IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v257y2013icp1-10.html
   My bibliography  Save this article

Choice of study area and predictors affect habitat suitability projections, but not the performance of species distribution models of stream biota

Author

Listed:
  • Domisch, Sami
  • Kuemmerlen, Mathias
  • Jähnig, Sonja C.
  • Haase, Peter

Abstract

Species distribution models (SDMs) that provide extrapolations of species habitat suitability are increasingly being used in stream ecosystems, however the effects of different modelling techniques on model projections remain unknown. We tested how different study areas and predictors affect SDMs by using consensus projections of a fixed set of 224 stream macroinvertebrate species and five algorithms implemented in BIOMOD/R. Four modelling designs were applied: (1) a landscape as a continuous study area without any discrimination between terrestrial and aquatic realms, (2) a stream network masked a posteriori from the previous design, (3) a stream network as the study area during the model-building stage, and (4) same as (3) but with a hydrologically corrected set of predictors. The true skill statistic (TSS) and accuracy of the consensus projections were not influenced by the different designs (TSS ranged from 0.80 to 1.00, accuracy ranged from 0.70 to 0.96). The projections of design (4) yielded a strong reduction in false positive predictions compared to (1) (on average by 56%), (2) (11%) or (3) (8%). Our results show how SDMs with equally high accuracy may differ widely in habitat suitability projections for benthic macroinvertebrates. As model performance and output are not necessarily congruent, habitat suitability projections of stream biota need to be carefully assessed.

Suggested Citation

  • Domisch, Sami & Kuemmerlen, Mathias & Jähnig, Sonja C. & Haase, Peter, 2013. "Choice of study area and predictors affect habitat suitability projections, but not the performance of species distribution models of stream biota," Ecological Modelling, Elsevier, vol. 257(C), pages 1-10.
    • Handle: RePEc:eee:ecomod:v:257:y:2013:i:c:p:1-10
    DOI: 10.1016/j.ecolmodel.2013.02.019
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380013001063
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ecolmodel.2013.02.019?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. Stokland, Jogeir N. & Halvorsen, Rune & Støa, Bente, 2011. "Species distribution modelling—Effect of design and sample size of pseudo-absence observations," Ecological Modelling, Elsevier, vol. 222(11), pages 1800-1809.
    2. Mouton, Ans M. & De Baets, Bernard & Goethals, Peter L.M., 2010. "Ecological relevance of performance criteria for species distribution models," Ecological Modelling, Elsevier, vol. 221(16), pages 1995-2002.
    3. 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. Pimenta, Mayra & Andrade, André Felipe Alves de & Fernandes, Fernando Hiago Souza & Amboni, Mayra Pereira de Melo & Almeida, Renata Silva & Soares, Ana Hermínia Simões de Bello & Falcon, Guth Berger &, 2022. "One size does not fit all: Priority areas for real world problems," Ecological Modelling, Elsevier, vol. 470(C).
    2. Bennetsen, Elina & Gobeyn, Sacha & Goethals, Peter L.M., 2016. "Species distribution models grounded in ecological theory for decision support in river management," Ecological Modelling, Elsevier, vol. 325(C), pages 1-12.
    3. 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.
    4. Iacopo Bernetti & Veronica Alampi Sottini & Lorenzo Bambi & Elena Barbierato & Tommaso Borghini & Irene Capecchi & Claudio Saragosa, 2020. "Urban Niche Assessment: An Approach Integrating Social Media Analysis, Spatial Urban Indicators and Geo-Statistical Techniques," Sustainability, MDPI, vol. 12(10), pages 1-26, May.
    5. Markovic, Danijela & Walz, Ariane & Kärcher, Oskar, 2019. "Scale effects on the performance of niche-based models of freshwater fish distributions: Local vs. upstream area influences," Ecological Modelling, Elsevier, vol. 411(C).
    6. Kärcher, Oskar & Frank, Karin & Walz, Ariane & Markovic, Danijela, 2019. "Scale effects on the performance of niche-based models of freshwater fish distributions," Ecological Modelling, Elsevier, vol. 405(C), pages 33-42.
    7. Zhiqiang Chen & Zhibiao Chen, 2018. "Effects of ecological restoration measures on the distribution of Dicranopteris dichotoma at the microscale in the red soil hilly region of China," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-17, October.
    8. Kuemmerlen, Mathias & Schmalz, Britta & Guse, Björn & Cai, Qinghua & Fohrer, Nicola & Jähnig, Sonja C., 2014. "Integrating catchment properties in small scale species distribution models of stream macroinvertebrates," Ecological Modelling, Elsevier, vol. 277(C), pages 77-86.
    9. Schuwirth, Nele & Borgwardt, Florian & Domisch, Sami & Friedrichs, Martin & Kattwinkel, Mira & Kneis, David & Kuemmerlen, Mathias & Langhans, Simone D. & Martínez-López, Javier & Vermeiren, Peter, 2019. "How to make ecological models useful for environmental management," Ecological Modelling, Elsevier, vol. 411(C).
    10. Wentao Yang & Huaxi He & Dongsheng Wei & Hao Chen, 2022. "Generating pseudo-absence samples of invasive species based on outlier detection in the geographical characteristic space," Journal of Geographical Systems, Springer, vol. 24(2), pages 261-279, April.
    11. Gama, M. & Crespo, D. & Dolbeth, M. & Anastácio, P., 2016. "Predicting global habitat suitability for Corbicula fluminea using species distribution models: The importance of different environmental datasets," Ecological Modelling, Elsevier, vol. 319(C), pages 163-169.
    12. Wentao Yang & Min Deng & Jianbo Tang & Liang Luo, 2023. "Geographically weighted regression with the integration of machine learning for spatial prediction," Journal of Geographical Systems, Springer, vol. 25(2), pages 213-236, April.

