Evidence map›Paper›PMID 33495593›Full record

ArticleNature ecology & evolution2021

Environmental resistance predicts the spread of alien species.

Rebecca S L Lovell, Tim M Blackburn, Ellie E Dyer, Alex L Pigot

Abstract read
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Article in Nature ecology & evolution, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Rebecca S L LovellCentre for Biodiversity and Environment Research, Department of Genetics, Evolution and Environment, University College London, London, UK. Rebecca.Lovell.15@ucl.ac.uk.ORCID http://orcid.org/0000-0003-1776-1124
Tim M BlackburnCentre for Biodiversity and Environment Research, Department of Genetics, Evolution and Environment, University College London, London, UK.ORCID http://orcid.org/0000-0003-0152-2663
Ellie E DyerCentre for Biodiversity and Environment Research, Department of Genetics, Evolution and Environment, University College London, London, UK.
Alex L PigotCentre for Biodiversity and Environment Research, Department of Genetics, Evolution and Environment, University College London, London, UK.ORCID http://orcid.org/0000-0002-6490-6261

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

EcosystemIntroduced SpeciesClimate

Identifiers

PMID33495593

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.