Abstract
The accurate anticipation of potential biological invasions is a crucial step toward the control of invasive species. The method used most commonly to identify areas suitable for biological invasion is the construction of ecological niche models (ENMs), although the potential accuracy of this approach may be grossly overestimated. In the present study, we examine how biogeographical, biological, and methodological factors may affect our capacity to identify areas suitable for biological invasion. We created virtual species to investigate the incongruences between the fundamental and available niche in a natural environment. Firstly, we verified how differences in species characteristics (environmental tolerance and dispersal capacity) may hinder our ability to predict invasions using ENMs. We also evaluated how different algorithms behave in the context of these differences. We also measured how prediction accuracy varies in different regions of the world, by evaluating the degree of niche mismatch found in each zoogeographic region. In general, the predictions of the ENMs varied according to species tolerance, dispersal capacity, and the algorithm used to fit the model, although the principal source of variation was the degree to which the algorithms under- or over-estimated the fundamental niche. Some zoogeographic regions did indeed prove to be more error-prone than others, due to the variation in the levels of climatic incompleteness and the representation of the fundamental niche within a species’ distribution. We demonstrate that the prediction of potential biological invasions using ENMs may incur errors in niche estimation, which may result in suitable locations being overlooked. This reinforces the need for caution in the prediction of biological invasions, given that the fundamental niche may not be expressed adequately within the native range of the species, as determined fundamentally by its biological characteristics.
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Data availability
Due to the large size of files (all the raster files were over 40 GB), we will make available at Pangaea the occurrence data used for model fitting and the tables used for niche and geographical analysis (under-and overprediction). The mentioned data is available at Pangaea database through the link https://doi.pangaea.de/10.1594/PANGAEA.892658. For the remaining data (species’ fundamental niche and distribution and models’ results), please contact the corresponding author.
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Acknowledgements
We are greatly indebted to Danira Letícia Padilha, Daniel de Paiva Silva and José Alexandre Felizola Diniz-Filho, who revised an early version of this manuscript. PDM is supported by a productivity Grant (308694/2015-5) from the Brazilian National Council for Scientific and Technological Development (CNPq). The research of AFAA is funded by the Brazilian Coordination for Higher Education Personnel Training (CAPES). AFAA and PDM conceived the idea for the study, designed the methodology, and were responsible for the creation of the virtual species; AFAA and SJEV analysed the data; AFAA led the writing of the manuscript. All authors contributed to the draft, improvement of the content, and gave their final approval for its publication.
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de Andrade, A.F.A., Velazco, S.J.E. & De Marco, P. Niche mismatches can impair our ability to predict potential invasions. Biol Invasions 21, 3135–3150 (2019). https://doi.org/10.1007/s10530-019-02037-2
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DOI: https://doi.org/10.1007/s10530-019-02037-2