The unabating rise in the number of species introduced outside of their native range makes predicting the spread of alien species an urgent challenge. Most predictions use models of the ecological niche of a species to identify suitable areas for invasion, but these predictions may have limited accuracy. Here, using the global alien avifauna, we demonstrate an alternative approach for predicting alien spread based on the environmental resistance of the landscape. This approach does not require any information on the ecological niche of the invading species and, instead, uses gradients of biotic similarity among native communities in the invaded region to predict the most likely routes of spread. We show that environmental resistance predicts patterns of spread better than a null model of random dispersal or a model based on climate matching to the native range of each species. Applying this approach to simulate future spread reveals major regional differences in projected invasion risk, shaped by proximity to existing invasion hotspots as well as gradients in environmental resistance. Our results show how environmental resistance may provide a general and complementary approach for predicting invasion risk that can be rapidly deployed even when information on the niche or the identity of potential invaders is unknown.
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All data used in the analysis is available at Figshare (https://doi.org/10.6084/m9.figshare.13270406). Geographical range data for native and alien birds are available from the BirdLife International Data Zone (http://www.datazone.birdlife.org/) and the GAVIA database (https://doi.org/10.1038/sdata.2017.41), respectively. Climate data are available from WorldClim (https://doi.org/10.1002/joc.1276).
The code to conduct range spread simulations and to reproduce the analyses is available at Figshare (https://doi.org/10.6084/m9.figshare.13270406).
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A.L.P. is supported by a Royal Society University Research Fellowship. Initial funding for data collection was provided by a grant from the Leverhulme Trust (no. RF/2/RFG/2010/0016, to E.E.D.), with additional support from a UCL IMPACT studentship (no. 10989, to E.E.D.), a Leverhulme Trust grant (no. RPG-2015-392, to T.M.B., A.L.P. and E.E.D.) and from a King Saud University Distinguished Scientist Research Fellowship (to T.M.B. and E.E.D.).
The authors declare no competing interests.
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Flowchart illustrating the four types of models used to predict alien spread: Random Dispersal Model, Environmental Resistance (ER) Model, Climate Resistance Model, and Climate Matching Model. Grids show hypothetical probability surfaces used to model spread. The red square indicates the cell where the alien population established. Darker colours indicate that a cell has a higher probability of being invaded. Under the random dispersal model, alien spread is purely stochastic and all cells have an equal probability of being invaded. The other three models use conditions in either the invading species’ alien (red) or native (blue) ranges to guide dispersal. The environmental resistance model uses an invasion probability based on the distributions of native species in the invaded region. The native species with ranges overlapping the cell of alien population establishment are identified, and these species’ ranges are used to calculate the environmental resistance (1 – biotic similarity) to all other cells in the landscape. Biotic similarity is here taken as the proportion of native bird species in the establishment cell that are present at each other cell (yellow box). The climate resistance model calculates the probability of invasion based on the similarity of each cell’s climatic conditions to the conditions within the cell of alien population establishment. Under the climate matching model, invasion probability is determined by a cell’s climatic similarity to the average conditions occupied in the invading species’ native range.
Extended Data Fig. 2 The optimal combination of principal component climate axes for predicting spread in native (rows) and alien (columns) ranges.
For the n = 283 alien species which have spread beyond the cells of initial establishment, the set of climate axes best predicting spread in each alien range fragment (n = 1278) were identified, as well as the set of climate axes best predicting that species’ native range. Each cell in the matrix defined by position (x,y), indicates the number of cases where x is the best model in an alien range fragment and y is the best model in the native range. Diagonal elements indicate cases where the same set of climate axes limit spread in the native and alien ranges of a species. In cases where multiple sets of climate axes predicted spread equally well, fragments were assigned to multiple cells in the matrix but weighted by the inverse of the number of ties. Thus, the sum of the entries in the matrix is equal to the total number of alien range fragments.
For three exemplar focal cells in the (a) Amazon, (b) Sahel and (c) Siberia (Fig. 1), the environmental resistance and climate distance to all other cells was calculated. Environmental resistance is 1-biotic similarity, where biotic similarity is the proportion of species present in the focal cell that are also present in each other cell. Climate distance is the Euclidean distance in four-dimensional climate space between the conditions in the focal cell and each other cell. Bivariate plots show the relationship between the climatic distance and the environmental resistance of each cell relative to the focal cell, and these patterns are shown on the maps below.
For each species (n = 283), the model with the highest predictive accuracy for the species’ alien range was identified from the following models: random dispersal, environmental resistance, climate matchingOptimal, climate matchingNative, climate resistanceOptimal, climate resistanceNative (Supplementary Table 1). Predictive accuracy was rounded to two decimal places before comparing models to prevent detecting marginal differences in accuracy. Where multiple models had the same predictive accuracy, that species was assigned to multiple models but weighted by the inverse of the number of ties. Thus, the sum of the entries is equal to the total number of species.
Extended Data Fig. 5 Optimal environmental resistance threshold (ERT) for predicting the extent of alien spread at the level of species’ range fragments (n = 1704).
(a) the root-mean-squared error (r.m.s.e.) between simulated and observed alien range size under different ERT values; (b) the optimal ERT for predicting the size of each alien range fragment; (c) for different ERT values, the root-mean-squared error (r.m.s.e.Quantile) between the line of unity and the upper boundary of the relationship between simulated and observed alien range size obtained from a quantile regression (0.99 percentile). In (a,c) the grey dashed line indicates the ERT minimising error.
Extended Data Fig. 6 Predicted against observed range size for different environmental resistance threshold (ERT) values.
Red regions indicate range sizes that are under-predicted. Lines indicate the quantile regression (0.99 percentile) of simulated against observed alien range size. Range size was calculated for individual fragments of species’ ranges (n = 1704 fragments, left column) or for total species range size (n = 339 species, right column). Range size is the number of occupied ~100 km grid cells.
(a) relative probability of invasion as a function of environmental resistance for different values of µ; (b) mean predictive accuracy across species (n = 283) of the environmental resistance model under different values of µ. When µ is high (for example µ = 30), patterns of spread are strongly deterministic. When µ = 0, patterns of spread are independent of environmental resistance, corresponding to the null model of random dispersal. Increasing µ above 30 (marked with an arrow) only resulted in a small increase in model predictive accuracy.
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Lovell, R.S.L., Blackburn, T.M., Dyer, E.E. et al. Environmental resistance predicts the spread of alien species. Nat Ecol Evol 5, 322–329 (2021). https://doi.org/10.1038/s41559-020-01376-x
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