Predicting Invasion Probability from Botanic Gardens using Exotic Species Traits

Decky Indrawan Junaedi, Zaenal Mutaqien

Abstract


Preventative management, such as framework-based assessment, considered as the best option for invasive species management. Alternatively, risk assessment can be conducted based on traits of occurred invasive species to build prediction system for invasive risk assessment. This study aimed to test whether trait-based assessment system can differentiate the escaped from non-escaped exotic collections of botanic gardens and to compare the reliability of trait-based versus framework-based risk assessment on differentiating these escaped from non-escaped exotics. In this study, Bayesian logistic regression analysis was conducted to assess the reliability of framework-based and trait-based risk assessment systems. For trait-based system, clear effect of leaf trait, height, and dispersal method to escape probability was detected. For framework-based system, clear effect of Tropical Weed Risk Assessment Protocol on escape probability was detected. Leaf trait, dispersal method and height are reliable predictors for escaped probability of botanic gardens exotic collection. The fact that the reliability of trait-based assessment systems is better than the commonly used framework-based system is the main novel finding in this study. This finding implies that trait-based is better than framework-based for invasive species risk assessment approach in Indonesian botanic gardens. Trait-based assessment also a relevant tool to support management with limited resources to conduct adequate early risk assessment.

Keywords


Bayesian logistic regression; Tropical Invasion; Botanic Gardens; Specific Leaf Area risk assessment; WRA



DOI: https://doi.org/10.15294/biosaintifika.v10i3.15500

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Biosaintifika: Journal of Biology & Biology Education by Department of Biology, Faculty of Mathematics and Natural Sciences, Universitas Negeri Semarang is licensed under a Creative Commons Attribution 4.0 International Licensep-ISSN (Print) 2085-191X | e-ISSN (Online) 2338-7610