IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v16y2025i1d10.1038_s41467-025-64303-3.html
   My bibliography  Save this article

Global sampling decline erodes science potential of natural history collections

Author

Listed:
  • Owen Forbes

    (CSIRO)

  • Andrew G. Young

    (CSIRO)

  • Peter H. Thrall

    (CSIRO)

Abstract

The world’s natural history collections hold over two billion specimens, representing a unique spatial and taxonomic record of biodiversity on Earth over time. In recent decades, the accessibility and value of collections data have grown through specimen digitisation, enhanced connectivity, and enriched information from new genotyping and digital trait extraction. These advances are expanding the relevance of collections beyond taxonomy and evolutionary biology to fields like environmental monitoring, agriculture, biosecurity, and public health. However, their utility for addressing major global challenges relies on mobilising legacy data and continuing specimen collection and digitisation. Here we show substantial declines in the rates of collection of specimen data over recent decades, from analysis of over 150 million records from the Global Biodiversity Information Facility (GBIF) spanning more than two centuries. The degree and timing of decline varies across taxonomic groups and geographical regions. Overall, these findings suggest that the value of natural history collections as global research infrastructure is eroding due to decreased collecting of specimen data across species, locations, and time. This is occurring precisely when applications for these data have never been more important, and advances in data analytics, AI and genomics promise to unlock deeper insights from natural history collections.

Suggested Citation

  • Owen Forbes & Andrew G. Young & Peter H. Thrall, 2025. "Global sampling decline erodes science potential of natural history collections," Nature Communications, Nature, vol. 16(1), pages 1-10, December.
  • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-64303-3
    DOI: 10.1038/s41467-025-64303-3
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-025-64303-3
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-025-64303-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    2. Zeileis, Achim & Grothendieck, Gabor, 2005. "zoo: S3 Infrastructure for Regular and Irregular Time Series," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 14(i06).
    3. Grolemund, Garrett & Wickham, Hadley, 2011. "Dates and Times Made Easy with lubridate," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 40(i03).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Richard T. A. Samuel & Charles Chimedza & Caston Sigauke, 2023. "Simulation Framework to Determine Suitable Innovations for Volatility Persistence Estimation: The GARCH Approach," JRFM, MDPI, vol. 16(9), pages 1-30, September.
    2. Behm, Svenia & Haupt, Harry, 2020. "Predictability of hourly nitrogen dioxide concentration," Ecological Modelling, Elsevier, vol. 428(C).
    3. David M Newbery & Marcus Lingenfelder, 2022. "Stem girth changes in response to soil water potential in lowland dipterocarp forest in Borneo: An individualistic time-series analysis," PLOS ONE, Public Library of Science, vol. 17(6), pages 1-43, June.
    4. Massimo Albanese, 2022. "Community Enterprises: Snapshots from Italy," European Journal of Economics and Business Studies Articles, Revistia Research and Publishing, vol. 8, ejes_v8_i.
    5. Chris Heaton & Natalia Ponomareva & Qin Zhang, 2020. "Forecasting models for the Chinese macroeconomy: the simpler the better?," Empirical Economics, Springer, vol. 58(1), pages 139-167, January.
    6. Georgia Papacharalampous & Hristos Tyralis & Demetris Koutsoyiannis, 2018. "Univariate Time Series Forecasting of Temperature and Precipitation with a Focus on Machine Learning Algorithms: a Multiple-Case Study from Greece," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(15), pages 5207-5239, December.
    7. Arthur Novaes de Amorim & Rob Deardon & Vineet Saini, 2021. "A stacked ensemble method for forecasting influenza-like illness visit volumes at emergency departments," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-15, March.
    8. Pebesma, Edzer, 2012. "spacetime: Spatio-Temporal Data in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 51(i07).
    9. Gkillas, Konstantinos & Gupta, Rangan & Pierdzioch, Christian, 2020. "Forecasting realized oil-price volatility: The role of financial stress and asymmetric loss," Journal of International Money and Finance, Elsevier, vol. 104(C).
    10. Nahapetyan Yervand, 2019. "The benefits of the Velvet Revolution in Armenia: Estimation of the short-term economic gains using deep neural networks," Central European Economic Journal, Sciendo, vol. 6(53), pages 286-303, January.
    11. Laha, A. K. & Putatunda, Sayan, 2017. "Travel Time Prediction for Taxi-GPS Data Streams," IIMA Working Papers WP 2017-03-03, Indian Institute of Management Ahmedabad, Research and Publication Department.
    12. Dombi, József & Jónás, Tamás & Tóth, Zsuzsanna Eszter, 2018. "Modeling and long-term forecasting demand in spare parts logistics businesses," International Journal of Production Economics, Elsevier, vol. 201(C), pages 1-17.
    13. Fraccaroli, Nicolò & Giovannini, Alessandro & Jamet, Jean-François & Persson, Eric, 2022. "Ideology and monetary policy. The role of political parties’ stances in the European Central Bank’s parliamentary hearings," European Journal of Political Economy, Elsevier, vol. 74(C).
    14. Amita Gajewar & Gagan Bansal, 2016. "Revenue Forecasting for Enterprise Products," Papers 1701.06624, arXiv.org.
    15. Tao XIONG & LI Chongguang & Yukun BAO, 2017. "An improved EEMD-based hybrid approach for the short-term forecasting of hog price in China," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 63(3), pages 136-148.
    16. Pieter van der Spek & Chris Verhoef, 2014. "Balancing Time‐to‐Market and Quality in Embedded Systems," Systems Engineering, John Wiley & Sons, vol. 17(2), pages 166-192, June.
    17. Hyndman, Rob J. & Ahmed, Roman A. & Athanasopoulos, George & Shang, Han Lin, 2011. "Optimal combination forecasts for hierarchical time series," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2579-2589, September.
    18. Michael A Ruderman & Deirdra F Wilson & Savanna Reid, 2015. "Does Prison Crowding Predict Higher Rates of Substance Use Related Parole Violations? A Recurrent Events Multi-Level Survival Analysis," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-19, October.
    19. Kourentzes, Nikolaos & Petropoulos, Fotios & Trapero, Juan R., 2014. "Improving forecasting by estimating time series structural components across multiple frequencies," International Journal of Forecasting, Elsevier, vol. 30(2), pages 291-302.
    20. Tendai Makoni & Delson Chikobvu, 2023. "Assessing and Forecasting the Long-Term Impact of the Global Financial Crisis on Manufacturing Sales in South Africa," Economies, MDPI, vol. 11(6), pages 1-17, May.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-64303-3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.