 (2017).CAS 
    PubMed 
    ADS 

    Google Scholar 
    20.Genersch, E. et al. The German bee monitoring project: A long term study to understand periodically high winter losses of honey bee colonies. Apidologie 41, 332–352 (2010).CAS 

    Google Scholar 
    21.van Dooremalen, C. et al. Winter survival of individual honey bees and honey bee colonies depends on level of Varroa destructor infestation. PLoS One 7, e36285 (2012).PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    22.Morawetz, L. et al. Health status of honey bee colonies (Apis mellifera) and disease-related risk factors for colony losses in Austria. PLoS One 14, e0219293 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    23.Fries, I., Imdorf, A. & Rosenkranz, P. Survival of mite infested (Varroa destructor) honey bee (Apis mellifera) colonies in a Nordic climate. Apidologie 37, 564–570 (2006).
    Google Scholar 
    24.Guzmán-Novoa, E. et al. Varroa destructor is the main culprit for the death and reduced populations of overwintered honey bee (Apis mellifera) colonies in Ontario, Canada. Apidologie 41, 443–450 (2010).
    Google Scholar 
    25.Giacobino, A. et al. Environment or beekeeping management: What explains better the prevalence of honey bee colonies with high levels of Varroa destructor?. Res. Vet. Sci. 112, 1–6 (2017).PubMed 

    Google Scholar 
    26.van de Pol, M. et al. Identifying the best climatic predictors in ecology and evolution. Methods Ecol. Evol. 7, 1246–1257 (2016).
    Google Scholar 
    27.Leza, M. M., Miranda-Chueca, M. A. & Purse, B. V. Patterns in Varroa destructor depend on bee host abundance, availability of natural resources, and climate in Mediterranean apiaries. Ecol. Entomol. 41, 542–553 (2016).
    Google Scholar 
    28.Dietemann, V. et al. Standard methods for Varroa research. J. Apic. Res. 52, 1–54 (2013).
    Google Scholar 
    29.Branco, M. R., Kidd, N. A. C. & Pickard, R. S. A comparative evaluation of sampling methods for Varroa destructor (Acari: Varroidae) population estimation. Apidologie 37, 452–461 (2006).
    Google Scholar 
    30.Haylock, M. R. et al. A European daily high-resolution gridded data set of surface temperature and precipitation for 1950–2006. J. Geophys. Res. Atmos. 113, D20119 (2008).ADS 

    Google Scholar 
    31.Bailey, L. D. & van de Pol, M. climwin: An R toolbox for climate window analysis. PLoS One 11, 1–27 (2016).
    Google Scholar 
    32.Hartig, F. Residual Diagnostics for Hierachical (Multi-Level/Mixed) Regression Models. (2021).33.Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 51 (2014).
    Google Scholar 
    34.Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest Package: Tests in linear mixed effects models. J. Stat. Softw. 82, 1–26 (2017).
    Google Scholar 
    35.R Core Team. R: A Language and Environment for Statistical Computing. (2021).36.Seeley, T. D. & Morse, R. A. The nest of the honey bee (Apis mellifera L.). Insectes Soc. 23, 495–512 (1976).
    Google Scholar 
    37.Calis, J. N. M., Fries, I. & Ryrie, S. C. Population modelling of Varroa jacobsoni Oud. Apidologie 30, 111–124 (1999).
    Google Scholar 
    38.Fries, I., Hansen, H., Imdorf, A. & Rosenkranz, P. Swarming in honey bees (Apis mellifera) and Varroa destructor population development in Sweden. Apidologie 34, 389–397 (2003).
    Google Scholar 
    39.Wilde, J., Fuchs, S., Bratkowski, J. & Siuda, M. Distribution of Varroa destructor between swarms and colonies. J. Apic. Res. 44, 190–194 (2005).
    Google Scholar 
    40.Loftus, J. C., Smith, M. L. & Seeley, T. D. How honey bee colonies survive in the wild: Testing the importance of small nests and frequent swarming. PLoS One 11, 1–11 (2016).
    Google Scholar 
    41.Moretto, G., Goncalves, L. S., De Jong, D. & Bichuette, M. Z. The effects of climate and bee race on Varroa jacobsoni Oud infestations in Brazil. Apidologie 22, 197–203 (1991).
    Google Scholar 
    42.Guzmán-Novoa, E., Vandame, R. & Arechavaleta, M. E. Susceptibility of European and Africanized honey bees (Apis mellifera L.) to Varroa jacobsoni Oud. in Mexico. Apidologie 30, 173–182 (1999).
    Google Scholar 
    43.Ruttner, F. Biogeography and Taxonomy of Honeybees (Springer, 1988). https://doi.org/10.1007/978-3-642-72649-1.Book 

