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2018 Vol.43, Issue 4 Preview Page

Original Article

Effectiveness of Sensitivity Analysis for Parameter Selection in CLIMEX Modeling of Metcalfa pruinosa Distribution
Dae-hyeon Byeon1
Sunghoon Jung2
Changyeun Mo3
Wang-Hee Lee1*
1Department of Biosystems Machinery Engineering, Chungnam National University, Daejeon, 34134, Korea
2Department of Applied Biology, Chungnam National University, Daejeon, 34134, Korea
3National Institute of Agricultural Sciences, Rural Development Administration, Jeonju, Jeollabuk-do, 54875, Korea
* Corresponding Author
December 2018. pp. 410-419
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Abstract

Purpose: CLIMEX, a species distribution modeling tool, includes various types of parameters representing climatic conditions; the estimation of these parameters directly determines the model accuracy. In this study, we investigated the sensitivity of parameters for the climatic suitability calculated by CLIMEX for Metcalfa pruinosain South Korea. Methods: We first changed 12 parameters and identified the three significant parameters that considerably affected the CLIMEX simulation response. Results: The result indicated that the simulation was highly sensitive to changes in lower optimal temperatures, lower soil moisture thresholds, and cold stress accumulation rate based on the sensitivity index, suggesting that these were the fundamental parameters to be used for fitting the simulation into the actual distribution. Conclusion: Sensitivity analysis is effective for estimating parameter values, and selecting the most important parameters for improving model accuracy.

Keywords
CLIMEX
Metcalfa pruinosa
Parameter estimation
Sensitivity analysis
Species distribution modeling
References
1
Ahn, K. S., G. S. Lee, K. H. Lee, M. K. Song, S. C. Lim and G. H. Kim. 2011. Susceptibility of North American planthopper, Metcalfa pruinosa to commercially registered insecticides in Korea. The Korean Journal of Pesticide Science 15(3): 329-334 (In Korean, with English abstract).
2
Andrewartha, H. G. and L. C. Birch. 1954. The Distribution and Abundance of Animals, Chicago, IL, USA: University of Chicago Press.
10.1086/401497
3
Bale, J. S., G. J. Masters, I. D. Hodkinson, C. Awmack, T. M. Bezemer, V. K. Brown, J. Butterfield, A. Buse, J. C. Coulson, J. Farrar, J. E. Good, R. Harrington, S. Hartley, T. H. Jones, R. L. Lindroth, M. C. Press, I. Symrnioudis, A. D. Watt and J. B. Whittaker. 2002. Herbivory in global climate change research: direct effects of rising temperature on insect herbivores. Global Change Biology 8(1): 1-16.
10.1046/j.1365-2486.2002.00451.x
4
Barry, S. and J. Elith. 2006. Error and uncertainty in habitat models. Journal of Applied Ecology 43(3): 413-423.
10.1111/j.1365-2664.2006.01136.x
5
Bennett, S. J., N. Said and D. Enneking. 1998. Modelling climatic similarities in Mediterranean areas: a potential tool for genetic resources and breeding programmes. Agriculture, Ecosystems & Environment 70(2-3): 129-143.
10.1016/s0167-8809(98)00121-2
6
Burgman, M. A., D. B. Lindenmayer and J. Elith. 2005. Managing landscapes for conservation under uncertainty. Ecology 86(8): 2007-2017.
10.1890/04-0906
7
Busby, J. R. 1991. BIOCLIM-a bioclimate analysis and prediction system. Plant Protection Quarterly 6: 8-9.
8
Byeon, D. H., J. M. Jung, S. Jung and W. H. Lee. 2018. Prediction of global geographic distribution of Metcalfa pruinosa using CLIMEX. Entomological Research 48(2): 99-107.
10.1111/1748-5967.12253
9
Byeon, D. H., J. M. Jung, S. Lohumi, B. K. Cho, S. Jung and W. H. Lee. 2017. Predictive analysis of Metcalfa pruinosa (Hemiptera: Flatidae) distribution in South Korea using CLIMEX software. Journal of Asia-Pacific Biodiversity 10(3): 379-384.
10.1016/j.japb.2017.06.004
10
Dixon, A. F. G. 1998. Aphid Ecology, 2nd edn., London., UK: Chapman & Hall.
