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
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).
Ricklefs, R. E. A comprehensive framework for global patterns in biodiversity. Ecol. Lett. 7, 1–15 (2004).
Ricklefs, R. E. Community diversity: Relative roles of local and regional processes. Science 235, 167–171 (1987).
Srivastava, D. Using local–regional richness plots to test for species saturation: Pitfalls and potentials. J. Anim. Ecol. 68, 1–16 (1999).
Cornell, H. V. & Harrison, S. P. What are species pools and when are they important? Annu. Rev. Ecol. Evol. Syst. 45, 45–67 (2014).
Borregaard, M. K., Graves, G. R. & Rahbek, C. Dispersion fields reveal the compositional structure of South American vertebrate assemblages. Nat. Commun. 11, 491 (2020).
Graves, G. R. & Rahbek, C. Source pool geometry and the assembly of continental avifaunas. Proc. Natl. Acad. Sci. 102, 7871–7876 (2005).
Soberón, J. & Cavner, J. Indices of biodiversity pattern based on presence-absence matrices: A GIS implementation. Biodivers. Inf. 10, 22–34 (2015).
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).
Peterson, A. T. et al. Ecological Niches and Geographic Distributions (Princeton University Press, 2011).
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).
Nogués-Bravo, D. Predicting the past distribution of species climatic niches. Glob Ecol. Biogeogr. 18, 521–531 (2009).
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).
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).
Qiao, H. et al. NicheA: Creating virtual species and ecological niches in multivariate environmental scenarios. Ecography 39, 805–813 (2016).
Soberón, J. & Osorio-Olvera, L. A dynamic theory of the area of distribution. J. Biogeogr. 50, 1037–1048 (2023).
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).
Karl, W. C., Verghese, G. C. & Willsky, A. S. Reconstructing ellipsoids from projections. CVGIP Graph Models Image Process. 56, 124–139 (1994).
Stockwell, D. Niche Modeling: Predictions from Statistical Distributions (Chapman and Hall/CRC, 2006). https://doi.org/10.1201/9781420010466
Carstensen, D. W., Lessard, J. P., Holt, B. G., Krabbe Borregaard, M. & Rahbek, C. Introducing the biogeographic species pool. Ecography 36, 1310–1318 (2013).
Kraft, N. J. B. et al. Disentangling the drivers of β diversity along latitudinal and elevational gradients. Science 333, 1755–1758 (2011).
Patrick, C. J. & Brown, B. L. Species pool functional diversity plays a hidden role in generating β-diversity. Am. Nat. 191, E159–E170 (2018).
Anacker, B. L. & Harrison, S. P. Historical and ecological controls on phylogenetic diversity in californian plant communities. Am. Nat. 180, 257–269 (2012).
Muscarella, R. et al. Soil fertility and flood regime are correlated with phylogenetic structure of Amazonian palm communities. Ann. Bot. 123, 641–655 (2019).
Stendera, S. E. S. & Johnson, R. K. Additive partitioning of aquatic invertebrate species diversity across multiple spatial scales. Freshw. Biol. 50, 1360–1375 (2005).
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).
Ball, B., Karrer, B. & Newman, M. E. J. Efficient and principled method for detecting communities in networks. Phys. Rev. E. 84, 036103 (2011).
Linder, H. P. et al. The partitioning of Africa: Statistically defined biogeographical regions in sub-Saharan Africa. J. Biogeogr. 39, 1189–1205 (2012).
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).
Diaz, S., Cabido, M. & Casanoves, F. Plant functional traits and environmental filters at a regional scale. J. Veg. Sci. 9, 113–122 (1998).
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).
Le Bagousse-Pinguet, Y. et al. Testing the environmental filtering concept in global drylands. J. Ecol. 105, 1058–1069 (2017).
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).
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).
Cain, S. A. Foundations of Plant Geography (Harper and Brothers, 1944).
Soberón, J. & Peterson, A. T. Interpretation of models of fundamental ecological niches and species’ distributional areas. Biodivers. Inf. 2, 1–10 (2005).
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).
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).
Riley, N. D. A Field Guide to the Butterflies of the West Indies (Demeter Press Book, 1975).
Chapman, A. D. Principles of Data Quality (GBIF, 2005).
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).
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).
R Core Team. R: A language and environment for statistical computing (R Foundation for Statistical Computing, 2023).
Cobos, M. E. et al. ellipsenm: Ecological niche’s characterizations using ellipsoids. R Package (2020). https://github.com/marlonecobos/ellipsenm
Guisan, A., Thuiller, W. & Zimmermann, N. E. Habitat Suitability and Distribution Models: With Applications in R (Cambridge University Press, 2017).
Franklin, J. Mapping Species Distributions: Spatial Inference and Prediction (Cambridge University Press, 2010).
Farber, O. & Kadmon, R. Assessment of alternative approaches for bioclimatic modeling with special emphasis on the Mahalanobis distance. Ecol. Model. 160, 115–130 (2003).
Ahmed, S. E. et al. Scientists and software—surveying the species distribution modelling community. Divers. Distrib. 21, 258–267 (2015).
Cunze, S. & Tackenberg, O. Decomposition of the maximum entropy niche function—a step beyond modelling species distribution. Environ. Model. Softw. 72, 250–260 (2015).
Drake, J. M. Range bagging: A new method for ecological niche modelling from presence-only data. J. R Soc. Interface. 12, 20150086 (2015).
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).
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).
Strang, G. Introduction to Linear Algebra (Wellesley-Cambridge, 2003).
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).
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).
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).
Nuñez-Penichet, C. et al. biosurvey: Tools for biological survey planning. (2020).
Oksanen, J. et al. vegan: Community ecology package. (2024).
Murdoch, D. & Chow, E. D. ellipse: Functions for drawing ellipses and ellipse-like confidence regions. (2023).
Hijmans, R. J., Bivand, R., Dyba, K., Pebesma, E. & Sumner M. D. terra: Spatial data analysis. (2024).
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).
Jackson, D. A. PROTEST: A procrustean randomization test of community environment concordance. Écoscience 2, 297–303 (1995).
Acknowledgements
We would like to thank the members of the KUENM group for their support in the development of this manuscript.
Funding
This research did not receive funding.
Author information
Authors and Affiliations
Contributions
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.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Supplementary Material 1 (download DOCX )
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Reprints and permissions
About this article
Cite this article
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
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41598-026-43084-9
Keywords
- Caribbean
- Island biogeography
- Niche modeling
- Species pools
- Sulphurs
- Swallowtails
Source: Ecology - nature.com

