IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0063708.html
   My bibliography  Save this article

Fit-for-Purpose: Species Distribution Model Performance Depends on Evaluation Criteria – Dutch Hoverflies as a Case Study

Author

Listed:
  • Jesús Aguirre-Gutiérrez
  • Luísa G Carvalheiro
  • Chiara Polce
  • E Emiel van Loon
  • Niels Raes
  • Menno Reemer
  • Jacobus C Biesmeijer

Abstract

Understanding species distributions and the factors limiting them is an important topic in ecology and conservation, including in nature reserve selection and predicting climate change impacts. While Species Distribution Models (SDM) are the main tool used for these purposes, choosing the best SDM algorithm is not straightforward as these are plentiful and can be applied in many different ways. SDM are used mainly to gain insight in 1) overall species distributions, 2) their past-present-future probability of occurrence and/or 3) to understand their ecological niche limits (also referred to as ecological niche modelling). The fact that these three aims may require different models and outputs is, however, rarely considered and has not been evaluated consistently. Here we use data from a systematically sampled set of species occurrences to specifically test the performance of Species Distribution Models across several commonly used algorithms. Species range in distribution patterns from rare to common and from local to widespread. We compare overall model fit (representing species distribution), the accuracy of the predictions at multiple spatial scales, and the consistency in selection of environmental correlations all across multiple modelling runs. As expected, the choice of modelling algorithm determines model outcome. However, model quality depends not only on the algorithm, but also on the measure of model fit used and the scale at which it is used. Although model fit was higher for the consensus approach and Maxent, Maxent and GAM models were more consistent in estimating local occurrence, while RF and GBM showed higher consistency in environmental variables selection. Model outcomes diverged more for narrowly distributed species than for widespread species. We suggest that matching study aims with modelling approach is essential in Species Distribution Models, and provide suggestions how to do this for different modelling aims and species’ data characteristics (i.e. sample size, spatial distribution).

Suggested Citation

  • 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.
    • Handle: RePEc:plo:pone00:0063708
    DOI: 10.1371/journal.pone.0063708
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0063708
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0063708&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0063708?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. 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.
    2. 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.
    3. Alex Hagen-Zanker, 2006. "Map comparison methods that simultaneously address overlap and structure," Journal of Geographical Systems, Springer, vol. 8(2), pages 165-185, July.
    4. Austin, Mike, 2007. "Species distribution models and ecological theory: A critical assessment and some possible new approaches," Ecological Modelling, Elsevier, vol. 200(1), pages 1-19.
    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. Tereza Cristina Giannini & Wilian França Costa & Guaraci Duran Cordeiro & Vera Lucia Imperatriz-Fonseca & Antonio Mauro Saraiva & Jacobus Biesmeijer & Lucas Alejandro Garibaldi, 2017. "Projected climate change threatens pollinators and crop production in Brazil," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-13, August.
    2. 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).
    3. Wen-Dong Xie & Jia Jia & Kai Song & Chang-Li Bu & Li-Ming Ma & Ge-Sang Wang-Jie & Quan-Liang Li & Heng-Qing Yin & Feng-Yi Xu & Dui-Fang Ma & Xin-Hai Li & Yun Fang & Yue-Hua Sun, 2022. "Comparative Habitat Divergence and Fragmentation Analysis of Two Sympatric Pheasants in the Qilian Mountains, China," Land, MDPI, vol. 11(12), pages 1-14, November.
    4. Bell, David M. & Schlaepfer, Daniel R., 2016. "On the dangers of model complexity without ecological justification in species distribution modeling," Ecological Modelling, Elsevier, vol. 330(C), pages 50-59.
    5. Barker, Justin R. & MacIsaac, Hugh J., 2022. "Species distribution models: Administrative boundary centroid occurrences require careful interpretation," Ecological Modelling, Elsevier, vol. 472(C).
    6. Santiago José Elías Velazco & Franklin Galvão & Fabricio Villalobos & Paulo De Marco Júnior, 2017. "Using worldwide edaphic data to model plant species niches: An assessment at a continental extent," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-24, October.
    7. Perennes, Marie & Diekötter, Tim & Groß, Jens & Burkhard, Benjamin, 2021. "A hierarchical framework for mapping pollination ecosystem service potential at the local scale," Ecological Modelling, Elsevier, vol. 444(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. 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).

