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

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.

References

  1. 1.

    Blackburn, T. M. et al. A proposed unified framework for biological invasions. Trends Ecol. Evol. 26, 333–339 (2011).

    Article  Google Scholar 

  2. 2.

    Lewis, S. L. & Maslin, M. A. Defining the Anthropocene. Nature 519, 171–180 (2015).

    ADS  CAS  Article  Google Scholar 

  3. 3.

    Pimentel, D. Biological Invasions: Economic and Environmental Costs of Alien Plant, Animal, and Microbe Species (CRC, Boca Raton, 2011).

  4. 4.

    Dyer, E. E., Redding, D. W. & Blackburn, T. M. The global avian invasions atlas, a database of alien bird distributions worldwide. Sci. Data 4, 170041 (2017).

    Article  Google Scholar 

  5. 5.

    Seebens, H. et al. No saturation in the accumulation of alien species worldwide. Nat. Commun. 8, 14435 (2017).

    ADS  CAS  Article  Google Scholar 

  6. 6.

    Banks, N. C., Paini, D. R., Bayliss, K. L. & Hodda, M. The role of global trade and transport network topology in the human-mediated dispersal of alien species. Ecol. Lett. 18, 188–199 (2015).

    Article  Google Scholar 

  7. 7.

    Drake, J. A. et al. Biological Invasions: A Global Perspective (John Wiley & Sons, Chichester, 1989).

    Google Scholar 

  8. 8.

    Crawley, M. in Colonization, Succession And Stability. 26th Symposium of the British Ecological Society (eds Gray, A. J. et al.) 429–453 (Blackwell Scientific Publications, Oxford, 1986).

  9. 9.

    Lockwood, J. L., Cassey, P. & Blackburn, T. The role of propagule pressure in explaining species invasions. Trends Ecol. Evol. 20, 223–228 (2005).

    Article  Google Scholar 

  10. 10.

    Sol, D. et al. Unraveling the life history of successful invaders. Science 337, 580–583 (2012).

    ADS  CAS  Article  Google Scholar 

  11. 11.

    Sol, D., Griffin, A. S., Bartomeus, I. & Boyce, H. Exploring or avoiding novel food resources? The novelty conflict in an invasive bird. PLoS ONE 6, e19535 (2011).

    ADS  CAS  Article  Google Scholar 

  12. 12.

    Sol, D., González-Lagos, C., Lapiedra, O. & Díaz, M. in Ecology and Conservation of Birds in Urban Environments (eds Murgui, E. & Hedblom, M.) 75–89 (Springer, 2017).

  13. 13.

    Duncan, R. & Forsyth, D. in Conceptual Ecology and Invasion Biology: Reciprocal Approaches to Nature (eds Cadotte, M. W. et al.) 405–421 (Springer, 2006).

  14. 14.

    Peterson, A. T. Predicting the geography of species’ invasions via ecological niche modeling. Q. Rev. Biol. 78, 419–433 (2003).

    Article  Google Scholar 

  15. 15.

    Wagner, V. et al. Alien plant invasions in European woodlands. Divers. Distrib. 23, 969–981 (2017).

    Article  Google Scholar 

  16. 16.

    Duncan, R. P., Blackburn, T. M. & Sol, D. The ecology of bird introductions. Annu. Rev. Ecol. Evol. Syst. 34, 71–98 (2003).

    Article  Google Scholar 

  17. 17.

    Jeschke, J. M. et al. Support for major hypotheses in invasion biology is uneven and declining. NeoBiota 14, 1–20 (2012).

    Article  Google Scholar 

  18. 18.

    Pyšek, P. & Richardson, D. M. The biogeography of naturalization in alien plants. J. Biogeogr. 33, 2040–2050 (2006).

    Article  Google Scholar 

  19. 19.

    Jeschke, J. M. Across islands and continents, mammals are more successful invaders than birds. Divers. Distrib. 14, 913–916 (2008).