    Google Scholar 
    44.Adam, B. Breeding the Honeybee: A Contribution to the Science of Bee Breeding (Northern Bee Books, 2013).
    Google Scholar 
    45.Tarpy, D. R., Hatch, S. & Fletcher, D. J. C. The influence of queen age and quality during queen replacement in honeybee colonies. Anim. Behav. 59, 97–101 (2000).CAS 
    PubMed 

    Google Scholar 
    46.Simeunovic, P. et al. Nosema ceranae and queen age influence the reproduction and productivity of the honey bee colony. J. Apic. Res. 53, 545–554 (2014).
    Google Scholar 
    47.Akyol, E., Yeninar, H., Karatepe, M., Karatepe, B. & Özkök, D. Effects of queen ages on Varroa (Varroa destructor) infestation level in honey bee (Apis mellifera caucasica) colonies and colony performance. Ital. J. Anim. Sci. 6, 143–149 (2007).
    Google Scholar 
    48.Harris, J. W., Harbo, J. R., Villa, J. D. & Danka, R. G. Variable population growth of Varroa destructor (Mesostigmata: Varroidae) in colonies of honey bees (Hymenoptera: Apidae) during a 10-year period. Environ. Entomol. 32, 1305–1312 (2003).
    Google Scholar 
    49.Kruuk, L. E. B., Osmond, H. L. & Cockburn, A. Contrasting effects of climate on juvenile body size in a Southern Hemisphere passerine bird. Glob. Change Biol. 21, 2929–2941 (2015).ADS 

    Google Scholar 
    50.Dainat, B., Evans, J. D., Chen, Y. P., Gauthier, L. & Neumann, P. Predictive markers of honey bee colony collapse. PLoS One 7, e32151 (2012).CAS 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    51.Peck, D. T., Smith, M. L. & Seeley, T. D. Varroa destructor mites can nimbly climb from flowers onto foraging honey bees. PLoS One 11, e0167798 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    52.Peck, D. T. & Seeley, T. D. Mite bombs or robber lures? The roles of drifting and robbing in Varroa destructor transmission from collapsing honey bee colonies to their neighbors. PLoS One 14, e0218392 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    53.Seeley, T. D. & Smith, M. L. Crowding honeybee colonies in apiaries can increase their vulnerability to the deadly ectoparasite Varroa destructor. Apidologie 46, 716–727 (2015).
    Google Scholar 
    54.Vetharaniam, I. Predicting reproduction rate of Varroa. Ecol. Model. 224, 11–17 (2012).
    Google Scholar 
    55.Nürnberger, F., Härtel, S. & Steffan-Dewenter, I. The influence of temperature and photoperiod on the timing of brood onset in hibernating honey bee colonies. PeerJ 6, e4801. https://doi.org/10.7717/peerj.4801 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    56.Seeley, T. D. & Visscher, P. K. Survival of honeybees in cold climates: The critical timing of colony growth and reproduction. Ecol. Entomol. 10, 81–88 (1985).
    Google Scholar 
    57.Martin, S. J. Ontogenesis of the mite Varroa jacobsoni Oud. in worker brood of the honeybee Apis mellifera L. under natural conditions. Exp. Appl. Acarol. https://doi.org/10.1007/BF00055033 (1994).Article 

    Google Scholar 
    58.Martin, S. J. Reproduction of Varroa jacobsoni in cells of Apis mellifera containing one or more mother mites and the distribution of these cells. J. Apic. Res. 34, 187–196 (1995).
    Google Scholar 
    59.Sparks, T. H. et al. Advances in the timing of spring cleaning by the honeybee Apis mellifera in Poland. Ecol. Entomol. 35, 788–791 (2010).
    Google Scholar 
    60.Langowska, A. et al. Long-term effect of temperature on honey yield and honeybee phenology. Int. J. Biometeorol. 61, 1125–1132 (2017).PubMed 
    ADS 

