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Enhancing island biogeography: improving identification of potential species pools via environmental filtering


Abstract

A successful theory in ecology is the equilibrium species numbers on islands. Most of it is about the total number of species on “islands” (sensu lato), ignoring complications like species identities, history, and environmental differences. We propose an operational approach for identification of potential species pools, moving from predicting only numbers toward a better understanding of species identities. This procedure includes two main steps: (1) provide an initial pool of species names, and (2) refine that list via niche-based environmental filtering. To provide the initial species pool (step 1), we explored two regions of different sizes. For step 2, we used ecological niche modeling to separate potential colonizers from species that do not have any distributional potential on the island(s) in question. As an example, we used data on butterflies of the Caribbean Islands. We found that, regardless of the size of the pool, our methods were able to refine (reduce) potential species pools without increasing omission errors. Our predicted species pools, however, show apparent “errors” of commission—we interpret these species as representing as-yet unrealized potential colonizers. Overall, this approach allows researchers to refine estimates of species pools, and thereby improve the ability to predict potential local species richness and species identities.

Data availability

Curated data used in this study are available at https://doi.org/10.6084/m9.figshare.27397749 . The code needed to reproduce all steps of the project (including data download and preparation) is openly available at https://github.com/claununez/Island_Biogeography_Pools.

References

  1. Wu, J. & Vankat, J. L. Island biogeography: Theory and applications. In Encyclopedia of Environmental Biology (ed Nierenberg, W. A.) vol. 2 371–379 (Academic, 1995).

    Google Scholar 

  2. Ricklefs, R. E. A comprehensive framework for global patterns in biodiversity. Ecol. Lett. 7, 1–15 (2004).

    Google Scholar 

  3. Ricklefs, R. E. Community diversity: Relative roles of local and regional processes. Science 235, 167–171 (1987).

    Google Scholar 

  4. Srivastava, D. Using local–regional richness plots to test for species saturation: Pitfalls and potentials. J. Anim. Ecol. 68, 1–16 (1999).

    Google Scholar 

  5. Cornell, H. V. & Harrison, S. P. What are species pools and when are they important? Annu. Rev. Ecol. Evol. Syst. 45, 45–67 (2014).

    Google Scholar 

  6. Borregaard, M. K., Graves, G. R. & Rahbek, C. Dispersion fields reveal the compositional structure of South American vertebrate assemblages. Nat. Commun. 11, 491 (2020).

    Google Scholar 

  7. Graves, G. R. & Rahbek, C. Source pool geometry and the assembly of continental avifaunas. Proc. Natl. Acad. Sci. 102, 7871–7876 (2005).

  8. Soberón, J. & Cavner, J. Indices of biodiversity pattern based on presence-absence matrices: A GIS implementation. Biodivers. Inf. 10, 22–34 (2015).

    Google Scholar 

  9. Herkt, K. M. B., Skidmore, A. K. & Fahr, J. Macroecological conclusions based on IUCN expert maps: A call for caution. Glob Ecol. Biogeogr. 26, 930–941 (2017).

    Google Scholar 

  10. Peterson, A. T. et al. Ecological Niches and Geographic Distributions (Princeton University Press, 2011).

  11. Smith, A. B., Godsoe, W., Rodríguez-Sánchez, F., Wang, H. H. & Warren, D. Niche estimation above and below the species level. Trends Ecol. Evol. 34, 260–273 (2019).

    Google Scholar 

  12. Nogués-Bravo, D. Predicting the past distribution of species climatic niches. Glob Ecol. Biogeogr. 18, 521–531 (2009).

    Google Scholar 

  13. Colwell, R. K. & Rangel, T. F. A stochastic, evolutionary model for range shifts and richness on tropical elevational gradients under Quaternary glacial cycles. Philos. Trans. R Soc. B Biol. Sci. 365, 3695–3707 (2010).

    Google Scholar 

  14. Hagen, O. et al. gen3sis: A general engine for eco-evolutionary simulations of the processes that shape Earth’s biodiversity. PLOS Biol. 19, e3001340 (2021).

    Google Scholar 

  15. Qiao, H. et al. NicheA: Creating virtual species and ecological niches in multivariate environmental scenarios. Ecography 39, 805–813 (2016).

    Google Scholar 

  16. Soberón, J. & Osorio-Olvera, L. A dynamic theory of the area of distribution. J. Biogeogr. 50, 1037–1048 (2023).

    Google Scholar 

  17. Machado-Stredel, F., Cobos, M. E. & Peterson, A. T. A simulation-based method for selecting calibration areas for ecological niche models and species distribution models. Front. Biogeogr. 13, e488 (2021).

