4.7 Article

Developing an approach to defining the potential distributions of invasive plant species:: a case study of Hakea species in South Africa

期刊

GLOBAL ECOLOGY AND BIOGEOGRAPHY
卷 17, 期 5, 页码 569-584

出版社

WILEY
DOI: 10.1111/j.1466-8238.2008.00407.x

关键词

false absence; Hakea; limiting factors; potential distribution; presence; Proteaceae; pseudo-absence; South Africa; species distribution models; true absence

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Aim Models of the potential distributions of invading species have to deal with a number of issues. The key one is the high likelihood that the absence of an invading species in an area is a false absence because it may not have invaded that area yet, or that it may not have been detected. This paper develops an approach for screening pseudo-absences in a way that is logical and defensible. Innovation The step-wise approach involves: (1) screening environmental variables to identify those most likely to indicate conditions where the species cannot invade; (2) identifying and selecting the most likely limiting variables; (3) using these to define the limits of its invasion potential; and (4) selecting points outside these limits as true absence records for input into species distribution models. This approach was adopted and used for the study of three prominent Hakea species in South Africa. Models with and without the false absence records were compared. Two rainfall variables and the mean minimum temperature of the coldest month were the strongest predictors of potential distributions. Models which excluded false absences predicted that more of the potential distribution would have a high invasion potential than those which included them. Main conclusions The approach of applying a priori knowledge can be useful in refining the potential distribution of a species by excluding pseudo-absence records which are likely to be due to the species not having invaded an area yet or being undetected. The differences between the potential distributions predicted by the different models convey more information than making a single prediction, albeit a consensus model. The robustness of this approach depends strongly on an adequate knowledge of the ecology, invasion history and current distribution of that species.

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