    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. 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.
    2. 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.
    3. Kuemmerlen, Mathias & Schmalz, Britta & Guse, Björn & Cai, Qinghua & Fohrer, Nicola & Jähnig, Sonja C., 2014. "Integrating catchment properties in small scale species distribution models of stream macroinvertebrates," Ecological Modelling, Elsevier, vol. 277(C), pages 77-86.
    4. 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.
    5. Tonnang, Henri E.Z. & Hervé, Bisseleua D.B. & Biber-Freudenberger, Lisa & Salifu, Daisy & Subramanian, Sevgan & Ngowi, Valentine B. & Guimapi, Ritter Y.A. & Anani, Bruce & Kakmeni, Francois M.M. & Aff, 2017. "Advances in crop insect modelling methods—Towards a whole system approach," Ecological Modelling, Elsevier, vol. 354(C), pages 88-103.
    6. 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).
    7. Rana, Divyashree & Sartor, Caroline Charão & Chiaverini, Luca & Cushman, Samuel Alan & Kaszta, Żaneta & Ramakrishnan, Uma & Macdonald, David W., 2024. "Differentially biased sampling strategies reveal the non-stationarity of species distribution models for Indian small felids," Ecological Modelling, Elsevier, vol. 493(C).
    8. 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.
    9. 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.
    10. 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.
    11. Sutton, G.F. & Martin, G.D., 2022. "Testing MaxEnt model performance in a novel geographic region using an intentionally introduced insect," Ecological Modelling, Elsevier, vol. 473(C).
    12. Marcot, Bruce G., 2012. "Metrics for evaluating performance and uncertainty of Bayesian network models," Ecological Modelling, Elsevier, vol. 230(C), pages 50-62.
    13. Xiaojiong Zhao & Jian Wang & Junde Su & Wei Sun & Haoxian Meng, 2021. "Research on a Biodiversity Conservation Value Assessment Method Based on Habitat Suitability of Species: A Case Study in Gansu Province, China," Sustainability, MDPI, vol. 13(6), pages 1-30, March.
    14. An Cao & Xueyi Shi, 2022. "The Effects of Climate Change on Habitat Connectivity: A Case Study of the Brown-Eared Pheasant in China," Land, MDPI, vol. 11(6), pages 1-17, May.
    15. Fukuda, Shinji & De Baets, Bernard & Mouton, Ans M. & Waegeman, Willem & Nakajima, Jun & Mukai, Takahiko & Hiramatsu, Kazuaki & Onikura, Norio, 2011. "Effect of model formulation on the optimization of a genetic Takagi–Sugeno fuzzy system for fish habitat suitability evaluation," Ecological Modelling, Elsevier, vol. 222(8), pages 1401-1413.
    16. 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).
    17. Christian Bunn & Peter Läderach & Oriana Ovalle Rivera & Dieter Kirschke, 2015. "A bitter cup: climate change profile of global production of Arabica and Robusta coffee," Climatic Change, Springer, vol. 129(1), pages 89-101, March.
    18. 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.
    19. Brice B Hanberry & Hong S He & Brian J Palik, 2012. "Pseudoabsence Generation Strategies for Species Distribution Models," PLOS ONE, Public Library of Science, vol. 7(8), pages 1-12, August.
    20. 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.

    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:257:y:2013:i:c:p:1-10. 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.