    Google Scholar 
    61.Bordier, C. et al. Colony adaptive response to simulated heat waves and consequences at the individual level in honeybees (Apis mellifera). Sci. Rep. 7, 1–11 (2017).CAS 

    Google Scholar 
    62.Fahrenholz, L., Lamprecht, I. & Schricker, B. Thermal investigations of a honey bee colony: Thermoregulation of the hive during summer and winter and heat production of members of different bee castes. J. Comp. Physiol. B 159, 551–560 (1989).
    Google Scholar 
    63.Villa, J. D., Gentry, C. & Taylor, O. R. Jr. Preliminary observations on thermoregulation, clustering, and energy utilization in African and European Honey Bees. J. Kansas Entomol. Soc. 60, 4–14 (1987).
    Google Scholar 
    64.Anderson, D. L. & Trueman, J. W. H. Varroa jacobsoni (Acari: Varroidae) is more than one species. Exp. Appl. Acarol. 24, 165–189 (2000).CAS 
    PubMed 

    Google Scholar 
    65.Kottek, M., Grieser, J., Beck, C., Rudolf, B. & Rubel, F. World Map of the Köppen–Geiger climate classification updated. Meteorol. Zeitschrift 15, 259–263 (2006).ADS 

    Google Scholar 
    66.Schmickl, T. & Crailsheim, K. Cannibalism and early capping: Strategy of honeybee colonies in times of experimental pollen shortages. J. Comp. Physiol. A Sens. Neural Behav. Physiol. 187, 541–547 (2001).CAS 

    Google Scholar 
    67.Requier, F., Odoux, J. F., Henry, M. & Bretagnolle, V. The carry-over effects of pollen shortage decrease the survival of honeybee colonies in farmlands. J. Appl. Ecol. 54, 1161–1170 (2017).
    Google Scholar 
    68.Seeley, T. D. Honeybee Ecology. A Study of Adaptation in Social Life (Princeton University Press, 1985).
    Google Scholar 
    69.Martin, S. J. Ontogenesis of the mite Varroa jacobsoni Oud. in drone brood of the honeybee Apis mellifera L. under natural conditions. Exp. Appl. Acarol. 19, 199–210 (1995).ADS 

    Google Scholar 
    70.Amiri, E., Strand, M. K., Rueppell, O. & Tarpy, D. R. Queen quality and the impact of honey bee diseases on queen health: Potential for interactions between two major threats to colony health. Insects 8, 48 (2017).PubMed Central 

    Google Scholar 
    71.Giacobino, A. et al. Risk factors associated with failures of Varroa treatments in honey bee colonies without broodless period. Apidologie 46, 573–582 (2015).
    Google Scholar 
    72.Locke, B. Natural Varroa mite-surviving Apis mellifera honeybee populations. Apidologie 47, 467–482 (2016).
    Google Scholar 
    73.FAO. Good beekeeping practices: Practical manual on how to identify and control the main diseases of the honeybee (Apis mellifera). TECA—Technologies and practices for small agricultural producers. (2020).74.Harbo, J. R. Effect of population size on brood production, worker survival and honey gain in colonies of honeybees. J. Apic. Res. 25, 22–29 (1986).
    Google Scholar 
    75.Döke, M. A., McGrady, C. M., Otieno, M., Grozinger, C. M. & Frazier, M. Colony size, rather than geographic origin of stocks, predicts overwintering success in honey bees (Hymenoptera: Apidae) in the Northeastern United States. J. Econ. Entomol. 112, 525–533 (2019).PubMed 

    Google Scholar 
    76.Martin, S. J. The role of Varroa and viral pathogens in the collapse of honeybee colonies: A modelling approach. J. Appl. Ecol. 38, 1082–1093 (2001).
    Google Scholar  More

  • in

    Winter distribution of juvenile and sub-adult male Antarctic fur seals (Arctocephalus gazella) along the western Antarctic Peninsula

