2015
DOI: 10.3390/f6124374
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Species Distribution Model for Management of an Invasive Vine in Forestlands of Eastern Texas

Abstract: Abstract:Invasive plants decrease biodiversity, modify vegetation structure, and inhibit growth and reproduction of native species. Japanese honeysuckle (Lonicera japonica Thunb.) is the most prevalent invasive vine in the forestlands of eastern Texas. Hence, we aimed to identify potential factors influencing the distribution of the species, quantify the relative importance of each factor, and test possible management strategies. We analyzed an extensive dataset collected as part of the Forest Inventory and An… Show more

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Cited by 10 publications
(4 citation statements)
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“…Honeysuckle ( Lonicera japonica Thunb.) is a twining, semi-evergreen vine, distributed widely in temperate and tropical regions 33 , 34 . It is a popular landscape plant with high environmental adaptability 35 .…”
Section: Introductionmentioning
confidence: 99%
“…Honeysuckle ( Lonicera japonica Thunb.) is a twining, semi-evergreen vine, distributed widely in temperate and tropical regions 33 , 34 . It is a popular landscape plant with high environmental adaptability 35 .…”
Section: Introductionmentioning
confidence: 99%
“…Areas with high productivity and natural regeneration also should be targeted for prompt inspection and potential control measures in order to mitigate the ecological impacts of this aggressive understory invader. While there are no direct solutions for invasive plant problems, identifying potential casual factors of invasion can help improve management strategies to limit the spread of non-natives (Wang et al 2015 ). In this regard, our model could aid in the on-going development of control strategies for confronting Japanese stiltgrass invasions by identifying vulnerable areas that might emerge as a result of changes in climatic conditions and land use patterns.…”
Section: Discussionmentioning
confidence: 99%
“…Once the optimal combination of learning rate and tree complexity was found, model performance was evaluated using a tenfold crossvalidation procedure with re-substitution. For each cross-validation trial, 60% of the dataset was randomly selected for model fitting, and the excluded 40% was used for testing, following the recommendation of Wang et al [31]. We derived our optimal model in R 3.6.0 [32] using the gbm package version 1.5-7 [33].…”
Section: Data Processing and Analysismentioning
confidence: 99%