    Google Scholar 

  18. Karl, W. C., Verghese, G. C. & Willsky, A. S. Reconstructing ellipsoids from projections. CVGIP Graph Models Image Process. 56, 124–139 (1994).

    Google Scholar 

  19. Stockwell, D. Niche Modeling: Predictions from Statistical Distributions (Chapman and Hall/CRC, 2006). https://doi.org/10.1201/9781420010466

  20. Carstensen, D. W., Lessard, J. P., Holt, B. G., Krabbe Borregaard, M. & Rahbek, C. Introducing the biogeographic species pool. Ecography 36, 1310–1318 (2013).

    Google Scholar 

  21. Kraft, N. J. B. et al. Disentangling the drivers of β diversity along latitudinal and elevational gradients. Science 333, 1755–1758 (2011).

    Google Scholar 

  22. Patrick, C. J. & Brown, B. L. Species pool functional diversity plays a hidden role in generating β-diversity. Am. Nat. 191, E159–E170 (2018).

    Google Scholar 

  23. Anacker, B. L. & Harrison, S. P. Historical and ecological controls on phylogenetic diversity in californian plant communities. Am. Nat. 180, 257–269 (2012).

    Google Scholar 

  24. Muscarella, R. et al. Soil fertility and flood regime are correlated with phylogenetic structure of Amazonian palm communities. Ann. Bot. 123, 641–655 (2019).

    Google Scholar 

  25. Stendera, S. E. S. & Johnson, R. K. Additive partitioning of aquatic invertebrate species diversity across multiple spatial scales. Freshw. Biol. 50, 1360–1375 (2005).

    Google Scholar 

  26. Passy, S. I. The relationship between local and regional diatom richness is mediated by the local and regional environment. Glob Ecol. Biogeogr. 18, 383–391 (2009).

    Google Scholar 

  27. Ball, B., Karrer, B. & Newman, M. E. J. Efficient and principled method for detecting communities in networks. Phys. Rev. E. 84, 036103 (2011).

    Google Scholar 

  28. Linder, H. P. et al. The partitioning of Africa: Statistically defined biogeographical regions in sub-Saharan Africa. J. Biogeogr. 39, 1189–1205 (2012).

    Google Scholar 

  29. Burger, J. R., Anderson, R. P., Balk, M. A. & Fristoe, T. S. A constraint-based model of dynamic island biogeography: Environmental history and species traits predict hysteresis in populations and communities. Front Biogeogr 11, 109527 (2019).

  30. Diaz, S., Cabido, M. & Casanoves, F. Plant functional traits and environmental filters at a regional scale. J. Veg. Sci. 9, 113–122 (1998).

    Google Scholar 

  31. de Bello, F. et al. Hierarchical effects of environmental filters on the functional structure of plant communities: A case study in the French Alps. Ecography 36, 393–402 (2013).

    Google Scholar 

  32. Le Bagousse-Pinguet, Y. et al. Testing the environmental filtering concept in global drylands. J. Ecol. 105, 1058–1069 (2017).

    Google Scholar 

  33. Mendes, P., Velazco, S. J. E., de Andrade, A. F. A. & De Marco, P. Dealing with overprediction in species distribution models: How adding distance constraints can improve model accuracy. Ecol. Model. 431, 109180 (2020).

    Google Scholar 

  34. Qian, H. Are species lists derived from modeled species range maps appropriate for macroecological studies? A case study on data from BIEN. Basic. Appl. Ecol. 48, 146–156 (2020).

    Google Scholar 

  35. Cain, S. A. Foundations of Plant Geography (Harper and Brothers, 1944).

  36. Soberón, J. & Peterson, A. T. Interpretation of models of fundamental ecological niches and species’ distributional areas. Biodivers. Inf. 2, 1–10 (2005).

    Google Scholar 

  37. Da Re, D., Tordoni, E., Lenoir, J., Rubin, S. & Vanwambeke, S. O. Towards causal relationships for modelling species distribution. J. Biogeogr. 51, 840–852 (2024).

    Google Scholar 

  38. Olson, D. M. et al. Terrestrial ecoregions of the world: A new map of life on earth: A new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity. BioScience 51, 933–938 (2001).

    Google Scholar 

  39. Riley, N. D. A Field Guide to the Butterflies of the West Indies (Demeter Press Book, 1975).

  40. Chapman, A. D. Principles of Data Quality (GBIF, 2005).

  41. Cobos, M. E., Jiménez, L., Nuñez-Penichet, C., Romero-Alvarez, D. & Simoes, M. Sample data and training modules for cleaning biodiversity information. Biodivers. Inf. 13, 49–50 (2018).

