IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v475y2023ics030438002200271x.html
   My bibliography  Save this article

SDM profiling: A tool for assessing the information-content of sampled and unsampled locations for species distribution models

Author

Listed:
  • Marsh, Charles J.
  • Gavish, Yoni
  • Kuemmerlen, Mathias
  • Stoll, Stefan
  • Haase, Peter
  • Kunin, William E.

Abstract

Species distribution models (SDMs) are key tools in biodiversity and conservation, but assessing their reliability in unsampled locations is difficult, especially where there are sampling biases. We present a spatially-explicit sensitivity analysis for SDMs – SDM profiling – which assesses the leverage that unsampled locations have on the overall model by exploring the interaction between the effect on the variable response curves and the prevalence of the affected environmental conditions. The method adds a ‘pseudo-presence’ and ‘pseudo-absence’ to unsampled locations, re-running the SDM for each, and measuring the difference between the probability surfaces of the original and new SDMs. When the standardised difference values are plotted against each other (a ‘profile plot’), each point's location can be summarized by four leverage measures, calculated as the distances to each corner. We explore several applications: visualization of model certainty; identification of optimal new sampling locations and redundant existing locations; and flagging potentially erroneous occurrence records.

Suggested Citation

  • Marsh, Charles J. & Gavish, Yoni & Kuemmerlen, Mathias & Stoll, Stefan & Haase, Peter & Kunin, William E., 2023. "SDM profiling: A tool for assessing the information-content of sampled and unsampled locations for species distribution models," Ecological Modelling, Elsevier, vol. 475(C).
    • Handle: RePEc:eee:ecomod:v:475:y:2023:i:c:s030438002200271x
    DOI: 10.1016/j.ecolmodel.2022.110170
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S030438002200271X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ecolmodel.2022.110170?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Boria, Robert A. & Olson, Link E. & Goodman, Steven M. & Anderson, Robert P., 2014. "Spatial filtering to reduce sampling bias can improve the performance of ecological niche models," Ecological Modelling, Elsevier, vol. 275(C), pages 73-77.
    2. Pierre Ploton & Frédéric Mortier & Maxime Réjou-Méchain & Nicolas Barbier & Nicolas Picard & Vivien Rossi & Carsten Dormann & Guillaume Cornu & Gaëlle Viennois & Nicolas Bayol & Alexei Lyapustin & Syl, 2020. "Spatial validation reveals poor predictive performance of large-scale ecological mapping models," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
    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. Ramos, Rodrigo Soares & Kumar, Lalit & Shabani, Farzin & Picanço, Marcelo Coutinho, 2019. "Risk of spread of tomato yellow leaf curl virus (TYLCV) in tomato crops under various climate change scenarios," Agricultural Systems, Elsevier, vol. 173(C), pages 524-535.
    2. Feng Dong & Chih-Ming Hung & Shou-Hsien Li & Xiao-Jun Yang, 2021. "Potential Himalayan community turnover through the Late Pleistocene," Climatic Change, Springer, vol. 164(1), pages 1-10, January.
    3. Dana H. Mills & Michael L. McKinney, 2024. "Climate Change and Jump Dispersal Drive Invasion of the Rosy Wolfsnail ( Euglandina rosea ) in the United States," Sustainability, MDPI, vol. 16(5), pages 1-14, February.
    4. Van Eupen, Camille & Maes, Dirk & Herremans, Marc & Swinnen, Kristijn R.R. & Somers, Ben & Luca, Stijn, 2021. "The impact of data quality filtering of opportunistic citizen science data on species distribution model performance," Ecological Modelling, Elsevier, vol. 444(C).
    5. Yinglian Qi & Xiaoyan Pu & Yaxiong Li & Dingai Li & Mingrui Huang & Xuan Zheng & Jiaxin Guo & Zhi Chen, 2022. "Prediction of Suitable Distribution Area of Plateau pika ( Ochotona curzoniae ) in the Qinghai–Tibet Plateau under Shared Socioeconomic Pathways (SSPs)," Sustainability, MDPI, vol. 14(19), pages 1-23, September.
    6. Carlos Yañez-Arenas & A. Townsend Peterson & Karla Rodríguez-Medina & Narayani Barve, 2016. "Mapping current and future potential snakebite risk in the new world," Climatic Change, Springer, vol. 134(4), pages 697-711, February.
