Forestry trial data can be used to evaluate climate-based species distribution models in predicting tree invasions

Rethabile Motloung,John Wilson,Mark Robertson,Mathieu Rouget

doi:10.3897/neobiota.20.5778

Abstract

Climate is frequently used to predict the outcome of species introductions based on the results from species distribution models (SDMs). However, despite the widespread use of SDMs for pre- and post-border risk assessments, data that can be used to validate predictions is often not available until after an invasion has occurred. Here we explore the potential for using historical forestry trials to assess the performance of climate-based SDMs. SDMs were parameterized based on the native range distribution of 36 Australian acacias, and predictions were compared against both the results of 150 years of government forestry trials, and current invasive distribution in southern Africa using true skill statistic, sensitivity and specificity. Classification tree analysis was used to evaluate why some Australian acacias failed in trials while others were successful. Predicted suitability was significantly related to the invaded range (sensitivity = 0.87) and success in forestry trials (sensitivity = 0.80), but forestry trial failures were under-predicted (specificity = 0.35). Notably, for forestry trials, the success in trials was greater for species invasive somewhere in the world. SDM predictions also indicate a considerable invasion potential of eight species that are currently naturalized but not yet widespread. Forestry trial data clearly provides a useful additional source of data to validate and refine SDMs in the context of risk assessment. Our study identified the climatic factors required for successful invasion of acacias, and accentuates the importance of integration of status elsewhere for risk assessment.

Highlights

Predicting which species will escape from forestry plantations and become invasive remains a challenge in invasion biology (Daehler et al 2004)
The species distribution models (SDMs) successfully predicted the outcome for 81 of the 129 forestry trials (63%), with a high percentage of true presences predicted as present but a rather low percentage of true absences predicted as absent
Observed invasive ranges of Australian acacias in southern Africa are generally correctly predicted as suitable based on the SDMs

Summary

Introduction

Predicting which species will escape from forestry plantations and become invasive remains a challenge in invasion biology (Daehler et al 2004). Such prediction is an essential requirement for proactive management (Ficetola et al 2007). Climate plays a fundamental role in determining species distributions (Gaston 2003), and the predictive success of invasive risk assessments is still largely a function of invasiveness elsewhere and climate suitability (Hulme 2012). Species distribution models (SDMs) have been widely used to predict invasions (Elith and Leathwick 2009; Pauchard et al 2004; Peterson 2003; Zhu et al 2007). SDMs have considerable potential in risk assessment but they are seldom tested in predicting successful tree establishments but see Nuñez and Medley (2011)

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