Presence—Absence versus Invasive Status Data for Modelling Potential Distribution of Invasive Plants: Saltcedar in Argentina

Spontaneous populations of saltcedars are widely distributed in Argentina. The invasive behaviour of the genus has been documented in the USA, Mexico, and Australia, where its presence is associated with significant changes in ecosystem functioning and the structure of natural communities. Previous...

Full description

Saved in:
Bibliographic Details
Published in:Écoscience (Sainte-Foy) Vol. 20; no. 2; pp. 161 - 171
Main Authors: Natale, Evangelina, Zalba Sergio Martin, Reinoso Herminda
Format: Journal Article
Language:English
Published: Université Laval 2013
Subjects:
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Spontaneous populations of saltcedars are widely distributed in Argentina. The invasive behaviour of the genus has been documented in the USA, Mexico, and Australia, where its presence is associated with significant changes in ecosystem functioning and the structure of natural communities. Previous to this work there were no studies assessing the potential of saltcedars as drivers of ecological change in Argentina. The aim of this work was to assess the potential distribution of saltcedars in the country in order to provide useful information for designing management strategies to reduce the impacts associated with their invasion. Known occurrences of the genus in Argentina were used to predict its potential distribution by applying different distribution models using both presence/absence and presence-only data. The DOMAIN model was the model that performed best once sensitivity and omission errors were taken into account. Our results indicate the severity of the problem of saltcedar in Argentina, with more than three quarters of the total arid and semiarid area vulnerable to invasion. Our results also highlight the need to include information about the status of populations when selecting training points for the development of distribution models.
Bibliography:http://dx.doi.org/10.2980%2F20-2-3571
ISSN:1195-6860
2376-7626