IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v431y2020ics0304380020302659.html
   My bibliography  Save this article

Presence-only species distribution models are sensitive to sample prevalence: Evaluating models using spatial prediction stability and accuracy metrics

Author

Listed:
  • Grimmett, Liam
  • Whitsed, Rachel
  • Horta, Ana

Abstract

Species distribution modelling (SDM) is an important tool for ecologists, but different algorithms and different sampling strategies produce different results. Using virtual species with differing characteristics, this study investigated the effect of sampling strategy choices on SDM predictions across multiple algorithms and species, including the impacts of different sample size and prevalence choices, and the effects of validating models using presence and background data as opposed to true absences. We also assessed the consistency of predictions between algorithms, and investigated the effectiveness of using stability assessment of spatial predictions in geographic space to evaluate SDM predictions. Maxent performed most consistently under all scenarios both in regards to performance metrics and spatial prediction stability, and should be considered for most scenarios either on its own or as part of a model ensemble, in particular when true absences are not available. A key recommendation of this study is the use of metrics to assess agreement between replicate predictions as a measure of spatial stability, rather than relying solely on performance metrics such as area under the curve (AUC).

Suggested Citation

  • Grimmett, Liam & Whitsed, Rachel & Horta, Ana, 2020. "Presence-only species distribution models are sensitive to sample prevalence: Evaluating models using spatial prediction stability and accuracy metrics," Ecological Modelling, Elsevier, vol. 431(C).
    • Handle: RePEc:eee:ecomod:v:431:y:2020:i:c:s0304380020302659
    DOI: 10.1016/j.ecolmodel.2020.109194
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380020302659
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ecolmodel.2020.109194?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. Giovanelli, João G.R. & de Siqueira, Marinez Ferreira & Haddad, Célio F.B. & Alexandrino, João, 2010. "Modeling a spatially restricted distribution in the Neotropics: How the size of calibration area affects the performance of five presence-only methods," Ecological Modelling, Elsevier, vol. 221(2), pages 215-224.
    2. Iturbide, Maialen & Bedia, Joaquín & Herrera, Sixto & del Hierro, Oscar & Pinto, Miriam & Gutiérrez, Jose Manuel, 2015. "A framework for species distribution modelling with improved pseudo-absence generation," Ecological Modelling, Elsevier, vol. 312(C), pages 166-174.
    3. Jesús Aguirre-Gutiérrez & Luísa G Carvalheiro & Chiara Polce & E Emiel van Loon & Niels Raes & Menno Reemer & Jacobus C Biesmeijer, 2013. "Fit-for-Purpose: Species Distribution Model Performance Depends on Evaluation Criteria – Dutch Hoverflies as a Case Study," PLOS ONE, Public Library of Science, vol. 8(5), pages 1-11, May.
    4. Vincenzi, Simone & Zucchetta, Matteo & Franzoi, Piero & Pellizzato, Michele & Pranovi, Fabio & De Leo, Giulio A. & Torricelli, Patrizia, 2011. "Application of a Random Forest algorithm to predict spatial distribution of the potential yield of Ruditapes philippinarum in the Venice lagoon, Italy," Ecological Modelling, Elsevier, vol. 222(8), pages 1471-1478.
    5. VanDerWal, Jeremy & Shoo, Luke P. & Graham, Catherine & Williams, Stephen E., 2009. "Selecting pseudo-absence data for presence-only distribution modeling: How far should you stray from what you know?," Ecological Modelling, Elsevier, vol. 220(4), pages 589-594.
    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. Yeeun Shin & Eunseo Shin & Sang-Woo Lee & Kyungjin An, 2024. "Predicting Changes in and Future Distributions of Plant Habitats of Climate-Sensitive Biological Indicator Species in South Korea," Sustainability, MDPI, vol. 16(3), pages 1-17, January.
    2. Barker, Justin R. & MacIsaac, Hugh J., 2022. "Species distribution models: Administrative boundary centroid occurrences require careful interpretation," Ecological Modelling, Elsevier, vol. 472(C).
    3. Marchetto, Elisa & Da Re, Daniele & Tordoni, Enrico & Bazzichetto, Manuele & Zannini, Piero & Celebrin, Simone & Chieffallo, Ludovico & Malavasi, Marco & Rocchini, Duccio, 2023. "Testing the effect of sample prevalence and sampling methods on probability- and favourability-based SDMs," Ecological Modelling, Elsevier, vol. 477(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 & 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).