    Article  Google Scholar 

  20. 20.

    Veran, S. et al. Modeling spatial expansion of invasive alien species: relative contributions of environmental and anthropogenic factors to the spreading of the harlequin ladybird in France. Ecography 39, 665–675 (2016).

    Article  Google Scholar 

  21. 21.

    Case, T. J. Global patterns in the establishment and distribution of exotic birds. Biol. Conserv. 78, 69–96 (1996).

    Article  Google Scholar 

  22. 22.

    Cassey, P., Blackburn, T. M., Sol, D., Duncan, R. P. & Lockwood, J. L. Global patterns of introduction effort and establishment success in birds. Proc. R. Soc. Lond. B 271, S405–S408 (2004).

    Google Scholar 

  23. 23.

    Rue, H., Martino, S. & Chopin, N. Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. J. R. Stat. Soc. B 71, 319–392 (2009).

    MathSciNet  Article  Google Scholar 

  24. 24.

    Ameca y Juárez, E. I. A., Mace, G. M., Cowlishaw, G. & Pettorelli, N. Natural population die-offs: causes and consequences for terrestrial mammals. Trends Ecol. Evol. 27, 272–277 (2012).

    Article  Google Scholar 

  25. 25.

    Cheke, A. Seafaring behaviour in house crows Corvus splendens–a precursor to ship-assisted dispersal? Phelsuma 16, 65–68 (2008).

    Google Scholar 

  26. 26.

    Fridley, J. D. et al. The invasion paradox: reconciling pattern and process in species invasions. Ecology 88, 3–17 (2007).

    CAS  Article  Google Scholar 

  27. 27.

    Allen, W. L., Street, S. E. & Capellini, I. Fast life history traits promote invasion success in amphibians and reptiles. Ecol. Lett. 20, 222–230 (2017).

    Article  Google Scholar 

  28. 28.

    Sæther, B.-E. et al. Life-history variation predicts the effects of demographic stochasticity on avian population dynamics. Am. Nat. 164, 793–802 (2004).

    Article  Google Scholar 

  29. 29.

    Hayes, K. R. & Barry, S. C. Are there any consistent predictors of invasion success? Biol. Inv. 10, 483–506 (2008).

    Article  Google Scholar 

  30. 30.

    Duncan, R. P., Blackburn, T. M., Rossinelli, S. & Bacher, S. Quantifying invasion risk: the relationship between establishment probability and founding population size. Methods Ecol. Evol. 5, 1255–1263 (2014).

    Article  Google Scholar 

  31. 31.

    Dyer, E. E. et al. The global distribution and drivers of alien bird species richness. PLoS Biol. 15, e2000942 (2017).

    Article  Google Scholar 

  32. 32.

    Simberloff, D. & Von Holle, B. Positive interactions of nonindigenous species: invasional meltdown? Biol. Inv. 1, 21–32 (1999).

    Article  Google Scholar 

  33. 33.

    Myhrvold, N. P. et al. An amniote life-history database to perform comparative analyses with birds, mammals, and reptiles. Ecology 96, 3109 (2015).

    Article  Google Scholar 

  34. 34.

    Tacutu, R. et al. Human Ageing Genomic Resources: integrated databases and tools for the biology and genetics of ageing. Nucleic Acids Res. 41, D1027–D1033 (2013).

    CAS  Article  Google Scholar 

  35. 35.

    Wilman, H. et al. EltonTraits 1.0: species-level foraging attributes of the world’s birds and mammals. Ecology 95, 2027 (2014).

    Article  Google Scholar 

  36. 36.

    Sayol, F. et al. Environmental variation and the evolution of large brains in birds. Nat. Commun. 7, 13971 (2016).

    ADS  CAS  Article  Google Scholar 

  37. 37.