    1.Knox, G. A. Biology of the Southern Ocean (CRC Press, 2006). https://doi.org/10.1201/9781420005134Book 

    Google Scholar 
    2.Thomas, D. N. et al. The Biology of Polar Regions: The Biology of Polar Regions (Oxford University Press, 2008).
    Google Scholar 
    3.Trathan, P. N. & Hill, S. L. The Importance of Krill Predation in the Southern Ocean. In Biology and Ecology of Antarctic Krill (ed. Siegel, V.) 321–350 (Springer, 2016). https://doi.org/10.1007/978-3-319-29279-3_9.Chapter 

    Google Scholar 
    4.Atkinson, A. et al. Oceanic circumpolar habitats of Antarctic krill. Mar. Ecol. Prog. Ser. 362, 1–23 (2008).ADS 
    CAS 

    Google Scholar 
    5.Siegel, V. & Watkins, J. L. Distribution, biomass and demography of antarctic krill, Euphausia superba. In Biology and Ecology of Antarctic Krill (ed. Siegel, V.) 21–100 (Springer, 2016). https://doi.org/10.1007/978-3-319-29279-3_2.Chapter 

    Google Scholar 
    6.Reiss, C. S. et al. Overwinter habitat selection by Antarctic krill under varying sea-ice conditions: Implications for top predators and fishery management. Mar. Ecol. Prog. Ser. 568, 1–16 (2017).ADS 
    CAS 

    Google Scholar 
    7.Andrews-Goff, V. et al. Humpback whale migrations to Antarctic summer foraging grounds through the southwest Pacific Ocean. Sci. Rep. 8, 12333 (2018).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    8.Ribic, C. A., Ainley, D. G. & Fraser, W. R. Habitat selection by marine mammals in the marginal ice zone. Antarct. Sci. 3, 181–186 (1991).ADS 

    Google Scholar 
    9.Takahashi, A. et al. Migratory movements and winter diving activity of Adélie penguins in East Antarctica. Mar. Ecol. Prog. Ser. 589, 227–239 (2018).ADS 

    Google Scholar 
    10.Hückstädt, L. A. et al. Projected shifts in the foraging habitat of crabeater seals along the Antarctic Peninsula. Nat. Clim. Change 10, 472–477 (2020).ADS 

    Google Scholar 
    11.Lowther, A. D., Staniland, I., Lydersen, C. & Kovacs, K. M. Male Antarctic fur seals: Neglected food competitors of bioindicator species in the context of an increasing Antarctic krill fishery. Sci. Rep. 10, 18436 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    12.Forcada, J. & Staniland, I. J. Antarctic fur seal Arctocephalus gazella. In Encyclopedia of Marine Mammals (eds Perrin, W. F. et al.) 36–42 (Academic Press, 2009).
    Google Scholar 
    13.Boyd, I. L., McCafferty, D. J., Reid, K., Taylor, R. & Walker, T. R. Dispersal of male and female Antarctic fur seals (Arctocephalus gazella). Can. J. Fish. Aquat. Sci. https://doi.org/10.1139/f97-314 (1998).Article 

    Google Scholar 
    14.Cherel, Y., Kernaléguen, L., Richard, P. & Guinet, C. Whisker isotopic signature depicts migration patterns and multi-year intra- and inter-individual foraging strategies in fur seals. Biol. Lett. 5, 830–832 (2009).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    15.Kernaléguen, L. et al. Long-term species, sexual and individual variations in foraging strategies of fur seals revealed by stable isotopes in whiskers. PLoS ONE 7, e32916 (2012).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    16.Kernaléguen, L., Arnould, J. P. Y., Guinet, C. & Cherel, Y. Determinants of individual foraging specialization in large marine vertebrates, the Antarctic and subantarctic fur seals. J. Anim. Ecol. 84, 1081–1091 (2015).PubMed 

    Google Scholar 
    17.Arthur, B. et al. Winter habitat predictions of a key Southern Ocean predator, the Antarctic fur seal (Arctocephalus gazella). Deep Sea Res. Part II Top. Stud. Oceanogr. 140, 171–181 (2017).ADS 

