IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v217y2008i1p79-86.html
   My bibliography  Save this article

Classification of aquatic bioregions through the use of distributional modelling of freshwater fish

Author

Listed:
  • Growns, Ivor
  • West, Greg

Abstract

Bioregional classifications are used to manage natural resources including the conservation of biota. There are a variety of ways to define bioregions and mostly a combination of data analysis and subjective expert judgement is used, mainly because data on the distributions of biota are sparse or uneven. We trialled a method of using distributional modelling of individual freshwater fish species to produce a classification of rivers in New South Wales, Australia. Distributional modelling was done for 44 fish taxa using the genetic algorithm for rule set production (GARP) and a classification was done using non-hierarchical clustering. The data used was a combination of museum records (presence only records) and data from designed surveys. The natural distributions of seven fish species could not be modelled due to insufficient records. The models for the majority of remaining species displayed substantial to almost perfect model accuracy. The classification produced similar bioregions as had been previously defined for freshwater fish in New South Wales. Our study demonstrates that distributional modelling of individual species is a feasible and practical approach to defining regions using data derived from a variety of sources. The potential benefits of the method would be that a description of the potential “natural” fish assemblage could be described for any given site, separation between zones can be clearly delineated and it is independent of the actual fish sampling locations.

Suggested Citation

  • Growns, Ivor & West, Greg, 2008. "Classification of aquatic bioregions through the use of distributional modelling of freshwater fish," Ecological Modelling, Elsevier, vol. 217(1), pages 79-86.
    • Handle: RePEc:eee:ecomod:v:217:y:2008:i:1:p:79-86
    DOI: 10.1016/j.ecolmodel.2008.06.009
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380008002883
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ecolmodel.2008.06.009?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. A. Townsend Peterson & Miguel A. Ortega-Huerta & Jeremy Bartley & Victor Sánchez-Cordero & Jorge Soberón & Robert H. Buddemeier & David R. B. Stockwell, 2002. "Future projections for Mexican faunas under global climate change scenarios," Nature, Nature, vol. 416(6881), pages 626-629, April.
    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. 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.

    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. Olivier, Frédérique & Wotherspoon, Simon J., 2008. "Nest selection by snow petrels Pagodroma nivea in East Antarctica," Ecological Modelling, Elsevier, vol. 210(4), pages 414-430.
    2. Yañez-Arenas, Carlos & Guevara, Roger & Martínez-Meyer, Enrique & Mandujano, Salvador & Lobo, Jorge M., 2014. "Predicting species’ abundances from occurrence data: Effects of sample size and bias," Ecological Modelling, Elsevier, vol. 294(C), pages 36-41.
    3. Marco-Fondevila, Miguel & Álvarez-Etxeberría, Igor, 2023. "Trends in private sector engagement with biodiversity: EU listed companies' disclosure and indicators," Ecological Economics, Elsevier, vol. 210(C).
    4. Stankowski, Philippe A. & Parker, William H., 2011. "Future distribution modelling: A stitch in time is not enough," Ecological Modelling, Elsevier, vol. 222(3), pages 567-572.
    5. Mahya Norallahi & Hesam Seyed Kaboli, 2021. "Urban flood hazard mapping using machine learning models: GARP, RF, MaxEnt and NB," 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. 106(1), pages 119-137, March.
    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. 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.
    8. Weiyu Yu & Nicola A Wardrop & Robert E S Bain & Victor Alegana & Laura J Graham & Jim A Wright, 2019. "Mapping access to domestic water supplies from incomplete data in developing countries: An illustrative assessment for Kenya," PLOS ONE, Public Library of Science, vol. 14(5), pages 1-19, May.
    9. Xiaojun Lyu & Haiqian Ke, 2022. "Dynamic Threshold Effect of Directed Technical Change Suppress on Urban Carbon Footprint in China," IJERPH, MDPI, vol. 19(9), pages 1-15, April.
    10. Acevedo, Pelayo & González-Quirós, Pablo & Prieto, José M. & Etherington, Thomas R. & Gortázar, Christian & Balseiro, Ana, 2014. "Generalizing and transferring spatial models: A case study to predict Eurasian badger abundance in Atlantic Spain," Ecological Modelling, Elsevier, vol. 275(C), pages 1-8.
    11. Buse, Jörn & Griebeler, Eva Maria, 2011. "Incorporating classified dispersal assumptions in predictive distribution models – A case study with grasshoppers and bush-crickets," Ecological Modelling, Elsevier, vol. 222(13), pages 2130-2141.
    12. Horemans, Dante M.L. & Friedrichs, Marjorie A.M. & St-Laurent, Pierre & Hood, Raleigh R. & Brown, Christopher W., 2024. "Evaluating the skill of correlative species distribution models trained with mechanistic model output," Ecological Modelling, Elsevier, vol. 491(C).
    13. Matt J. Michel & Huicheng Chien & Collin E. Beachum & Micah G. Bennett & Jason H. Knouft, 2017. "Climate change, hydrology, and fish morphology: predictions using phenotype-environment associations," Climatic Change, Springer, vol. 140(3), pages 563-576, February.
    14. Edgard David Mason-Romo & Ariel A Farías & Gerardo Ceballos, 2017. "Two decades of climate driving the dynamics of functional and taxonomic diversity of a tropical small mammal community in western Mexico," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-25, December.
    15. Soto-Montes-de-Oca, Gloria & Bark, Rosalind & González-Arellano, Salomón, 2020. "Incorporating the insurance value of peri-urban ecosystem services into natural hazard policies and insurance products: Insights from Mexico," Ecological Economics, Elsevier, vol. 169(C).
    16. Gobeyn, Sacha & Mouton, Ans M. & Cord, Anna F. & Kaim, Andrea & Volk, Martin & Goethals, Peter L.M., 2019. "Evolutionary algorithms for species distribution modelling: A review in the context of machine learning," Ecological Modelling, Elsevier, vol. 392(C), pages 179-195.
    17. Coppée, Thomas & Paquet, Jean-Yves & Titeux, Nicolas & Dufrêne, Marc, 2022. "Temporal transferability of species abundance models to study the changes of breeding bird species based on land cover changes," Ecological Modelling, Elsevier, vol. 473(C).
    18. Iker Pardo & María P Pata & Daniel Gómez & María B García, 2013. "A Novel Method to Handle the Effect of Uneven Sampling Effort in Biodiversity Databases," PLOS ONE, Public Library of Science, vol. 8(1), pages 1-9, January.
    19. Nenzén, H.K. & Araújo, M.B., 2011. "Choice of threshold alters projections of species range shifts under climate change," Ecological Modelling, Elsevier, vol. 222(18), pages 3346-3354.
    20. repec:plo:pone00:0221934 is not listed on IDEAS
    21. Cuevas-Carvajal, N. & Cortes-Ramirez, J.S. & Norato, Julian A. & Hernandez, C. & Montoya-Vallejo, M.F., 2022. "Effect of geometrical parameters on the performance of conventional Savonius VAWT: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).

    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:217:y:2008:i:1:p:79-86. 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.