IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v471y2022ics0304380022001508.html
   My bibliography  Save this article

To be or not to be: the role of absences in niche modelling for highly mobile species in dynamic marine environments

Author

Listed:
  • Fernandez, Marc
  • Sillero, Neftali
  • Yesson, Chris

Abstract

Species distribution models are valuable tools for conservation management. However, there remain challenges in developing and interpreting these models in the marine environment, such as the nature of the species used for the modelling process. When working with mobile species in dynamic environments, lack of observation is usually interpreted as an observation of absence, which can result in the introduction of biases by methodological (false) absences. Here, we explore the role of absences when modelling marine megafauna distributions. To better understand how the use of absences (or equivalent) affects the niche modelling algorithms, we used a set of 20 virtual species with different relations to the habitat (generalist static, specialist static, generalist dynamic and specialist dynamic) with different encounter rates. We tested six different modelling techniques divided into three distinct groups: presence-only, presence-background and presence-absence. We compared the outputs of the models using traditional validation metrics and overlap metrics in the geographical and environmental spaces. Algorithms characterized the ecological niche for the simulated species differently. Approaches using background data generally outperformed the other methods, suggesting that the non-observation of a species in a given location and time should not be considered as an absence. A very intense (practically unrealistic) sampling schema would be required to obtain a genuine unbiased absence when working with these species and habitats. For highly mobile species, a precautionary approach would be to consider the non-observation of a species as part of the background (a sample of the conditions available in the study area) rather than an absence. A good starting point would be to use presence-background models, complemented with presence-absence and/or presence-only models, comparing outputs from the different algorithms tested in the geographic and environmental space. Improving model performance for highly mobile marine species should lead to better-informed decision making for conservation.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:ecomod:v:471:y:2022:i:c:s0304380022001508
    DOI: 10.1016/j.ecolmodel.2022.110040
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380022001508
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ecolmodel.2022.110040?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. Virgili, Auriane & Racine, Mélanie & Authier, Matthieu & Monestiez, Pascal & Ridoux, Vincent, 2017. "Comparison of habitat models for scarcely detected species," Ecological Modelling, Elsevier, vol. 346(C), pages 88-98.
    3. Sillero, Neftalí & Arenas-Castro, Salvador & Enriquez‐Urzelai, Urtzi & Vale, Cândida Gomes & Sousa-Guedes, Diana & Martínez-Freiría, Fernando & Real, Raimundo & Barbosa, A.Márcia, 2021. "Want to model a species niche? A step-by-step guideline on correlative ecological niche modelling," Ecological Modelling, Elsevier, vol. 456(C).
    4. B. A. Block & I. D. Jonsen & S. J. Jorgensen & A. J. Winship & S. A. Shaffer & S. J. Bograd & E. L. Hazen & D. G. Foley & G. A. Breed & A.-L. Harrison & J. E. Ganong & A. Swithenbank & M. Castleton & , 2011. "Tracking apex marine predator movements in a dynamic ocean," Nature, Nature, vol. 475(7354), pages 86-90, July.
    5. Simon N. Wood, 2011. "Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(1), pages 3-36, January.
    6. Sillero, Neftalí, 2011. "What does ecological modelling model? A proposed classification of ecological niche models based on their underlying methods," Ecological Modelling, Elsevier, vol. 222(8), pages 1343-1346.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Amaro, George & Fidelis, Elisangela Gomes & da Silva, Ricardo Siqueira & Marchioro, Cesar Augusto, 2023. "Effect of study area extent on the potential distribution of Species: A case study with models for Raoiella indica Hirst (Acari: Tenuipalpidae)," Ecological Modelling, Elsevier, vol. 483(C).