    Google Scholar 
    18.Arthur, B. et al. Managing for change: Using vertebrate at sea habitat use to direct management efforts. Ecol. Indic. 91, 338–349 (2018).
    Google Scholar 
    19.Reisinger, R. R. et al. Habitat modelling of tracking data from multiple marine predators identifies important areas in the Southern Indian Ocean. Divers. Distrib. 24, 535–550 (2018).MathSciNet 

    Google Scholar 
    20.Wege, M., de Bruyn, P. J. N., Hindell, M. A., Lea, M.-A. & Bester, M. N. Preferred, small-scale foraging areas of two Southern Ocean fur seal species are not determined by habitat characteristics. BMC Ecol. 19, 36 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    21.Jones, K. A. et al. Intra-specific niche partitioning in antarctic fur seals, Arctocephalus gazella. Sci. Rep. 10, 3238 (2020).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    22.Siniff, D. B., Garrott, R. A., Rotella, J. J., Fraser, W. R. & Ainley, D. G. Opinion: Projecting the effects of environmental change on Antarctic seals. Antarct. Sci. 20, 425–435 (2008).ADS 

    Google Scholar 
    23.Raymond, B. et al. Important marine habitat off east Antarctica revealed by two decades of multi-species predator tracking. Ecography 38, 121–129 (2015).
    Google Scholar 
    24.Bestley, S., Jonsen, I. D., Hindell, M. A., Harcourt, R. G. & Gales, N. J. Taking animal tracking to new depths: Synthesizing horizontal–vertical movement relationships for four marine predators. Ecology 96, 417–427 (2015).PubMed 

    Google Scholar 
    25.Kernaléguen, L. et al. Early-life sexual segregation: Ontogeny of isotopic niche differentiation in the Antarctic fur seal. Sci. Rep. 6, 33211 (2016).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    26.Payne, M. R. Growth in the Antarctic fur seal Arctocephalus gazella. J. Zool. 187, 1–20 (1979).
    Google Scholar 
    27.Costa, D., Goebel, M. E. & Sterling, J. T. Foraging energetics and diving behavior of the Antarctic fur seal, Arctocephalus gazzella at Cape Shirreff, Livingston Island. In Antarctic Ecosystems: Models for Wider Ecological Understanding (eds Davision, W. et al.) 77–84 (New Zealand Natural Science Press, 2000).
    Google Scholar 
    28.Staniland, I. J. et al. Geographical variation in the behaviour of a central place forager: Antarctic fur seals foraging in contrasting environments. Mar. Biol. 157, 2383–2396 (2010).
    Google Scholar 
    29.Blanchet, M.-A. et al. At-sea behaviour of three krill predators breeding at Bouvetøya—Antarctic fur seals, macaroni penguins and chinstrap penguins. Mar. Ecol. Prog. Ser. 477, 285–302 (2013).ADS 

    Google Scholar 
    30.Jeanniard-du-Dot, T., Trites, A. W., Arnould, J. P. Y. & Guinet, C. Reproductive success is energetically linked to foraging efficiency in Antarctic fur seals. PLoS ONE 12, e0174001 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    31.Favilla, A. B. & Costa, D. P. Thermoregulatory strategies of diving air-breathing marine vertebrates: A review. Front. Ecol. Evol. 8, 292 (2020).
    Google Scholar 
    32.Staniland, I. J. & Robinson, S. L. Segregation between the sexes: Antarctic fur seals, Arctocephalus gazella, foraging at South Georgia. Anim. Behav. 75, 1581–1590 (2008).
    Google Scholar 
    33.Reid, K. The diet of Antarctic fur seals (Arctocephalus gazella Peters 1875) during winter at South Georgia. Antarct. Sci. 7, 241–249 (1995).ADS 

