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Cited by 16Ecology and Vegetation Dynamics StudiesSpecies Distribution and Climate ChangeMarine Ecology and Invasive Species

Models of alien species richness show moderate predictive accuracy and poor transferability

Open access
Capinha, César, Essl, Franz, Seebens, Hanno, Pereira, Henrique Miguel, Kühn, Ingolf·2018·NeoBiota
DOI 10.3897/neobiota.38.23518
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Abstract

Robust predictions of alien species richness are useful to assess global biodiversity change. Nevertheless, the capacity to predict spatial patterns of alien species richness remains largely unassessed. Using 22 data sets of alien species richness from diverse taxonomic groups and covering various parts of the world, we evaluated whether different statistical models were able to provide useful predictions of absolute and relative alien species richness, as a function of explanatory variables representing geographical, environmental and socio-economic factors. Five state-of-the-art count data modelling techniques were used and compared: Poisson and negative binomial generalised linear models (GLMs), multivariate adaptive regression splines (MARS), random forests (RF) and boosted regression trees (BRT). We found that predictions of absolute alien species richness had a low to moderate accuracy in the region where the models were developed and a consistently poor accuracy in new regions. Predictions of relative richness performed in a superior manner in both geographical settings, but still were not good. Flexible tree ensembles-type techniques (RF and BRT) were shown to be significantly better in modelling alien species richness than parametric linear models (such as GLM), despite the latter being more commonly applied for this purpose. Importantly, the poor spatial transferability of models also warrants caution in assuming the generality of the relationships they identify, e.g. by applying projections under future scenario conditions. Ultimately, our results strongly suggest that predictability of spatial variation in richness of alien species richness is limited. The somewhat more robust ability to rank regions according to the number of aliens they have (i.e. relative richness), suggests that models of aliens species richness may be useful for prioritising and comparing regions, but not for predicting exact species numbers.

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  • There is still much uncertainty in predictions of which species will colonize new regions and habitats and what their impacts will be.

    “Despite intensive research directed at modelling potential changes in the distribution of terrestrial, freshwater, and marine species owing to climate and land‐use change, there is still much uncertainty in predictions of which species will colonize new regions and habitats and what their impacts will be (Elith, 2017; Capinha et al., 2018; Rocchini et al., 2019).”

    From Scientists' warning on invasive alien species (2020) · cites this paper as Capinha et al., 2018

    The Conclusions (page not available in our copy) say that regional alien-species richness cannot be predicted reliably, supporting uncertainty about colonization patterns in a broad sense. The paper does not establish uncertainty about which particular species will colonize new regions or habitats, or about predicting their impacts; its Introduction mentions impacts as a use for richness information, not as a prediction it tested. (LLM, conf=0.86)

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Research works printed but not linked: 4

  1. Burnham KP, Anderson DR (2002) Model selection and multimodel inference. 2nd edition. Springer, New York, 1-488.
  2. Fokkema M (2017) pre: An R Package for Fitting Prediction Rule Ensembles. arXiv preprint arXiv: 170707149. https://arxiv.org/abs/1707.07149 · arXiv
  3. Metzger MJ, Bunce RGH, Jongman RHG, Sayre R, Trabucco A, Zomer R (2013) A high- resolution bioclimate map of the world: a unifying framework for global biodiversity research and monitoring. Global Ecology and Biogeography, 22: 630-638. https://doi. org/10.1111/geb.1202 · DOI
  4. Witten IH, Frank E, Hall MA, Pal CJ (2016) Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann: 1-655.

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Authors
Capinha, C., Essl, F., Seebens, H., Pereira, H. M., & Kühn, I.
Affiliations
  • Universidade do Porto (PT)
  • University of Vienna (AT)
  • Senckenberg Biodiversity and Climate Research Centre (DE)
  • German Centre for Integrative Biodiversity Research (DE)
  • Helmholtz Centre for Environmental Research (DE)
ORCID
Venue
NeoBiota
Publisher
Pensoft Publishers
Published
2018-06-06
Volume
38
Pages
77-96
ISSN
  • 1314-2488 (ISSN-L)
  • 1619-0033
DOI
10.3897/neobiota.38.23518
OpenAlex
W2806685719
Cited by
16
References
51
Citations by year
2026: 1 · 2025: 2 · 2024: 2 · 2023: 1 · 2022: 4 · 2021: 1 · 2020: 4 · 2019: 1
FWCI
1.18
Type
journal article
Language
en
Open access
Licence
Locations
  • NeoBiota (publishedVersion, cc-by)
  • Phaidra (Universität Wien) (submittedVersion, cc-by-nc-nd)
  • Biodiversity Heritage Library (Smithsonian Institution) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Informationszentrum Lebenswissenschaften (ZB MED) (submittedVersion, cc-by)
  • Publication Server of Goethe University Frankfurt am Main (Goethe University Frankfurt) (submittedVersion, cc-by)
  • Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, cc-by)
Topics
  • Physical Sciences › Environmental Science › Nature and Landscape Conservation › Ecology and Vegetation Dynamics Studies
  • Physical Sciences › Environmental Science › Ecological Modeling › Species Distribution and Climate Change
  • Physical Sciences › Environmental Science › Global and Planetary Change › Marine Ecology and Invasive Species
Keywords
invasive species, species richness prediction, spatial transferability, count data models, random forest, boosted regression trees, multivariate adaptive regression splines, negative binomial regression, Poisson regression, biodiversity change, model comparison

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