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Want to model a species niche? A step-by-step guideline on correlative ecological niche modelling

Author

Listed:
  • Sillero, Neftalí
  • Arenas-Castro, Salvador
  • Enriquez‐Urzelai, Urtzi
  • Vale, Cândida Gomes
  • Sousa-Guedes, Diana
  • Martínez-Freiría, Fernando
  • Real, Raimundo
  • Barbosa, A.Márcia

Abstract

The use of correlative ecological niche models has highly increased in the last decade. Despite all literature and textbooks in this field, few practical guidelines exist on the correct application of these techniques. We present here a step-by-step guideline explaining best practices for calculating correlative ecological niche models considering their conceptual and statistical assumptions and limitations. We divided the modelling process into four stages: 1) data collection and preparation; 2) model calculation; 3) model evaluation and validation; 4) and model application. Based on ecological niche theory, we review the concepts of ecological niche and how they can be modelled; classes of correlative models; modelling software; selection of study area; data sources for species records and environmental variables; types of species records and their influence on correlative models; errors in species records; minimum number of species records and environmental variables; effects of prevalence, sampling design, biases, and collinearity between variables; model calculation; model projection to different scenarios in time and space; ensemble modelling; model validation; classification, discrimination and calibration metrics; calculation of null models; analysis of model results; and model thresholding. This guideline is expected to help potential users to obtain better results when using correlative ecological niche models.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:ecomod:v:456:y:2021:i:c:s0304380021002301
    DOI: 10.1016/j.ecolmodel.2021.109671
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    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. 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.
    3. Boria, Robert A. & Olson, Link E. & Goodman, Steven M. & Anderson, Robert P., 2014. "Spatial filtering to reduce sampling bias can improve the performance of ecological niche models," Ecological Modelling, Elsevier, vol. 275(C), pages 73-77.
    4. Sara Varela & Matheus S Lima-Ribeiro & Levi Carina Terribile, 2015. "A Short Guide to the Climatic Variables of the Last Glacial Maximum for Biogeographers," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-15, June.
    5. Hatten, James R., 2014. "Mapping and monitoring Mount Graham red squirrel habitat with Lidar and Landsat imagery," Ecological Modelling, Elsevier, vol. 289(C), pages 106-123.
    6. Freeman, Elizabeth A. & Moisen, Gretchen G., 2008. "A comparison of the performance of threshold criteria for binary classification in terms of predicted prevalence and kappa," Ecological Modelling, Elsevier, vol. 217(1), pages 48-58.
    7. Shcheglovitova, Mariya & Anderson, Robert P., 2013. "Estimating optimal complexity for ecological niche models: A jackknife approach for species with small sample sizes," Ecological Modelling, Elsevier, vol. 269(C), pages 9-17.
    8. 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.
    9. Chefaoui, Rosa M. & Lobo, Jorge M., 2008. "Assessing the effects of pseudo-absences on predictive distribution model performance," Ecological Modelling, Elsevier, vol. 210(4), pages 478-486.
    10. Daniel Zacarias & Rafael Loyola, 2019. "Climate change impacts on the distribution of venomous snakes and snakebite risk in Mozambique," Climatic Change, Springer, vol. 152(1), pages 195-207, January.
    11. 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.
    12. Sillero, Neftalí, 2011. "What does ecological modelling model? A proposed classification of ecological niche models based on their underlying methods," Ecological Modelling, Elsevier, vol. 222(8), pages 1343-1346.
    13. Stockwell, David R.B. & Noble, Ian R., 1992. "Induction of sets of rules from animal distribution data: A robust and informative method of data analysis," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 33(5), pages 385-390.
    14. Getis, Arthur, 2007. "Reflections on spatial autocorrelation," Regional Science and Urban Economics, Elsevier, vol. 37(4), pages 491-496, July.
    15. Paulo De Marco Júnior & Caroline Corrêa Nóbrega, 2018. "Evaluating collinearity effects on species distribution models: An approach based on virtual species simulation," PLOS ONE, Public Library of Science, vol. 13(9), pages 1-25, September.
    16. Senait D Senay & Susan P Worner & Takayoshi Ikeda, 2013. "Novel Three-Step Pseudo-Absence Selection Technique for Improved Species Distribution Modelling," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-16, August.
    17. Salvador Arenas-Castro & João Gonçalves & Paulo Alves & Domingo Alcaraz-Segura & João P Honrado, 2018. "Assessing the multi-scale predictive ability of ecosystem functional attributes for species distribution modelling," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-31, June.
    18. 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.
    19. 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.
    20. Carlos Yañez-Arenas & A Townsend Peterson & Pierre Mokondoko & Octavio Rojas-Soto & Enrique Martínez-Meyer, 2014. "The Use of Ecological Niche Modeling to Infer Potential Risk Areas of Snakebite in the Mexican State of Veracruz," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-9, June.
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    2. Barker, Justin R. & MacIsaac, Hugh J., 2022. "Species distribution models applied to mosquitoes: Use, quality assessment, and recommendations for best practice," Ecological Modelling, Elsevier, vol. 472(C).
    3. Fernandez, Marc & Sillero, Neftali & Yesson, Chris, 2022. "To be or not to be: the role of absences in niche modelling for highly mobile species in dynamic marine environments," Ecological Modelling, Elsevier, vol. 471(C).
    4. Simon, Alois & Katzensteiner, Klaus & Wallentin, Gudrun, 2023. "The integration of hierarchical levels of scale in tree species distribution models of silver fir (Abies alba Mill.) and European beech (Fagus sylvatica L.) in mountain forests," Ecological Modelling, Elsevier, vol. 485(C).
    5. Sillero, Neftalí & Campos, João Carlos & Arenas-Castro, Salvador & Barbosa, A.Márcia, 2023. "A curated list of R packages for ecological niche modelling," Ecological Modelling, Elsevier, vol. 476(C).

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