    Google Scholar 
    34.Kirkman, S. P., Wilson, W., Klages, N. T. W., Bester, M. N. & Isaksen, K. Diet and estimated food consumption of Antarctic fur seals at Bouvetøya during summer. Polar Biol. 23, 745–752 (2000).
    Google Scholar 
    35.Casaux, R., Baroni, A., Arrighetti, F., Ramón, A. & Carlini, A. Geographical variation in the diet of the Antarctic fur seal Arctocephalus gazella. Polar Biol. 26, 753–758 (2003).
    Google Scholar 
    36.Casaux, R., Baroni, A. & Ramón, A. Diet of Antarctic fur seals Arctocephalus gazella at the Danco Coast, Antarctic Peninsula. Polar Biol. 26, 49–54 (2003).
    Google Scholar 
    37.Davis, D., Staniland, I. J. & Reid, K. Spatial and temporal variability in the fish diet of Antarctic fur seal (Arctocephalus gazella) in the Atlantic sector of the Southern Ocean. Can. J. Zool. https://doi.org/10.1139/z06-071 (2006).Article 

    Google Scholar 
    38.Casaux, R., Juares, M., Carlini, A. & Corbalán, A. The diet of the Antarctic fur seal Arctocephalus gazella at the South Orkney Islands in ten consecutive years. Polar Biol. 39, 1197–1206 (2016).
    Google Scholar 
    39.Tarroux, A., Lowther, A. D., Lydersen, C. & Kovacs, K. M. Temporal shift in the isotopic niche of female Antarctic fur seals from Bouvetøya. Polar Res. 35, 31335 (2016).
    Google Scholar 
    40.Garcia-Garin, O. et al. No evidence of microplastics in Antarctic fur seal scats from a hotspot of human activity in Western Antarctica. Sci. Total Environ. 737, 140210 (2020).ADS 
    CAS 
    PubMed 

    Google Scholar 
    41.Boyd, I. L. Estimating food consumption of marine predators: Antarctic fur seals and macaroni penguins. J. Appl. Ecol. 39, 103–119 (2002).
    Google Scholar 
    42.Wilson, D. E. & Mittermeier, R. A. Handbook of the mammals of the world : vol. 4 : Sea mammals. (2014).43.Melin, S. R. et al. Reversible immobilization of free-ranging adult male California sea lions (Zalophus californianus). Mar. Mammal Sci. 29, E529–E536 (2013).
    Google Scholar 
    44.Pussini, N. & Goebel, M. E. A safer protocol for field immobilization of leopard seals (Hydrurga leptonyx). Mar. Mammal Sci. 31, 1549–1558 (2015).
    Google Scholar 
    45.Spelman, L. H. Reversible anesthesia of captive California sea lions (Zalophus californianus) with medetomidine, midazolam, butorphanol, and isoflurane. J. Zoo Wildl. Med. Off. Publ. Am. Assoc. Zoo Vet. 35, 65–69 (2004).
    Google Scholar 
    46.Cook, T. A. Butorphanol tartrate: An intravenous analgesic for outpatient surgery. Otolaryngol. Head Neck Surg. J. Am. Acad. Otolaryngol. Head Nexk Surg. 91, 251–254 (1983).CAS 

    Google Scholar 
    47.Ropert-Coudert, Y. et al. The retrospective analysis of Antarctic tracking data project. Sci. Data 7, 94 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    48.Freitas, C., Lydersen, C., Fedak, M. A. & Kovacs, K. M. A simple new algorithm to filter marine mammal Argos locations. Mar. Mammal Sci. 24, 315–325 (2008).
    Google Scholar 
    49.Bonadonna, F., Lea, M.-A., Dehorter, O. & Guinet, C. Foraging ground fidelity and route-choice tactics of a marine predator: The Antarctic fur seal Arctocephalus gazella. Mar. Ecol. Prog. Ser. 223, 287–297 (2001).ADS 

    Google Scholar 
    50.Lea, M.-A. & Dubroca, L. Fine-scale linkages between the diving behaviour of Antarctic fur seals and oceanographic features in the southern Indian Ocean. ICES J. Mar. Sci. 60, 990–1002 (2003).
    Google Scholar 
    51.Jonsen, I. D. et al. Movement responses to environment: Fast inference of variation among southern elephant seals with a mixed effects model. Ecology 100, e02566 (2019).CAS 
    PubMed 