    2. Azuaje-Rodríguez, Roxiris A. & Silva, Sofia Marques & Carlos, Caio J., 2022. "Not going with the flow: Ecological niche of a migratory seabird, the South American Tern Sterna hirundinacea," Ecological Modelling, Elsevier, vol. 463(C).
    3. Wiltshire, Kathryn H & Tanner, Jason E, 2020. "Comparing maximum entropy modelling methods to inform aquaculture site selection for novel seaweed species," Ecological Modelling, Elsevier, vol. 429(C).
    4. Somaye Vaissi, 2021. "Design of Protected Area by Tracking and Excluding the Effects of Climate and Landscape Change: A Case Study Using Neurergus derjugini," Sustainability, MDPI, vol. 13(10), pages 1-20, May.
    5. Alsamadisi, Adam G. & Tran, Liem T. & Papeş, Monica, 2020. "Employing inferences across scales: Integrating spatial data with different resolutions to enhance Maxent models," Ecological Modelling, Elsevier, vol. 415(C).
    6. Leandro, Camila & Jay-Robert, Pierre & Mériguet, Bruno & Houard, Xavier & Renner, Ian W., 2020. "Is my sdm good enough? insights from a citizen science dataset in a point process modeling framework," Ecological Modelling, Elsevier, vol. 438(C).
    7. Liang, Wanwan & Papeş, Monica & Tran, Liem & Grant, Jerome & Washington-Allen, Robert & Stewart, Scott & Wiggins, Gregory, 2018. "The effect of pseudo-absence selection method on transferability of species distribution models in the context of non-adaptive niche shift," Ecological Modelling, Elsevier, vol. 388(C), pages 1-9.
    8. 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).
    9. Christian König & Patrick Weigelt & Julian Schrader & Amanda Taylor & Jens Kattge & Holger Kreft, 2019. "Biodiversity data integration—the significance of data resolution and domain," PLOS Biology, Public Library of Science, vol. 17(3), pages 1-16, March.
    10. Barker, Justin R. & MacIsaac, Hugh J., 2022. "Species distribution models: Administrative boundary centroid occurrences require careful interpretation," Ecological Modelling, Elsevier, vol. 472(C).
    11. Marchetto, Elisa & Da Re, Daniele & Tordoni, Enrico & Bazzichetto, Manuele & Zannini, Piero & Celebrin, Simone & Chieffallo, Ludovico & Malavasi, Marco & Rocchini, Duccio, 2023. "Testing the effect of sample prevalence and sampling methods on probability- and favourability-based SDMs," Ecological Modelling, Elsevier, vol. 477(C).
    12. Zeng, Yiwen & Low, Bi Wei & Yeo, Darren C.J., 2016. "Novel methods to select environmental variables in MaxEnt: A case study using invasive crayfish," Ecological Modelling, Elsevier, vol. 341(C), pages 5-13.
    13. Coro, Gianpaolo & Pagano, Pasquale & Ellenbroek, Anton, 2013. "Combining simulated expert knowledge with Neural Networks to produce Ecological Niche Models for Latimeria chalumnae," Ecological Modelling, Elsevier, vol. 268(C), pages 55-63.
    14. Dandan Zhao & Hong S. He & Wen J. Wang & Lei Wang & Haibo Du & Kai Liu & Shengwei Zong, 2018. "Predicting Wetland Distribution Changes under Climate Change and Human Activities in a Mid- and High-Latitude Region," Sustainability, MDPI, vol. 10(3), pages 1-14, March.
    15. Saupe, E.E. & Barve, V. & Myers, C.E. & Soberón, J. & Barve, N. & Hensz, C.M. & Peterson, A.T. & Owens, H.L. & Lira-Noriega, A., 2012. "Variation in niche and distribution model performance: The need for a priori assessment of key causal factors," Ecological Modelling, Elsevier, vol. 237, pages 11-22.
    16. Halvorsen, Rune & Mazzoni, Sabrina & Dirksen, John Wirkola & Næsset, Erik & Gobakken, Terje & Ohlson, Mikael, 2016. "How important are choice of model selection method and spatial autocorrelation of presence data for distribution modelling by MaxEnt?," Ecological Modelling, Elsevier, vol. 328(C), pages 108-118.
    17. Jiménez, Laura & Soberón, Jorge & Christen, J. Andrés & Soto, Desireé, 2019. "On the problem of modeling a fundamental niche from occurrence data," Ecological Modelling, Elsevier, vol. 397(C), pages 74-83.
    18. 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.
    19. Jig Han Jeong & Jonathan P Resop & Nathaniel D Mueller & David H Fleisher & Kyungdahm Yun & Ethan E Butler & Dennis J Timlin & Kyo-Moon Shim & James S Gerber & Vangimalla R Reddy & Soo-Hyung Kim, 2016. "Random Forests for Global and Regional Crop Yield Predictions," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-15, June.
    20. Reside, April E. & Watson, Ian & VanDerWal, Jeremy & Kutt, Alex S., 2011. "Incorporating low-resolution historic species location data decreases performance of distribution models," Ecological Modelling, Elsevier, vol. 222(18), pages 3444-3448.

    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:431:y:2020:i:c:s0304380020302659. 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.