11
Elith, J. and J. R. Leathwick. 2009. Species distribution models: ecological explanation and prediction across space and time. Annual Review of Ecology, Evolution, and Systematics 40(1): 677-697.
10.1146/annurev.ecolsys.110308.120159
12
GBIF.org (29th February 2016) GBIF Occurrence Download
10.15468/dl.ywhpmz
13
Gurevitch, J. and D. K. Padilla. 2004. Are invasive species a major cause of extinctions?. Trends in Ecology & Evolution 19(9): 470-474.
10.1016/j.tree.2004.07.005
14
Hallgren, W. S. and A. J. Pitman. 2000. The uncertainty in simulations by a Global Biome Model (BIOME3) to alternative parameter values. Global Change Biology 6(5): 483-495.
10.1046/j.1365-2486.2000.00325.x
15
Hamby, D. M. 1994. A review of techniques for parameter sensitivity analysis of environmental models. Environmental Monitoring & Assessment 32(2): 135-154.
10.1007/bf00547132
16
Hanspach, J., I. Ku¨hn, O. Schweiger, S. Pompe and S. Klotz. 2011. Geographical patterns in prediction errors of species distribution models. Global Ecology and Biogeography 20(5): 779-788.
10.1111/j.1466-8238.2011.00649.x
17
Hoffman, E. O. and R. H. Gardner. 1983. Evaluation of Uncertainties in Environmental Radiological Assessment Models. In: Radiological Assessments: A Textbook on Environmental Dose Assessment, eds. J. E. Till and H. R Meyer, 11.1-11.47. Washington, DC, USA: Nuclear Regulatory Commission.
18
Jung, J. M., S. Jung, M. R. Ahmed, B. K. Cho and W. H. Lee. 2017a. Invasion risk of the yellow crazy ant (Anoplolepis gracilipes) under the Representative Concentration Pathways 8.5 climate change scenario in South Korea. Journal of Asia-Pacific Biodiversity 10(4): 548-554.
10.1016/j.japb.2017.08.004
19
Jung, J. M., J. W. Lee, C. J. Kim, S. Jung and W.H. Lee. 2017b. CLIMEX-based Analysis of Potential Geographical Distribution of Aedes albopictus and Aedes aegypti in South Korea. Journal of Biosystems Engineering 42(3): 217-226.
10.5307/JBE.2017.42.3.217
20
Jung, J. M., W. H. Lee and S. Jung. 2016. Insect distribution in response to climate change based on a model: Review of function and use of CLIMEX. Entomological Research 46(4): 223-235.
10.1111/1748-5967.12171
21
Kim, D. E. and J. Kil. 2014. Occurrence and host plant of Metcalfa pruinosa (Say) (Hemiptera: Flatidae) in Korea. Journal of Environmental Science International 23(8): 1385-1394.
10.5322/jesi.2014.23.8.1385
22
Kim, Y. E., M. Y. Kim, K. J. Hong and S. H. Lee. 2011. Outbreak of an exotic flatid, Metcalfa pruinosa (Say) (Hemiptera: Flatidae), in the capital region of Korea. Journal of Asia-Pacific Entomology 14(4): 473-478.
10.1016/j.aspen.2011.06.002
23
Kriticos, D. J., G. F. Maywald, T. Yonow, E. J. Zurcher, N. I. Herrmann and R. W. Sutherst. 2015. CLIMEX Version 4: Exploring The Effects of Climate on Plants, Animals and Diseases. Canberra, Australia: CSIRO.
24
Lapointe, S. L. and J. P. Shapiro. 1999. Effect of soil moisture on development of Diaprepes abbreviatus (Coleoptera: Curculionidae). Florida Entomologist 82(2): 291-299.
10.2307/3496582
25
Lee, C. B. and J. C. Kim. 2007. Aspect and cause of long term trend of climate in Ulleungdo and Dokdo Area. Korean Journal of Nature Conservation 1(1): 19-28 (In Korean, with English abstract).
26
Lee, W.-H. and M. R. Okos. 2016. Model-based analysis of IGF-1 effect on osteoblast and osteoclast regulation in bone turnover. Journal of Biological Systems 24(1): 63-89.
10.1142/s0218339016500042
27
Mainali, K. P., D. L. Warren, K. Dhileepan, A. McConnachie, L. Strathie, G. Hassan, D. Karki, B. B. Shrestha and C. Parmesan. 2015. Projecting future expansion of invasive species: comparing and improving methodologies for species distribution modeling. Global Change Biology 21(12): 4464-4480.
