Importing risk: quantifying the propagule pressure–establishment relationship at the pathway level
Article first published online: 11 MAR 2013
© 2013 Blackwell Publishing Ltd
Diversity and Distributions
How to Cite
Bradie, J., Chivers, C., Leung, B. (2013), Importing risk: quantifying the propagule pressure–establishment relationship at the pathway level. Diversity and Distributions. doi: 10.1111/ddi.12081
- Article first published online: 11 MAR 2013
- Aquarium fish;
- biological invasions;
- establishment probability;
- non-indigenous species;
- pathway-level analysis;
- propagule pressure;
- risk assessment
To build and assess pathway-level non-indigenous species (NIS) establishment curves generated using a propagule pressure (PP) proxy and historical establishment data.
Our analysis examines the utility and behaviour of pathway-level NIS establishment curves that relate species-level PP to establishment probability. Using theoretical and empirical methods, we examine the behaviour of pathway-level establishment models when species are heterogeneous in their ability to establish. Next, we examine the implications of using PP proxy and historical establishment data to parameterize these models. Finally, we test the model by building an establishment curve for aquarium fish establishments in the United States using import data as a proxy for PP.
First, we show theoretically how species' heterogeneity and the use of a proxy metric for PP affect model parameterization and the interpretation of the establishment curve. Second, we demonstrate that import data are relatively consistent across space and time for aquarium fish species. Finally, we demonstrate how basic import-level data can improve our ability to predict which species are at risk of establishment using aquarium fish introductions to the United States as a case study.
Pathway-level analyses generated using species-level PP information can provide a snapshot of establishment probability for use in risk analyses without in-depth knowledge of species' abiotic and biotic interactions. Proxy data for PP can be a good metric for such analyses, and valid predictions can be expected when the PP data are relatively consistent across the time period for which establishments are recorded.