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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All data generated or analysed during this study are included with the paper and its Supplementary Information.
Code used to calculate the final model is included in Supplementary Information.
Blackburn, T. M. et al. A proposed unified framework for biological invasions. Trends Ecol. Evol. 26, 333–339 (2011).
Lewis, S. L. & Maslin, M. A. Defining the Anthropocene. Nature 519, 171–180 (2015).
Pimentel, D. Biological Invasions: Economic and Environmental Costs of Alien Plant, Animal, and Microbe Species (CRC, Boca Raton, 2011).
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).
Seebens, H. et al. No saturation in the accumulation of alien species worldwide. Nat. Commun. 8, 14435 (2017).
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).
Drake, J. A. et al. Biological Invasions: A Global Perspective (John Wiley & Sons, Chichester, 1989).
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).
Lockwood, J. L., Cassey, P. & Blackburn, T. The role of propagule pressure in explaining species invasions. Trends Ecol. Evol. 20, 223–228 (2005).
Sol, D. et al. Unraveling the life history of successful invaders. Science 337, 580–583 (2012).
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).
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).
Duncan, R. & Forsyth, D. in Conceptual Ecology and Invasion Biology: Reciprocal Approaches to Nature (eds Cadotte, M. W. et al.) 405–421 (Springer, 2006).
Peterson, A. T. Predicting the geography of species’ invasions via ecological niche modeling. Q. Rev. Biol. 78, 419–433 (2003).
Wagner, V. et al. Alien plant invasions in European woodlands. Divers. Distrib. 23, 969–981 (2017).
Duncan, R. P., Blackburn, T. M. & Sol, D. The ecology of bird introductions. Annu. Rev. Ecol. Evol. Syst. 34, 71–98 (2003).
Jeschke, J. M. et al. Support for major hypotheses in invasion biology is uneven and declining. NeoBiota 14, 1–20 (2012).
Pyšek, P. & Richardson, D. M. The biogeography of naturalization in alien plants. J. Biogeogr. 33, 2040–2050 (2006).
Jeschke, J. M. Across islands and continents, mammals are more successful invaders than birds. Divers. Distrib. 14, 913–916 (2008).
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).
Case, T. J. Global patterns in the establishment and distribution of exotic birds. Biol. Conserv. 78, 69–96 (1996).
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).
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).
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).
Cheke, A. Seafaring behaviour in house crows Corvus splendens–a precursor to ship-assisted dispersal? Phelsuma 16, 65–68 (2008).
Fridley, J. D. et al. The invasion paradox: reconciling pattern and process in species invasions. Ecology 88, 3–17 (2007).
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).
Sæther, B.-E. et al. Life-history variation predicts the effects of demographic stochasticity on avian population dynamics. Am. Nat. 164, 793–802 (2004).
Hayes, K. R. & Barry, S. C. Are there any consistent predictors of invasion success? Biol. Inv. 10, 483–506 (2008).
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).
Dyer, E. E. et al. The global distribution and drivers of alien bird species richness. PLoS Biol. 15, e2000942 (2017).
Simberloff, D. & Von Holle, B. Positive interactions of nonindigenous species: invasional meltdown? Biol. Inv. 1, 21–32 (1999).
Myhrvold, N. P. et al. An amniote life-history database to perform comparative analyses with birds, mammals, and reptiles. Ecology 96, 3109 (2015).
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).
Wilman, H. et al. EltonTraits 1.0: species-level foraging attributes of the world’s birds and mammals. Ecology 95, 2027 (2014).
Sayol, F. et al. Environmental variation and the evolution of large brains in birds. Nat. Commun. 7, 13971 (2016).
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).
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).
BirdLife International. IUCN Red List Bird Habitat Classifications http://www.iucnredlist.org (2017).
Simpson, E. H. Measurement of diversity. Nature 163, 688 (1949).
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).
Hijmans, R. J. et al. raster: Geographic Data Analysis and Modeling. https://cran.r-project.org/web/packages/raster/index.html (2016).
Hijmans, R. et al. WorldClim. Version 1.3 (Univ. California, Berkeley, 2005).
Weigelt, P., Jetz, W. & Kreft, H. Bioclimatic and physical characterization of the world’s islands. Proc. Natl Acad. Sci. USA 110, 15307–15312 (2013).
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).
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).
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).
Blangiardo, M., Cameletti, M., Baio, G. & Rue, H. Spatial and spatio-temporal models with R-INLA. Spat. Spatiotemporal Epidemiol. 7, 39–55 (2013).
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).
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).
Gelman, A., Hwang, J. & Vehtari, A. Understanding predictive information criteria for Bayesian models. Stat. Comput. 24, 997–1016 (2014).
Sullivan, B. L. et al. eBird: A citizen-based bird observation network in the biological sciences. Biol. Conserv. 142, 2282–2292 (2009).
Raftery, A. E. Bayesian model selection in social research. Sociol. Methodol. 25, 111–163 (1995).
Revell, L. J. phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).
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).
The authors declare no competing interests.
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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).
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).
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).
The R code used to run the analyses reported in this manuscript.
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.
All the covariates considered in the analysis with proposed hypotheses, underlying justification and source.
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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