IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0163441.html
   My bibliography  Save this article

Spatial Models of Abundance and Habitat Preferences of Commerson’s and Peale’s Dolphin in Southern Patagonian Waters

Author

Listed:
  • Natalia A Dellabianca
  • Graham J Pierce
  • Andrea Raya Rey
  • Gabriela Scioscia
  • David L Miller
  • Mónica A Torres
  • M Natalia Paso Viola
  • R Natalie P Goodall
  • Adrián C M Schiavini

Abstract

Commerson’s dolphins (Cephalorhynchus c. commersonii) and Peale’s dolphins (Lagenorhynchus australis) are two of the most common species of cetaceans in the coastal waters of southwest South Atlantic Ocean. Both species are listed as Data Deficient by the IUCN, mainly due to the lack of information about population sizes and trends. The goal of this study was to build spatially explicit models for the abundance of both species in relation to environmental variables using data collected during eight scientific cruises along the Patagonian shelf. Spatial models were constructed using generalized additive models. In total, 88 schools (212 individuals) of Commerson’s dolphin and 134 schools (465 individuals) of Peale’s dolphin were recorded in 8,535 km surveyed. Commerson’s dolphin was found less than 60 km from shore; whereas Peale’s dolphins occurred over a wider range of distances from the coast, the number of animals sighted usually being larger near or far from the coast. Fitted models indicate overall abundances of approximately 22,000 Commerson’s dolphins and 20,000 Peale’s dolphins in the total area studied. This work provides the first large-scale abundance estimate for Peale’s dolphin in the Atlantic Ocean and an update of population size for Commerson’s dolphin. Additionally, our results contribute to baseline data on suitable habitat conditions for both species in southern Patagonia, which is essential for the implementation of adequate conservation measures.

Suggested Citation

  • Natalia A Dellabianca & Graham J Pierce & Andrea Raya Rey & Gabriela Scioscia & David L Miller & Mónica A Torres & M Natalia Paso Viola & R Natalie P Goodall & Adrián C M Schiavini, 2016. "Spatial Models of Abundance and Habitat Preferences of Commerson’s and Peale’s Dolphin in Southern Patagonian Waters," PLOS ONE, Public Library of Science, vol. 11(10), pages 1-17, October.
  • Handle: RePEc:plo:pone00:0163441
    DOI: 10.1371/journal.pone.0163441
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0163441
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0163441&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0163441?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. Marra, Giampiero & Wood, Simon N., 2011. "Practical variable selection for generalized additive models," Computational Statistics & Data Analysis, Elsevier, vol. 55(7), pages 2372-2387, July.
    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. Zak-Szatkowska, Malgorzata & Bogdan, Malgorzata, 2011. "Modified versions of the Bayesian Information Criterion for sparse Generalized Linear Models," Computational Statistics & Data Analysis, Elsevier, vol. 55(11), pages 2908-2924, November.
    2. Christina Kassara & Christos Barboutis & Anastasios Bounas, 2025. "Favorable stopover sites and fuel load dynamics of spring bird migrants under a changing climate," Climatic Change, Springer, vol. 178(1), pages 1-19, January.
    3. Simon N. Wood & Natalya Pya & Benjamin Säfken, 2016. "Smoothing Parameter and Model Selection for General Smooth Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1548-1563, October.
    4. Finlay, Jessica & Esposito, Michael & Langa, Kenneth M. & Judd, Suzanne & Clarke, Philippa, 2022. "Cognability: An Ecological Theory of neighborhoods and cognitive aging," Social Science & Medicine, Elsevier, vol. 309(C).
    5. Jacqueline D. Seufert & Andre Python & Christoph Weisser & Elías Cisneros & Krisztina Kis‐Katos & Thomas Kneib, 2022. "Mapping ex ante risks of COVID‐19 in Indonesia using a Bayesian geostatistical model on airport network data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 2121-2155, October.
    6. Juan Armando Torres Munguía, 2018. "What is behind homicide gender gaps in Mexico? A spatial semiparametric approach," Ibero America Institute for Econ. Research (IAI) Discussion Papers 236, Ibero-America Institute for Economic Research.
    7. Mhalla, Linda & Chavez-Demoulin, Valérie & Naveau, Philippe, 2017. "Non-linear models for extremal dependence," Journal of Multivariate Analysis, Elsevier, vol. 159(C), pages 49-66.
    8. Alexander Kreuzer & Luciana Dalla Valle & Claudia Czado, 2022. "A Bayesian non‐linear state space copula model for air pollution in Beijing," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(3), pages 613-638, June.
    9. Gressani, Oswaldo & Lambert, Philippe, 2021. "Laplace approximations for fast Bayesian inference in generalized additive models based on P-splines," Computational Statistics & Data Analysis, Elsevier, vol. 154(C).
    10. Guo, Jie & Tang, Manlai & Tian, Maozai & Zhu, Kai, 2013. "Variable selection in high-dimensional partially linear additive models for composite quantile regression," Computational Statistics & Data Analysis, Elsevier, vol. 65(C), pages 56-67.
    11. Thi Huong Trinh & Christine Thomas-Agnan & Michel Simioni, 2016. "Calorie intake and income in China: new evidence using semiparametric modelling with generalized additive models," Post-Print hal-01515007, HAL.
    12. Théo Michelot & Richard Glennie & Catriona Harris & Len Thomas, 2021. "Varying-Coefficient Stochastic Differential Equations with Applications in Ecology," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(3), pages 446-463, September.
    13. Liu, Moyang & Hamilton, Serena H. & Jakeman, Anthony J. & Lerat, Julien & Savage, Callum & Croke, Barry F.W., 2024. "Assessing the contribution of hydrologic and climatic factors on vegetation condition changes in semi-arid wetlands: An analysis for the Narran Lakes," Ecological Modelling, Elsevier, vol. 487(C).
    14. Yang Zhang & Samsung Lim & Jason John Sharples, 2017. "Wildfire occurrence patterns in ecoregions of New South Wales and Australian Capital Territory, Australia," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 87(1), pages 415-435, May.
    15. Fabian Scheipl & Thomas Kneib & Ludwig Fahrmeir, 2013. "Penalized likelihood and Bayesian function selection in regression models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 97(4), pages 349-385, October.
    16. Jing Wei & Laurent Fontaine & Nicolas Valiente & Peter Dörsch & Dag O. Hessen & Alexander Eiler, 2023. "Trajectories of freshwater microbial genomics and greenhouse gas saturation upon glacial retreat," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    17. Jonathan Fuhr & Philipp Berens & Dominik Papies, 2024. "Estimating Causal Effects with Double Machine Learning -- A Method Evaluation," Papers 2403.14385, arXiv.org, revised Apr 2024.
    18. David C. Broadstock & Shunsuke Managi & Roman Matousek & Nickolaos G. Tzeremes, 2019. "Does doing “good” always translate into doing “well”? An eco‐efficiency perspective," Business Strategy and the Environment, Wiley Blackwell, vol. 28(6), pages 1199-1217, September.
    19. Roland Langrock & Timo Adam & Vianey Leos‐Barajas & Sina Mews & David L. Miller & Yannis P. Papastamatiou, 2018. "Spline‐based nonparametric inference in general state‐switching models," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 72(3), pages 179-200, August.
    20. Antonio Musolesi & Hervé Cardot, 2017. "Modeling temporal treatment effects with zero inflated semi-parametric regression models: the case of local development policies in France," Working Papers 2017036, University of Ferrara, Department of Economics.

    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:pone00:0163441. 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.