    Google Scholar 

  42. Aiello-Lammens, M. E., Boria, R. A., Radosavljevic, A., Vilela, B. & Anderson, R. P. spThin: An R package for spatial thinning of species occurrence records for use in ecological niche models. Ecography 38, 541–545 (2015).

    Google Scholar 

  43. R Core Team. R: A language and environment for statistical computing (R Foundation for Statistical Computing, 2023).

  44. Cobos, M. E. et al. ellipsenm: Ecological niche’s characterizations using ellipsoids. R Package (2020). https://github.com/marlonecobos/ellipsenm

  45. Guisan, A., Thuiller, W. & Zimmermann, N. E. Habitat Suitability and Distribution Models: With Applications in R (Cambridge University Press, 2017).

  46. Franklin, J. Mapping Species Distributions: Spatial Inference and Prediction (Cambridge University Press, 2010).

  47. Farber, O. & Kadmon, R. Assessment of alternative approaches for bioclimatic modeling with special emphasis on the Mahalanobis distance. Ecol. Model. 160, 115–130 (2003).

    Google Scholar 

  48. Ahmed, S. E. et al. Scientists and software—surveying the species distribution modelling community. Divers. Distrib. 21, 258–267 (2015).

    Google Scholar 

  49. Cunze, S. & Tackenberg, O. Decomposition of the maximum entropy niche function—a step beyond modelling species distribution. Environ. Model. Softw. 72, 250–260 (2015).

    Google Scholar 

  50. Drake, J. M. Range bagging: A new method for ecological niche modelling from presence-only data. J. R Soc. Interface. 12, 20150086 (2015).

    Google Scholar 

  51. Jiménez, L., Soberón, J., Christen, J. A. & Soto, D. On the problem of modeling a fundamental niche from occurrence data. Ecol. Model. 397, 74–83 (2019).

    Google Scholar 

  52. Clark, J. D., Dunn, J. E. & Smith, K. G. A multivariate model of female black bear habitat use for a geographic information system. J. Wildl. Manag. 57, 519–526 (1993).

    Google Scholar 

  53. Strang, G. Introduction to Linear Algebra (Wellesley-Cambridge, 2003).

  54. Owens, H. L. et al. Constraints on interpretation of ecological niche models by limited environmental ranges on calibration areas. Ecol. Model. 263, 10–18 (2013).

    Google Scholar 

  55. Cobos, M. E., Owens, H. L., Soberón, J. & Peterson, A. T. Detailed multivariate comparisons of environments with mobility oriented parity. Front. Biogeogr. 17, e132916 (2024).

    Google Scholar 

  56. Nuñez-Penichet, C. et al. Selection of sampling sites for biodiversity inventory: Effects of environmental and geographical considerations. Methods Ecol. Evol. 13, 1595–1607 (2022).

    Google Scholar 

  57. Nuñez-Penichet, C. et al. biosurvey: Tools for biological survey planning. (2020).

  58. Oksanen, J. et al. vegan: Community ecology package. (2024).

  59. Murdoch, D. & Chow, E. D. ellipse: Functions for drawing ellipses and ellipse-like confidence regions. (2023).

  60. Hijmans, R. J., Bivand, R., Dyba, K., Pebesma, E. & Sumner M. D. terra: Spatial data analysis. (2024).

  61. Peres-Neto, P. R. & Jackson, D. A. How well do multivariate data sets match? The advantages of a Procrustean superimposition approach over the Mantel test. Oecologia 129, 169–178 (2001).

    Google Scholar 

  62. Jackson, D. A. PROTEST: A procrustean randomization test of community environment concordance. Écoscience 2, 297–303 (1995).

    Google Scholar 

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Acknowledgements

We would like to thank the members of the KUENM group for their support in the development of this manuscript.

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CNP, JS, and ATP designed the research. CNP and JS gathered the data. CNP, JS, and MEC developed R code and performed the analyses. CNP and FMS created the figures. All authors wrote and reviewed the manuscript.

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Claudia Nuñez-Penichet.

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Nuñez-Penichet, C., Soberón, J., Cobos, M.E. et al. Enhancing island biogeography: improving identification of potential species pools via environmental filtering.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-43084-9

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  • DOI: https://doi.org/10.1038/s41598-026-43084-9

Keywords

  • Caribbean
  • Island biogeography
  • Niche modeling
  • Species pools
  • Sulphurs
  • Swallowtails


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