    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. Sillero, Neftalí & Campos, João Carlos & Arenas-Castro, Salvador & Barbosa, A.Márcia, 2023. "A curated list of R packages for ecological niche modelling," Ecological Modelling, Elsevier, vol. 476(C).
    2. Sillero, Neftalí & Arenas-Castro, Salvador & Enriquez‐Urzelai, Urtzi & Vale, Cândida Gomes & Sousa-Guedes, Diana & Martínez-Freiría, Fernando & Real, Raimundo & Barbosa, A.Márcia, 2021. "Want to model a species niche? A step-by-step guideline on correlative ecological niche modelling," Ecological Modelling, Elsevier, vol. 456(C).
    3. Rutten, Anneleen & Casaer, Jim & Swinnen, Kristijn R.R. & Herremans, Marc & Leirs, Herwig, 2019. "Future distribution of wild boar in a highly anthropogenic landscape: Models combining hunting bag and citizen science data," Ecological Modelling, Elsevier, vol. 411(C).
    4. Gerhard Tutz & Moritz Berger, 2018. "Tree-structured modelling of categorical predictors in generalized additive regression," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(3), pages 737-758, September.
    5. Tommaso Luzzati & Angela Parenti & Tommaso Rughi, 2017. "Spatial error regressions for testing the Cancer-EKC," Discussion Papers 2017/218, Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy.
    6. Davide Fiaschi & Andrea Mario Lavezzi & Angela Parenti, 2020. "Deep and Proximate Determinants of the World Income Distribution," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 66(3), pages 677-710, September.
    7. Conor Waldock & Bernhard Wegscheider & Dario Josi & Bárbara Borges Calegari & Jakob Brodersen & Luiz Jardim de Queiroz & Ole Seehausen, 2024. "Deconstructing the geography of human impacts on species’ natural distribution," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    8. B Eugene Smith & Mark K Johnston & Robert Lücking, 2016. "From GenBank to GBIF: Phylogeny-Based Predictive Niche Modeling Tests Accuracy of Taxonomic Identifications in Large Occurrence Data Repositories," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-15, March.
    9. Gengping Zhu & Matthew J Petersen & Wenjun Bu, 2012. "Selecting Biological Meaningful Environmental Dimensions of Low Discrepancy among Ranges to Predict Potential Distribution of Bean Plataspid Invasion," PLOS ONE, Public Library of Science, vol. 7(9), pages 1-9, September.
    10. Uzma Ashraf & Hassan Ali & Muhammad Nawaz Chaudry & Irfan Ashraf & Adila Batool & Zafeer Saqib, 2016. "Predicting the Potential Distribution of Olea ferruginea in Pakistan incorporating Climate Change by Using Maxent Model," Sustainability, MDPI, vol. 8(8), pages 1-11, July.
    11. Crow White & Christopher Costello, 2014. "Close the High Seas to Fishing?," PLOS Biology, Public Library of Science, vol. 12(3), pages 1-5, March.
    12. Longhi, Christian & Musolesi, Antonio & Baumont, Catherine, 2014. "Modeling structural change in the European metropolitan areas during the process of economic integration," Economic Modelling, Elsevier, vol. 37(C), pages 395-407.
    13. Sihvonen, Markus, 2021. "Yield curve momentum," Research Discussion Papers 15/2021, Bank of Finland.
    14. Roberto Basile & Luigi Benfratello & Davide Castellani, 2012. "Geoadditive models for regional count data: an application to industrial location," ERSA conference papers ersa12p83, European Regional Science Association.
    15. Dillon T. Fogarty & Caleb P. Roberts & Daniel R. Uden & Victoria M. Donovan & Craig R. Allen & David E. Naugle & Matthew O. Jones & Brady W. Allred & Dirac Twidwell, 2020. "Woody Plant Encroachment and the Sustainability of Priority Conservation Areas," Sustainability, MDPI, vol. 12(20), pages 1-15, October.
    16. 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.
    17. E. Zanini & E. Eastoe & M. J. Jones & D. Randell & P. Jonathan, 2020. "Flexible covariate representations for extremes," Environmetrics, John Wiley & Sons, Ltd., vol. 31(5), August.
    18. Daniel Melser & Robert J. Hill, 2019. "Residential Real Estate, Risk, Return and Diversification: Some Empirical Evidence," The Journal of Real Estate Finance and Economics, Springer, vol. 59(1), pages 111-146, July.
    19. Fourcade, Yoan, 2021. "Fine-tuning niche models matters in invasion ecology. A lesson from the land planarian Obama nungara," Ecological Modelling, Elsevier, vol. 457(C).
    20. 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.

    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:471:y:2022:i:c:s0304380022001508. 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.