    Google Scholar 
    52.Jonsen, I. D. et al. A continuous-time state-space model for rapid quality control of argos locations from animal-borne tags. Mov. Ecol. 8, 31 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    53.Hazen, E. L. et al. Where did they not go? Considerations for generating pseudo-absences for telemetry-based habitat models. Mov. Ecol. 9, 5 (2021).PubMed 
    PubMed Central 

    Google Scholar 
    54.O’Toole, M., Queiroz, N., Humphries, N. E., Sims, D. W. & Sequeira, A. M. M. Quantifying effects of tracking data bias on species distribution models. Methods Ecol. Evol. 12, 170–181 (2021).
    Google Scholar 
    55.Lee, J. F., Friedlaender, A. S., Oliver, M. J. & DeLiberty, T. L. Behavior of satellite-tracked Antarctic minke whales (Balaenoptera bonaerensis) in relation to environmental factors around the western Antarctic Peninsula. Anim. Biotelemetry 5, 23 (2017).
    Google Scholar 
    56.Labrousse, S. et al. Under the sea ice: Exploring the relationship between sea ice and the foraging behaviour of southern elephant seals in East Antarctica. Prog. Oceanogr. 156, 17–40 (2017).ADS 

    Google Scholar 
    57.Hazen, E. L. et al. A dynamic ocean management tool to reduce bycatch and support sustainable fisheries. Sci. Adv. 4, eaar3001 (2018).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    58.Hindell, M. A. et al. Tracking of marine predators to protect Southern Ocean ecosystems. Nature 580, 87–92 (2020).ADS 
    CAS 
    PubMed 

    Google Scholar 
    59.Barbet-Massin, M., Jiguet, F., Albert, C. H. & Thuiller, W. Selecting pseudo-absences for species distribution models: How, where and how many?. Methods Ecol. Evol. 3, 327–338 (2012).
    Google Scholar 
    60.Dormann, C. F. et al. Collinearity: A review of methods to deal with it and a simulation study evaluating their performance. Ecography 36, 27–46 (2013).
    Google Scholar 
    61.Hijmans, R. J., Phillips, S. & Elith, J. L. dismo: Species Distribution Modeling. (2020).62.Elith, J., Leathwick, J. R. & Hastie, T. A working guide to boosted regression trees. J. Anim. Ecol. 77, 802–813 (2008).CAS 
    PubMed 

    Google Scholar 
    63.Roberts, D. R. et al. Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure. Ecography 40, 913–929 (2017).
    Google Scholar 
    64.Scales, K. L. et al. Fit to predict? Eco-informatics for predicting the catchability of a pelagic fish in near real time. Ecol. Appl. 27, 2313–2329 (2017).PubMed 

    Google Scholar 
    65.Pya, N. & Wood, S. N. Shape constrained additive models. Stat. Comput. 25, 543–559 (2015).MathSciNet 
    MATH 

    Google Scholar 
    66.R Core Team. R: A Language and Environment for Statistical Computing. (2019).67.Atkinson, A. et al. Krill (Euphausia superba) distribution contracts southward during rapid regional warming. Nat. Clim. Change 9, 142–147 (2019).ADS 

    Google Scholar 
    68.Brodie, S. et al. Integrating dynamic subsurface habitat metrics into species distribution models. Front. Mar. Sci. (2018).69.Becker, E. A. et al. Moving Towards dynamic ocean management: How well do modeled ocean products predict species distributions?. Remote Sens. 8, 149 (2016).ADS 

    Google Scholar 
    70.Lellouche, J.-M. et al. Recent updates to the Copernicus Marine Service global ocean monitoring and forecasting real-time 1∕12° high-resolution system. Ocean Sci. 14, 1093–1126 (2018).ADS 

    Google Scholar 
    71.Handcock, M. S. & Raphael, M. N. Modeling the annual cycle of daily Antarctic sea ice extent. Cryosphere 14, 2159–2172 (2020).ADS 

    Google Scholar 
    72.Smith, G. C. et al. Polar ocean observations: A critical gap in the observing system and its effect on environmental predictions from hours to a season. Front. Mar. Sci. (2019).73.March, D., Boehme, L., Tintoré, J., Vélez-Belchi, P. J. & Godley, B. J. Towards the integration of animal-borne instruments into global ocean observing systems. Glob. Change Biol. 26, 586–596 (2020).ADS 