    7. Herkt, K. Matthias B. & Barnikel, Günter & Skidmore, Andrew K. & Fahr, Jakob, 2016. "A high-resolution model of bat diversity and endemism for continental Africa," Ecological Modelling, Elsevier, vol. 320(C), pages 9-28.
    8. Gustavo Larrea‐Gallegos & Ian Vázquez‐Rowe, 2022. "Exploring machine learning techniques to predict deforestation to enhance the decision‐making of road construction projects," Journal of Industrial Ecology, Yale University, vol. 26(1), pages 225-239, February.
    9. Sutton, G.F. & Martin, G.D., 2022. "Testing MaxEnt model performance in a novel geographic region using an intentionally introduced insect," Ecological Modelling, Elsevier, vol. 473(C).
    10. Fernandez, Marc & Sillero, Neftali & Yesson, Chris, 2022. "To be or not to be: the role of absences in niche modelling for highly mobile species in dynamic marine environments," Ecological Modelling, Elsevier, vol. 471(C).
    11. Ingrid Lana Lima de Morais & Alexandra Amaro de Lima & Ivinne Nara Lobato dos Santos & Carlos Meneses & Rogério Freire da Silva & Ricardo Lopes & Santiago Linorio Ferreyra Ramos & Ananda Virginia de A, 2024. "Climate Change Impact on the Distribution of Forest Species in the Brazilian Amazon," Sustainability, MDPI, vol. 16(8), pages 1-15, April.
    12. Cesar A Marchioro, 2016. "Global Potential Distribution of Bactrocera carambolae and the Risks for Fruit Production in Brazil," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-16, November.
    13. David Mouillot & Laure Velez & Camille Albouy & Nicolas Casajus & Joachim Claudet & Vincent Delbar & Rodolphe Devillers & Tom B. Letessier & Nicolas Loiseau & Stéphanie Manel & Laura Mannocci & Jessic, 2024. "The socioeconomic and environmental niche of protected areas reveals global conservation gaps and opportunities," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    14. An T. N. Dang & Lalit Kumar & Michael Reid, 2020. "Modelling the Potential Impacts of Climate Change on Rice Cultivation in Mekong Delta, Vietnam," Sustainability, MDPI, vol. 12(22), pages 1-21, November.
    15. Hanna Meyer & Edzer Pebesma, 2022. "Machine learning-based global maps of ecological variables and the challenge of assessing them," Nature Communications, Nature, vol. 13(1), pages 1-4, December.
    16. Boris Kryštufek & Tanya Zorenko & Vasileios Bontzorlos & Ahmad Mahmoudi & Nasko Atanasov & Danijel Ivajnšič, 2018. "Incipient road to extinction of a keystone herbivore in south-eastern Europe: Harting’s vole (Microtus hartingi) under climate change," Climatic Change, Springer, vol. 149(3), pages 443-456, August.
    17. Temesgen Alemayehu Abera & Janne Heiskanen & Eduardo Eiji Maeda & Mohammed Ahmed Muhammed & Netra Bhandari & Ville Vakkari & Binyam Tesfaw Hailu & Petri K. E. Pellikka & Andreas Hemp & Pieter G. Zyl &, 2024. "Deforestation amplifies climate change effects on warming and cloud level rise in African montane forests," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    18. Diana Koldasbayeva & Polina Tregubova & Mikhail Gasanov & Alexey Zaytsev & Anna Petrovskaia & Evgeny Burnaev, 2024. "Challenges in data-driven geospatial modeling for environmental research and practice," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    19. Benjamin Herfort & Sven Lautenbach & João Porto de Albuquerque & Jennings Anderson & Alexander Zipf, 2023. "A spatio-temporal analysis investigating completeness and inequalities of global urban building data in OpenStreetMap," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    20. Francesco Maria Sabatini & Borja Jiménez-Alfaro & Ute Jandt & Milan Chytrý & Richard Field & Michael Kessler & Jonathan Lenoir & Franziska Schrodt & Susan K. Wiser & Mohammed A. S. Arfin Khan & Fabio , 2022. "Global patterns of vascular plant alpha diversity," Nature Communications, Nature, vol. 13(1), pages 1-16, December.

    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:eee:ecomod:v:475:y:2023:i:c:s030438002200271x. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/ecological-modelling .

    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.