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Cross-realm transferability of species distribution models–Species characteristics and prevalence matter more than modelling methods applied

Author

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  • Takolander, Antti
  • Forsblom, Louise
  • Hellsten, Seppo
  • Ilmonen, Jari
  • Jokinen, Ari-Pekka
  • Kallio, Niko
  • Koponen, Sampsa
  • Väkevä, Sakari
  • Virtanen, Elina

Abstract

Species Distribution Models (SDMs) are frequently applied in ecological research, but geographic transferability of SDMs holds major uncertainties. Here, we assess the cross-realm (sea to lake) geographic transferability of four SDM methods: Generalized Linear Models (GLMs), Generalized Additive Models (GAMs), Boosted Regression Trees (BRTs), and Bayesian Additive Regression Trees (BARTs) predicting occurrences of freshwater macrophytes from brackish water sea area (Bothnian Bay) to a freshwater lake environment in Finland. We found that the SDM method applied did not affect model transferability, and majority of the variation in transferability performance was associated with species. For most species model transferability was low, but reasonably good on one third of the species modelled, which had similar prevalences in both marine and freshwater data. These were emergent species or species growing close to shoreline, which presumably share similar environmental niche in terms of growing depth and water turbidity between the two environments. Generally, models which had high interpolation performance, also had higher transferability, but this relationship was not dependent on the SDM method applied. Our results suggest that species prevalence and species-specific characteristics, such as growth form, life history traits and ecological niche, are main contributors to geographic transferability of SDMs.

Suggested Citation

  • Takolander, Antti & Forsblom, Louise & Hellsten, Seppo & Ilmonen, Jari & Jokinen, Ari-Pekka & Kallio, Niko & Koponen, Sampsa & Väkevä, Sakari & Virtanen, Elina, 2025. "Cross-realm transferability of species distribution models–Species characteristics and prevalence matter more than modelling methods applied," Ecological Modelling, Elsevier, vol. 499(C).
  • Handle: RePEc:eee:ecomod:v:499:y:2025:i:c:s0304380024003387
    DOI: 10.1016/j.ecolmodel.2024.110950
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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. Simon N. Wood, 2011. "Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(1), pages 3-36, January.
    3. Silvia Ferrari & Francisco Cribari-Neto, 2004. "Beta Regression for Modelling Rates and Proportions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 31(7), pages 799-815.
    4. Duque-Lazo, J. & van Gils, H. & Groen, T.A. & Navarro-Cerrillo, R.M., 2016. "Transferability of species distribution models: The case of Phytophthora cinnamomi in Southwest Spain and Southwest Australia," Ecological Modelling, Elsevier, vol. 320(C), pages 62-70.
    5. Tjur, Tue, 2009. "Coefficients of Determination in Logistic Regression Models—A New Proposal: The Coefficient of Discrimination," The American Statistician, American Statistical Association, vol. 63(4), pages 366-372.
    6. 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.
    7. 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.
    8. Benkendorf, Donald J. & Schwartz, Samuel D. & Cutler, D. Richard & Hawkins, Charles P., 2023. "Correcting for the effects of class imbalance improves the performance of machine-learning based species distribution models," Ecological Modelling, Elsevier, vol. 483(C).
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