10.1111/gcb.13038
28
Merow, C., N. LaFleur, J. A. Silander Jr, A. M. Wilson and M. Rubega. 2011. Developing dynamic mechanistic species distribution models: predicting bird-mediated spread of invasive plants acrosss Northeastern North America. The American Naturalist 178(1): 30-43.
10.1086/660295
29
Pattison, R. R. and R. N. Mack. 2008. Potential distribution of the invasive tree Triadica sebifera (Euphorbiaceae) in the United States: evaluating CLIMEX predictions with field trials. Global Change Biology 14(4): 813-826.
10.1111/j.1365-2486.2007.01528.x
30
Phillips, S. J. and M. Dudík. 2008. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31(2): 161-175.
10.1111/j.0906-7590.2008.5203.x
31
Poutsma, J., A. J. M. Loomans, B. Aukema and T. Heijerman. 2007. Predicting The Potential Geographical Distribution of The Harlequin Ladybird, Harmonia axyridis, Using The CLIMEX Model. In: From Biological Control to Invasion: The Ladybird Harmonia Axyridis as A Model Species, eds. H. E. Roy and E. Wajnberg, 103-125. Berlin/Heidelberg, Germany: Springer
10.1007/978-1-4020-6939-0_8
32
Shabani, F. and L. Kumar. 2014. Sensitivity analysis of CLIMEX parameters in modeling potential distribution of Phoenix dactylifera L.. PLoS ONE 9(4): e94867.
10.1371/journal.pone.0094867
33
Strauss, G. 2010. Pest risk analysis of Metcalfa pruinosa in Austria. Journal of Pest Science 83(4): 381-390.
10.1007/s10340-010-0308-3
34
Sutherst, R. W. and A. S. Bourne. 2009. Modelling non-equilibrium distributions of invasive species: a tale of two modelling paradigms. Biological Invasions 11(6): 1231-1237.
10.1007/s10530-008-9335-x
35
Taylor, S. and L. Kumar. 2012. Sensitivity analysis of CLIMEX parameters in modelling potential distribution of Lantana camara L., PLoS ONE 7(7): e40969.
10.1371/journal.pone.0040969
36
Thacker, J. I., T. Thieme and A.F.G. Dixon. 1997. Forecasting of periodic fluctuations in annual abundance of the bean aphid: the role of density dependence and weather. Journal of Applied Entomology 121(1-5): 137-145.
10.1111/j.1439-0418.1997.tb01383.x
37
Tortorelli, D. A. and P. Michaleris. 1994. Design sensitivity analysis: overview and review. Inverse Problems in Engineering 1(1): 71-105.
10.1080/174159794088027573
38
Van Klinken, R. D., B. E. Lawson and M. P. Zalucki. 2009. Predicting invasions in Australia by a Neotropical shrub under climate change: the challenge of novel climates and parameter estimation. Global Ecology and Biogeography 18(6): 688-700.
10.1111/j.1466-8238.2009.00483.x
39
Webber, B. L., C. J. Yates, D. C. Le Maitre, J. K. Scott, D. J. Kriticos, N. Ota, A. McNeill, J. J. Le Roux and G. F. Midgley. 2011. Modelling horses for novel climate courses: insights from projecting potential distributions of native and alien Australian acacias with correlative and mechanistic models. Diversity and Distributions 17(5): 978-1000.
10.1111/j.1472-4642.2011.00811.x
40
Zaehle, S., S. Sitch, B. Smith and F. Hatterman. 2005. Effects of parameter uncertainties on the modeling of terrestrial bio-sphere dynamics. Global Biogeochemical Cycles 19(3): GB3020.
10.1029/2004gb002395
Information
  • Publisher :The Korean Society for Agricultural Machinery
  • Publisher(Ko) :한국농업기계학회
  • Journal Title :Journal of Biosystems Engineering
  • Journal Title(Ko) :바이오시스템공학
  • Volume : 43
  • No :4
  • Pages :410-419
  • Received Date :2018. 09. 18
  • Revised Date :2018. 11. 13
  • Accepted Date : 2018. 11. 15
  • DOI :https://doi.org/10.5307/JBE.2018.43.4.410
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