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Hotspots of plant invasion predicted by propagule pressure and ecosystem characteristics

Hotspots of plant invasion predicted by propagule pressure and ecosystem characteristics
Hotspots of plant invasion predicted by propagule pressure and ecosystem characteristics
Aim: Biological invasions pose a major conservation threat and are occurring at an unprecedented rate. Disproportionate levels of invasion across the landscape indicate that propagule pressure and ecosystem characteristics can mediate invasion success. However, most invasion predictions relate to species’ characteristics (invasiveness) and habitat requirements. Given myriad invaders and the inability to generalize from single-species studies, more general predictions about invasion are required. We present a simple new method for characterizing and predicting landscape susceptibility to invasion that is not species-specific.

Location:? Corangamite catchment (13,340 km2), south-east Australia.

Methods:? Using spatially referenced data on the locations of non-native plant species, we modelled their expected proportional cover as a function of a site’s environmental conditions and geographic location. Models were built as boosted regression trees (BRTs).

Results:? On average, the BRTs explained 38% of variation in occupancy and abundance of all exotic species and exotic forbs. Variables indicating propagule pressure, human impacts, abiotic and community characteristics were rated as the top four most influential variables in each model. Presumably reflecting higher propagule pressure and resource availability, invasion was highest near edges of vegetation fragments and areas of human activity. Sites with high vegetation cover had higher probability of occupancy but lower proportional cover of invaders, the latter trend suggesting a form of biotic resistance. Invasion patterns varied little in time despite the data spanning 34 years.

Main conclusions:? To our knowledge, this is the first multispecies model based on occupancy and abundance data used to predict invasion risk at the landscape scale. Our approach is flexible and can be applied in different biomes, at multiple scales and for different taxonomic groups. Quantifying general patterns and processes of plant invasion will increase understanding of invasion and community ecology. Predicting invasion risk enables spatial prioritization of weed surveillance and control.
1366-9516
1099-1110
Catford, Jane A.
13355676-9979-4a37-8b90-5ffe4080286a
Vesk, Peter A.
1378d4ce-a258-46f9-a602-26feed41d72f
White, Matt D.
4b1c7d91-ffdc-4286-967a-7635bac0d035
Wintle, Brendan A.
82c276b0-e4ca-4292-8859-3b711f769ea5
Catford, Jane A.
13355676-9979-4a37-8b90-5ffe4080286a
Vesk, Peter A.
1378d4ce-a258-46f9-a602-26feed41d72f
White, Matt D.
4b1c7d91-ffdc-4286-967a-7635bac0d035
Wintle, Brendan A.
82c276b0-e4ca-4292-8859-3b711f769ea5

Catford, Jane A., Vesk, Peter A., White, Matt D. and Wintle, Brendan A. (2011) Hotspots of plant invasion predicted by propagule pressure and ecosystem characteristics. Diversity and Distributions, 17 (6), 1099-1110. (doi:10.1111/j.1472-4642.2011.00794.x).

Record type: Article

Abstract

Aim: Biological invasions pose a major conservation threat and are occurring at an unprecedented rate. Disproportionate levels of invasion across the landscape indicate that propagule pressure and ecosystem characteristics can mediate invasion success. However, most invasion predictions relate to species’ characteristics (invasiveness) and habitat requirements. Given myriad invaders and the inability to generalize from single-species studies, more general predictions about invasion are required. We present a simple new method for characterizing and predicting landscape susceptibility to invasion that is not species-specific.

Location:? Corangamite catchment (13,340 km2), south-east Australia.

Methods:? Using spatially referenced data on the locations of non-native plant species, we modelled their expected proportional cover as a function of a site’s environmental conditions and geographic location. Models were built as boosted regression trees (BRTs).

Results:? On average, the BRTs explained 38% of variation in occupancy and abundance of all exotic species and exotic forbs. Variables indicating propagule pressure, human impacts, abiotic and community characteristics were rated as the top four most influential variables in each model. Presumably reflecting higher propagule pressure and resource availability, invasion was highest near edges of vegetation fragments and areas of human activity. Sites with high vegetation cover had higher probability of occupancy but lower proportional cover of invaders, the latter trend suggesting a form of biotic resistance. Invasion patterns varied little in time despite the data spanning 34 years.

Main conclusions:? To our knowledge, this is the first multispecies model based on occupancy and abundance data used to predict invasion risk at the landscape scale. Our approach is flexible and can be applied in different biomes, at multiple scales and for different taxonomic groups. Quantifying general patterns and processes of plant invasion will increase understanding of invasion and community ecology. Predicting invasion risk enables spatial prioritization of weed surveillance and control.

Full text not available from this repository.

More information

e-pub ahead of print date: 20 June 2011
Published date: November 2011
Organisations: Environmental

Identifiers

Local EPrints ID: 400863
URI: https://eprints.soton.ac.uk/id/eprint/400863
ISSN: 1366-9516
PURE UUID: 885c307b-b126-4fa9-8040-e53b04249cf0

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Date deposited: 30 Sep 2016 12:18
Last modified: 17 Jul 2017 18:08

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