IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0135209.html
   My bibliography  Save this article

Assessing the Risk of Invasion by Tephritid Fruit Flies: Intraspecific Divergence Matters

Author

Listed:
  • Martin Godefroid
  • Astrid Cruaud
  • Jean-Pierre Rossi
  • Jean-Yves Rasplus

Abstract

Widely distributed species often show strong phylogeographic structure, with lineages potentially adapted to different biotic and abiotic conditions. The success of an invasion process may thus depend on the intraspecific identity of the introduced propagules. However, pest risk analyses are usually performed without accounting for intraspecific diversity. In this study, we developed bioclimatic models using MaxEnt and boosted regression trees approaches, to predict the potential distribution in Europe of six economically important Tephritid pests (Ceratitis fasciventris (Bezzi), Bactrocera oleae (Rossi), Anastrepha obliqua (Macquart), Anastrepha fraterculus (Wiedemann), Rhagoletis pomonella (Walsh) and Bactrocera cucurbitae (Coquillet)). We considered intraspecific diversity in our risk analyses by independently modeling the distributions of conspecific lineages. The six species displayed different potential distributions in Europe. A strong signal of intraspecific climate envelope divergence was observed in most species. In some cases, conspecific lineages differed strongly in potential distributions suggesting that taxonomic resolution should be accounted for in pest risk analyses. No models (lineage- and species-based approaches) predicted high climatic suitability in the entire invaded range of B. oleae—the only species whose intraspecific identity of invading populations has been elucidated—in California. Host availability appears to play the most important role in shaping the geographic range of this specialist pest. However, climatic suitability values predicted by species-based models are correlated with population densities of B. oleae globally reported in California. Our study highlights how classical taxonomic boundaries may lead to under- or overestimation of the potential pest distributions and encourages accounting for intraspecific diversity when assessing the risk of biological invasion.

Suggested Citation

  • Martin Godefroid & Astrid Cruaud & Jean-Pierre Rossi & Jean-Yves Rasplus, 2015. "Assessing the Risk of Invasion by Tephritid Fruit Flies: Intraspecific Divergence Matters," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-19, August.
  • Handle: RePEc:plo:pone00:0135209
    DOI: 10.1371/journal.pone.0135209
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0135209
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0135209&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0135209?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. Barve, Narayani & Barve, Vijay & Jiménez-Valverde, Alberto & Lira-Noriega, Andrés & Maher, Sean P. & Peterson, A. Townsend & Soberón, Jorge & Villalobos, Fabricio, 2011. "The crucial role of the accessible area in ecological niche modeling and species distribution modeling," Ecological Modelling, Elsevier, vol. 222(11), pages 1810-1819.
    2. 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. Billy Joel M Almarinez & Thaddeus M Carvajal & Kozo Watanabe & Mary Angelique A Tavera & Divina M Amalin & Billy Joel M Almarinez & Thaddeus M Carvajal & Kozo Watanabe & Mary Angelique A Tavera & Mary, 2020. "A Bioclimate-Based Maximum Entropy Model for Comperiella calauanica Barrion, Almarinez & Amalin (Hymenoptera: Encyrtidae), Parasitoid of Aspidiotus rigidus Reyne, in the Philippines," Current Investigations in Agriculture and Current Research, Lupine Publishers, LLC, vol. 8(4), pages 1122-1131, June.

    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. 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).
    2. 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).
    3. 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.
    4. 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.
    5. 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).
    6. 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.
    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. 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).
    9. Pelayo Acevedo & Alberto Jiménez-Valverde & Jorge M. Lobo & Raimundo Real, 2017. "Predictor weighting and geographical background delimitation: two synergetic sources of uncertainty when assessing species sensitivity to climate change," Climatic Change, Springer, vol. 145(1), pages 131-143, November.
    10. Watling, James I. & Brandt, Laura A. & Bucklin, David N. & Fujisaki, Ikuko & Mazzotti, Frank J. & Romañach, Stephanie S. & Speroterra, Carolina, 2015. "Performance metrics and variance partitioning reveal sources of uncertainty in species distribution models," Ecological Modelling, Elsevier, vol. 309, pages 48-59.
    11. Jarnevich, Catherine S. & Talbert, Marian & Morisette, Jeffery & Aldridge, Cameron & Brown, Cynthia S. & Kumar, Sunil & Manier, Daniel & Talbert, Colin & Holcombe, Tracy, 2017. "Minimizing effects of methodological decisions on interpretation and prediction in species distribution studies: An example with background selection," Ecological Modelling, Elsevier, vol. 363(C), pages 48-56.
    12. Boria, Robert A. & Blois, Jessica L., 2018. "The effect of large sample sizes on ecological niche models: Analysis using a North American rodent, Peromyscus maniculatus," Ecological Modelling, Elsevier, vol. 386(C), pages 83-88.
    13. Wolke Tobón-Niedfeldt & Alicia Mastretta-Yanes & Tania Urquiza-Haas & Bárbara Goettsch & Angela P. Cuervo-Robayo & Esmeralda Urquiza-Haas & M. Andrea Orjuela-R & Francisca Acevedo Gasman & Oswaldo Oli, 2022. "Incorporating evolutionary and threat processes into crop wild relatives conservation," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
    14. 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.
    15. 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).
    16. 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.
    17. Barker, Justin R. & MacIsaac, Hugh J., 2022. "Species distribution models: Administrative boundary centroid occurrences require careful interpretation," Ecological Modelling, Elsevier, vol. 472(C).
    18. 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).
    19. 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.
    20. Ochoa-Ochoa, Leticia M. & Flores-Villela, Oscar A. & Bezaury-Creel, Juan E., 2016. "Using one vs. many, sensitivity and uncertainty analyses of species distribution models with focus on conservation area networks," Ecological Modelling, Elsevier, vol. 320(C), pages 372-382.

    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:plo:pone00:0135209. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.