    Google Scholar 
    74.Santora, J. A. Dynamic intra-seasonal habitat use by Antarctic fur seals suggests migratory hotspots near the Antarctic Peninsula. Mar. Biol. 160, 1383–1393 (2013).
    Google Scholar 
    75.Vergani, D. F. & Coria, N. R. Increase in numbers of male fur seals Arctocephalus gazella during the summer autumn period at Mossman Peninsula (Laurie Island). Polar Biol. 9, 487–488 (1989).
    Google Scholar 
    76.Rutishauser, M. R., Costa, D. P., Goebel, M. E. & Williams, T. M. Ecological implications of body composition and thermal capabilities in young antarctic fur seals (Arctocephalus gazella). Physiol. Biochem. Zool. PBZ 77, 669–681 (2004).PubMed 

    Google Scholar 
    77.Vales, D. G., Cardona, L., García, N. A., Zenteno, L. & Crespo, E. A. Ontogenetic dietary changes in male South American fur seals Arctocephalus australis in Patagonia. Mar. Ecol. Prog. Ser. 525, 245–260 (2015).ADS 
    CAS 

    Google Scholar 
    78.Cardona, L., Vales, D., Aguilar, A., Crespo, E. & Zenteno, L. Temporal variability in stable isotope ratios of C and N in the vibrissa of captive and wild adult South American sea lions Otaria byronia: More than just diet shifts. Mar. Mammal Sci. 33, 975–990 (2017).CAS 

    Google Scholar 
    79.Costa, D. P., Gales, N. J. & Goebel, M. E. Aerobic dive limit: How often does it occur in nature?. Comp. Biochem. Physiol. A. Mol. Integr. Physiol. 129, 771–783 (2001).CAS 
    PubMed 

    Google Scholar 
    80.Biuw, M., Krafft, B. A., Hofmeyr, G. J. G., Lydersen, C. & Kovacs, K. M. Time budgets and at-sea behaviour of lactating female Antarctic fur seals Arctocephalus gazella at Bouvetøya. Mar. Ecol. Prog. Ser. 385, 271–284 (2009).ADS 

    Google Scholar 
    81.Lascara, C. M., Hofmann, E. E., Ross, R. M. & Quetin, L. B. Seasonal variability in the distribution of Antarctic krill, Euphausia superba, west of the Antarctic Peninsula. Deep Sea Res. Part Oceanogr. Res. Pap. 46, 951–984 (1999).ADS 

    Google Scholar 
    82.Lea, M.-A., Hindell, M., Guinet, C. & Goldsworthy, S. Variability in the diving activity of Antarctic fur seals, Arctocephalus gazella, at Iles Kerguelen. Polar Biol. 25, 269–279 (2002).
    Google Scholar 
    83.Vaughan, D. G. et al. Recent rapid regional climate warming on the antarctic peninsula. Clim. Change 60, 243–274 (2003).
    Google Scholar 
    84.Forcada, J., Trathan, P. N., Reid, K. & Murphy, E. J. The effects of global climate variability in pup production of antarctic fur seals. Ecology 86, 2408–2417 (2005).
    Google Scholar 
    85.Forcada, J. & Hoffman, J. I. Climate change selects for heterozygosity in a declining fur seal population. Nature 511, 462–465 (2014).ADS 
    CAS 
    PubMed 

    Google Scholar 
    86.Schwarz, L. K., Goebel, M. E., Costa, D. P. & Kilpatrick, A. M. Top-down and bottom-up influences on demographic rates of Antarctic fur seals Arctocephalus gazella. J. Anim. Ecol. 82, 903–911 (2013).PubMed 

    Google Scholar 
    87.Hoffman, J. I. & Forcada, J. Extreme natal philopatry in female Antarctic fur seals (Arctocephalus gazella). Mamm. Biol. 77, 71–73 (2012).
    Google Scholar 
    88.Hucke-Gaete, R., Osman, L. P., Moreno, C. A. & Torres, D. Examining natural population growth from near extinction: The case of the Antarctic fur seal at the South Shetlands, Antarctica. Polar Biol. 27, 304–311 (2004).
    Google Scholar  More