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

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...

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Main Authors: César Capinha, Franz Essl, Hanno Seebens, Henrique Miguel Pereira, Ingolf Kühn
Format: Article
Language:English
Published: Pensoft Publishers 2018-06-01
Series:NeoBiota
Online Access:https://neobiota.pensoft.net/articles.php?id=23518
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spelling doaj-709376ebeda443898cd401bc2ed7a87a2020-11-24T21:49:00ZengPensoft PublishersNeoBiota1619-00331314-24882018-06-0138779610.3897/neobiota.38.2351823518Models of alien species richness show moderate predictive accuracy and poor transferabilityCésar Capinha0Franz Essl1Hanno Seebens2Henrique Miguel Pereira3Ingolf Kühn4Zoologisches Forschungsmuseum Alexander KoenigUniversity ViennaSenckenberg Biodiversity and Climate Research CentreGerman Centre for Integrative Biodiversity ResearchHelmholtz Centre for Environmental Research 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. https://neobiota.pensoft.net/articles.php?id=23518
collection DOAJ
language English
format Article
sources DOAJ
author César Capinha
Franz Essl
Hanno Seebens
Henrique Miguel Pereira
Ingolf Kühn
spellingShingle César Capinha
Franz Essl
Hanno Seebens
Henrique Miguel Pereira
Ingolf Kühn
Models of alien species richness show moderate predictive accuracy and poor transferability
NeoBiota
author_facet César Capinha
Franz Essl
Hanno Seebens
Henrique Miguel Pereira
Ingolf Kühn
author_sort César Capinha
title Models of alien species richness show moderate predictive accuracy and poor transferability
title_short Models of alien species richness show moderate predictive accuracy and poor transferability
title_full Models of alien species richness show moderate predictive accuracy and poor transferability
title_fullStr Models of alien species richness show moderate predictive accuracy and poor transferability
title_full_unstemmed Models of alien species richness show moderate predictive accuracy and poor transferability
title_sort models of alien species richness show moderate predictive accuracy and poor transferability
publisher Pensoft Publishers
series NeoBiota
issn 1619-0033
1314-2488
publishDate 2018-06-01
description 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.
url https://neobiota.pensoft.net/articles.php?id=23518
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