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Location-level processes drive the establishment of alien bird populations worldwide

Nature volume 571, pages 103–106 (2019)Cite this article

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Abstract

Human-mediated translocation of species to areas beyond their natural distribution (which results in ‘alien’ populations1) is a key signature of the Anthropocene2, and is a primary global driver of biodiversity loss and environmental change3. Stemming the tide of invasions requires understanding why some species fail to establish alien populations, and others succeed. To achieve this, we need to integrate the effects of features of the introduction site, the species introduced and the specific introduction event. Determining which, if any, location-level factors affect the success of establishment has proven difficult, owing to the multiple spatial, temporal and phylogenetic axes along which environmental variation may influence population survival. Here we apply Bayesian hierarchical regression analysis to a global spatially and temporally explicit database of introduction events of alien birds4 to show that environmental conditions at the introduction location, notably climatic suitability and the presence of other groups of alien species, are the primary determinants of successful establishment. Species-level traits and the size of the founding population (propagule pressure) exert secondary, but important, effects on success. Thus, current trajectories of anthropogenic environmental change will most probably facilitate future incursions by alien species, but predicting future invasions will require the integration of multiple location-, species- and event-level characteristics.

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Fig. 1: Posterior distributions for fixed-effects parameter estimates for the best-fitting model of the success of alien bird establishment.
Fig. 2: Relative effect size of different categories of predictors in the best-fitting model of the success of alien bird establishment.
Fig. 3: Phylogenetic patterns of invasion probability across alien birds.

Data availability

All data generated or analysed during this study are included with the paper and its Supplementary Information.

Code availability

Code used to calculate the final model is included in Supplementary Information.

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Acknowledgements

We thank M. Parnell, V. Franks, F. Spooner, R. Herdson, E. Jones, F. Davis and M. Susko for assisting with data collection and map creation. Initial funding for this study was provided by a grant from the Leverhulme Trust (RF/2/RFG/2010/0016) (E.E.D.), with additional support from a UCL IMPACT studentship (10989) (E.E.D.), a Leverhulme Trust grant (RPG-2015-073) (T.M.B., A.L.P. and E.E.D.), and from a King Saud University Distinguished Scientist Research Fellowship (T.M.B., D.W.R. and E.E.D.). D.W.R. is supported by a MRC UKRI/Rutherford Fellowship (MR/R02491X/1). A.L.P. is supported by a Royal Society University Research Fellowship (UF160281).

Author information

Authors and Affiliations

  1. Centre for Biodiversity and Environment Research, Department of Genetics, Evolution and Environment, University College London, London, UK

    David W. Redding, Alex L. Pigot, Ellie E. Dyer & Tim M. Blackburn

  2. Biodiversity and Conservation Ecology Laboratory, Department of Biology, University of Utah, Salt Lake City, UT, USA

    Çağan H. Şekercioğlu

  3. College of Sciences, Koç University, Istanbul, Turkey

    Çağan H. Şekercioğlu

  4. The Biodiversity Research Group, The School of Biological Sciences, The University of Queensland, Brisbane, Queensland, Australia

    Salit Kark

  5. Institute of Zoology, Zoological Society of London, London, UK

    Tim M. Blackburn

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Contributions

D.W.R., A.L.P. and T.M.B. developed the overall study design. E.E.D., S.K. and C.H.S. oversaw initial data collation. D.W.R. and A.L.P. carried out the modelling and data processing, with assistance from T.M.B. All authors contributed to writing the manuscript.

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Correspondence to Tim M. Blackburn.

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Extended data figures and tables

Extended Data Fig. 1 Sensitivity analysis of slope (β) estimates for the linear terms of a subset of variables across all versions of the input data.

Dot size is the size of the β value, with colour representing direction (red, positive; blue, negative). Each row label represents the name of the fixed effect. The column headings represent the subset of data used: ‘all-records’, all data (n = 4,346); ‘intro-and-unk.’, all data, but one record per species-location event (n = 3,762); ‘intro-only’, detailed introductions only (n = 1,784); ‘intro-last-only’, detailed introductions, but one record per species-location event (n = 1,530). The number at end of each column heading indicates the relative size of the buffer used to impute establishment status (Methods).

Extended Data Fig. 2 Approximate shape of fixed effects over the range of observed values.

Each panel represents the prediction using β slope estimates from the lowest wAIC model over the known range of values for that given fixed effect (identified by strip title) from the raw data. Only fixed effects for which the values were unlikely to include zero are included. All panels from a single Bayesian regression of global avian establishment success (n = 1,530 introductions).

Extended Data Fig. 3 Chord diagram showing the directions of origin and introduction location of avian introduction events between regions of the world.

The chords near the edge represent introductions to a region; chords away from the edge show origins of introduction. The width of chord is the relative number of introduction events (n = 4,346).

Extended Data Fig. 4 Model diagnostics from the best-fitting model.

a, Plot of out-of-sample CPO scores for all data points in rank order used in the model. b, Probability density of the CPO scores. c, Map of CPO scores. CPO is the probability of generating each data point in the dataset from a posterior fitted without this data point. Each panel allows visualization of where in the data the model does not fit well. All plots from a single Bayesian regression of global establishment success of avian introductions (n = 1,530 introductions).

Extended Data Table 1 All covariates in the best-fitting model, from a Bayesian regression of global avian establishment success
Full size table

Supplementary information

Reporting Summary

Supplementary Information

The R code used to run the analyses reported in this manuscript.

Supplementary Data 1

Raw data used in all analyses. Contains 5477 records of all alien bird introduction events that could be found in the literature, including those considered dubious.

Supplementary Data 2

All the covariates considered in the analysis with proposed hypotheses, underlying justification and source.

Supplementary Data 3

Data used in the final analysis (n = 1530). Contains known alien bird introduction events, with only those records that are specified as detailed introduction events. All events that were part of a chronological sequence of introduction events were collapsed into a single record, summarised using the date of the last introduction events.

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Redding, D.W., Pigot, A.L., Dyer, E.E. et al. Location-level processes drive the establishment of alien bird populations worldwide. Nature 571, 103–106 (2019). https://doi.org/10.1038/s41586-019-1292-2

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