The effect of data sources and quality on the predictive capacity of CLIMEX models: An assessment of Teleonemia scrupulosa and Octotoma scabripennis for the biocontrol of Lantana camara in Australia |
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Authors: | Ben E Lawson Michael D Day Michiala Bowen Rieks D van Klinken Myron P Zalucki |
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Institution: | 1. School of Integrative Biology, University of Queensland, St. Lucia, Queensland 4072, Australia;2. Biosecurity Queensland, Department of Employment, Economic Development and Innovation, P.O. Box 36, Sherwood, Queensland 4075, Australia;3. School of Geography, Planning and Architecture, University of Queensland, St. Lucia, Queensland 4072, Australia;4. CSIRO Entomology, Long Pocket Laboratories, 120 Meiers Road, Indooroopilly, Queensland 4068, Australia;5. Cooperative Research Centre for Australian Weed Management, PMB 1, Waite Campus, Glen Osmond, South Australia, 5064, Australia |
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Abstract: | Understanding the effects of different types and quality of data on bioclimatic modeling predictions is vital to ascertaining the value of existing models, and to improving future models. Bioclimatic models were constructed using the CLIMEX program, using different data types – seasonal dynamics, geographic (overseas) distribution, and a combination of the two – for two biological control agents for the major weed Lantana camara L. in Australia. The models for one agent, Teleonemia scrupulosa Stål (Hemiptera: Tingidae) were based on a higher quality and quantity of data than the models for the other agent, Octotoma scabripennis Guérin-Méneville (Coleoptera: Chrysomelidae). Predictions of the geographic distribution for Australia showed that T. scrupulosa models exhibited greater accuracy with a progressive improvement from seasonal dynamics data, to the model based on overseas distribution, and finally the model combining the two data types. In contrast, O. scabripennis models were of low accuracy, and showed no clear trends across the various model types. These case studies demonstrate the importance of high quality data for developing models, and of supplementing distributional data with species seasonal dynamics data wherever possible. Seasonal dynamics data allows the modeller to focus on the species response to climatic trends, while distributional data enables easier fitting of stress parameters by restricting the species envelope to the described distribution. It is apparent that CLIMEX models based on low quality seasonal dynamics data, together with a small quantity of distributional data, are of minimal value in predicting the spatial extent of species distribution. |
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