    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. 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.
    2. Trevor H. Booth, 2017. "Assessing species climatic requirements beyond the realized niche: some lessons mainly from tree species distribution modelling," Climatic Change, Springer, vol. 145(3), pages 259-271, December.
    3. Santiago José Elías Velazco & Franklin Galvão & Fabricio Villalobos & Paulo De Marco Júnior, 2017. "Using worldwide edaphic data to model plant species niches: An assessment at a continental extent," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-24, October.
    4. Holloway, Paul & Miller, Jennifer A., 2017. "A quantitative synthesis of the movement concepts used within species distribution modelling," Ecological Modelling, Elsevier, vol. 356(C), pages 91-103.
    5. Muñoz-Mas, R. & Martínez-Capel, F. & Alcaraz-Hernández, J.D. & Mouton, A.M., 2015. "Can multilayer perceptron ensembles model the ecological niche of freshwater fish species?," Ecological Modelling, Elsevier, vol. 309, pages 72-81.
    6. Owens, Hannah L. & Campbell, Lindsay P. & Dornak, L. Lynnette & Saupe, Erin E. & Barve, Narayani & Soberón, Jorge & Ingenloff, Kate & Lira-Noriega, Andrés & Hensz, Christopher M. & Myers, Corinne E. &, 2013. "Constraints on interpretation of ecological niche models by limited environmental ranges on calibration areas," Ecological Modelling, Elsevier, vol. 263(C), pages 10-18.
    7. 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.
    8. Melo-Merino, Sara M. & Reyes-Bonilla, Héctor & Lira-Noriega, Andrés, 2020. "Ecological niche models and species distribution models in marine environments: A literature review and spatial analysis of evidence," Ecological Modelling, Elsevier, vol. 415(C).
    9. Jiménez, L. & Soberón, J., 2022. "Estimating the fundamental niche: Accounting for the uneven availability of existing climates in the calibration area," Ecological Modelling, Elsevier, vol. 464(C).
    10. 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).
    11. Václavík, Tomáš & Meentemeyer, Ross K., 2009. "Invasive species distribution modeling (iSDM): Are absence data and dispersal constraints needed to predict actual distributions?," Ecological Modelling, Elsevier, vol. 220(23), pages 3248-3258.
    12. Muñoz-Mas, Rafael & Vezza, Paolo & Alcaraz-Hernández, Juan Diego & Martínez-Capel, Francisco, 2016. "Risk of invasion predicted with support vector machines: A case study on northern pike (Esox Lucius, L.) and bleak (Alburnus alburnus, L.)," Ecological Modelling, Elsevier, vol. 342(C), pages 123-134.
    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. Meineri, Eric & Dahlberg, C. Johan & Hylander, Kristoffer, 2015. "Using Gaussian Bayesian Networks to disentangle direct and indirect associations between landscape physiography, environmental variables and species distribution," Ecological Modelling, Elsevier, vol. 313(C), pages 127-136.
    15. Marmion, Mathieu & Luoto, Miska & Heikkinen, Risto K. & Thuiller, Wilfried, 2009. "The performance of state-of-the-art modelling techniques depends on geographical distribution of species," Ecological Modelling, Elsevier, vol. 220(24), pages 3512-3520.
    16. Kaiping Wang & Weiqi Wang & Niyi Zha & Yue Feng & Chenlan Qiu & Yunlu Zhang & Jia Ma & Rui Zhang, 2022. "Spatially Heterogeneity Response of Critical Ecosystem Service Capacity to Address Regional Development Risks to Rapid Urbanization: The Case of Beijing-Tianjin-Hebei Urban Agglomeration in China," Sustainability, MDPI, vol. 14(12), pages 1-21, June.
    17. 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).
    18. Sellami, Mohamed Habib & Sifaoui, Mohamed Salah, 2008. "Modelling of heat and mass transfer inside a traditional oasis: Experimental validation," Ecological Modelling, Elsevier, vol. 210(1), pages 144-154.
    19. Di Traglia, Mario & Attorre, Fabio & Francesconi, Fabio & Valenti, Roberto & Vitale, Marcello, 2011. "Is cellular automata algorithm able to predict the future dynamical shifts of tree species in Italy under climate change scenarios? A methodological approach," Ecological Modelling, Elsevier, vol. 222(4), pages 925-934.
    20. 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.

    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:pone00:0063708. 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.