    Sol, D., Duncan, R. P., Blackburn, T. M., Cassey, P. & Lefebvre, L. Big brains, enhanced cognition, and response of birds to novel environments. Proc. Natl Acad. Sci. USA 102, 5460–5465 (2005).

    ADS  CAS  Article  Google Scholar 

  38. 38.

    Kissling, W. D., Sekercioglu, C. H. & Jetz, W. Bird dietary guild richness across latitudes, environments and biogeographic regions. Glob. Ecol. Biogeogr. 21, 328–340 (2012).

    Article  Google Scholar 

  39. 39.

    BirdLife International. IUCN Red List Bird Habitat Classifications http://www.iucnredlist.org (2017).

  40. 40.

    Simpson, E. H. Measurement of diversity. Nature 163, 688 (1949).

    ADS  Article  Google Scholar 

  41. 41.

    Hastings, D. A. et al. (eds) The Global Land One-Kilometer Base Elevation (Globe) Digital Elevation Model. Version 1.0 http://www.ngdc.noaa.gov/mgg/topo/globe.html (National Oceanic and Atmospheric Administration, National Geophysical Data Center, 1999) (2015).

  42. 42.

    Hijmans, R. J. et al. raster: Geographic Data Analysis and Modeling. https://cran.r-project.org/web/packages/raster/index.html (2016).

  43. 43.

    Hijmans, R. et al. WorldClim. Version 1.3 (Univ. California, Berkeley, 2005).

    Google Scholar 

  44. 44.

    Weigelt, P., Jetz, W. & Kreft, H. Bioclimatic and physical characterization of the world’s islands. Proc. Natl Acad. Sci. USA 110, 15307–15312 (2013).

    ADS  CAS  Article  Google Scholar 

  45. 45.

    Brohan, P., Kennedy, J. J., Harris, I., Tett, S. F. & Jones, P. D. Uncertainty estimates in regional and global observed temperature changes: a new data set from 1850. J. Geophys. Res. Atmos. 111, D12106 (2006).

    ADS  Article  Google Scholar 

  46. 46.

    Hurtt, G. C. et al. Harmonization of land-use scenarios for the period 1500–2100: 600 years of global gridded annual land-use transitions, wood harvest, and resulting secondary lands. Clim. Change 109, 117 (2011).

    Article  Google Scholar 

  47. 47.

    Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. & Mooers, A. O. The global diversity of birds in space and time. Nature 491, 444–448 (2012).

    ADS  CAS  Article  Google Scholar 

  48. 48.

    Blangiardo, M., Cameletti, M., Baio, G. & Rue, H. Spatial and spatio-temporal models with R-INLA. Spat. Spatiotemporal Epidemiol. 7, 39–55 (2013).

    Article  Google Scholar 

  49. 49.

    Watanabe, S. Asymptotic equivalence of Bayes cross validation and widely applicable information criterion in singular learning theory. J. Mach. Learn. Res. 11, 3571–3594 (2010).

    MathSciNet  MATH  Google Scholar 

  50. 50.

    Spiegelhalter, D. J., Best, N. G., Carlin, B. P. & van der Linde, A. The deviance information criterion: 12 years on. J. R. Stat. Soc. B 76, 485–493 (2014).

    MathSciNet  Article  Google Scholar 

  51. 51.

    Gelman, A., Hwang, J. & Vehtari, A. Understanding predictive information criteria for Bayesian models. Stat. Comput. 24, 997–1016 (2014).

    MathSciNet  Article  Google Scholar 

  52. 52.

    Sullivan, B. L. et al. eBird: A citizen-based bird observation network in the biological sciences. Biol. Conserv. 142, 2282–2292 (2009).

    Article  Google Scholar 

  53. 53.

    Raftery, A. E. Bayesian model selection in social research. Sociol. Methodol. 25, 111–163 (1995).

    Article  Google Scholar 

  54. 54.

    Revell, L. J. phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).

    Article  Google Scholar 

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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).

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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.

Corresponding author

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

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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