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Selection of models to describe the temperature-dependent development of Neoleucinodes elegantalis (Lepidoptera: Crambidae) and its application to predict the species voltinism under future climate conditions

Published online by Cambridge University Press:  05 April 2021

Hevellyn Talissa dos Santos  and
Cesar Augusto Marchioro Open the ORCID record for Cesar Augusto Marchioro [Opens in a new window]
Hevellyn Talissa dos Santos
Affiliation:
Federal University of Santa Catarina, Campus Curitibanos, Curitibanos, Santa Catarina, Brazil
Cesar Augusto Marchioro*
Affiliation:
Department of Agriculture, Biodiversity and Forests, Post-graduate Programme in Agricultural and Natural Ecosystem, Federal University of Santa Catarina, Campus Curitibanos, Curitibanos, Santa Catarina, Brazil
*
Author for correspondence: Cesar Augusto Marchioro, Email: [email protected]
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Abstract

The small tomato borer, Neoleucinodes elegantalis (Guenée, 1854) is a multivoltine pest of tomato and other cultivated solanaceous plants. The knowledge on how N. elegantalis respond to temperature may help in the development of pest management strategies, and in the understanding of the effects of climate change on its voltinism. In this context, this study aimed to select models to describe the temperature-dependent development rate of N. elegantalis and apply the best models to evaluate the impacts of climate change on pest voltinism. Voltinism was estimated with the best fit non-linear model and the degree-day approach using future climate change scenarios representing intermediary and high greenhouse gas emission rates. Two out of the six models assessed showed a good fit to the observed data and accurately estimated the thermal thresholds of N. elegantalis. The degree-day and the non-linear model estimated more generations in the warmer regions and fewer generations in the colder areas, but differences of up to 41% between models were recorded mainly in the warmer regions. In general, both models predicted an increase in the voltinism of N. elegantalis in most of the study area, and this increase was more pronounced in the scenarios with high emission of greenhouse gases. The mathematical model (74.8%) and the location (9.8%) were the factors that mostly contributed to the observed variation in pest voltinism. Our findings highlight the impact of climate change on the voltinism of N. elegantalis and indicate that an increase in its population growth is expected in most regions of the study area.

Keywords

Climate changedegree-daydevelopment rate modelsglobal warminginsect pestphenological modelsmall tomato borer
Type
Research Paper
Information
Bulletin of Entomological Research , Volume 111 , Issue 4 , August 2021 , pp. 476 - 484
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Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press

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References

Alvares, CA, Stape, JL, Sentelhas, PC, Gonçalves, JLM and Sparovek, G (2013) Köppen's climate classification map for Brazil. Meteorologische Zeittschrift 22, 711–728.CrossRefGoogle Scholar
Biber-Freudenberger, L, Ziemacki, J, Tonnang, HEZ and Borgemeister, C (2016) Future risks of pest species under changing climatic conditions. PLoS ONE 11, e0153237.CrossRefGoogle ScholarPubMed
Bortoli, SA, Benvenga, SR, Gravena, S, Vacari, AM and Volpe, HXL (2013) Ação de inseticidas sobre os ovos e lagartas da broca-pequena-do-fruto do tomate, em bioensaio de laboratório. Arquivo do Instituto Biológico 80, 73–82.CrossRefGoogle Scholar
Briere, JF, Pracos, P, le Roux, AY and Pierre, JS (1999) A novel rate model of temperature-dependent development for arthropods. Environmental Entomology 28, 22–29.CrossRefGoogle Scholar
Burnham, KP, Anderson, DR and Huyaert, KP (2011) AIC model selection and multimodel inference in behavioral ecology: some background, observations, and comparisons. Behavioral Ecology Sociobiology 65, 23–35.CrossRefGoogle Scholar
Campbell, A, Frazer, BD, Gilbert, N, Gutierrez, AP and Mackauer, M (1974) Temperature requirements of some aphids and their parasites. Journal of Applied Ecology 11, 419–423.CrossRefGoogle Scholar
Cannon, RJC (1998) The implications of predicted climate change for insect pests in the UK, with emphasis on on-indigenous species. Global Change Biology 4, 785–796.CrossRefGoogle Scholar
Cornelissen, T (2011) Climate change and its effects on terrestrial insects and herbivory patterns. Neotropical Entomology 40, 155–163.CrossRefGoogle ScholarPubMed
Denlinger, DL and Yocum, GD (1998) Physiology of heat sensitivity. In Hallman, GJ and Denlinger, DL (eds), Temperature Sensitivity in Insects and Application in Integrated Pest Management. Boca Raton: Westview Press, pp. 7–54.Google Scholar
EPPO (2020) EPPO A1 list of pests recommended for regulation as quarantine pests. https://www.eppo.int/ACTIVITIES/plant_quarantine/A1_list. Accessed 03 March 2020.Google Scholar
ESRI (2016) ArcGis Desktop: Release 10.4. Redlands: Environmental Systems Research Institute.Google Scholar
Gilioli, G, Pasquali, S, Parisi, S and Winter, S (2014) Modelling the potential distribution of Bemisia tabaci in Europe in light of the climate change scenario. Pest Management Science 70, 1611–1623.CrossRefGoogle ScholarPubMed
Hijmans, RJ, Cameron, SE, Parra, JL, Jones, PG and Jarvis, A (2005) Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25, 1965–1978.CrossRefGoogle Scholar
IPCC (2014) Synthesis report. Contribution works I, II and III to the fifth assessment report of the Intergovernmental Panel on Climate Change Pachauri, R.K. & Meyer, L.A. (Eds.). Geneva, Switzerland.Google Scholar
Jacques, J, Sampaio, F, dos Santos, HT and Marchioro, CA (2019) Climate change and voltinism of Mythimna sequax: the location and choice of phenological models matter. Agricultural and Forest Entomology 21, 431–444.CrossRefGoogle Scholar
Jepsen, JU, Hagen, SB, Ims, RA and Yoccoz, NG (2008) Climate change and outbreaks of the geometrids Operophtera brumata and Epirrita autumnata in subarctic birch forest: evidence of a recent outbreak range expansion. Journal of Animal Ecology 77, 257–264.CrossRefGoogle ScholarPubMed
Jönsson, AM, Appelberg, G, Harding, S and Bärring, L (2009) Spatio temporal impact of climate change on the activity and voltinism of spruce bark beetle, Ips typographus. Global Change Biology 15, 486–499.CrossRefGoogle Scholar
Jordão, AL and Nakano, O (2002) Ensacamento de frutos do tomateiro visando o controle de pragas e à redução de defensivos. Scientia Agricola 59, 282–289.CrossRefGoogle Scholar
Karimi-Malati, A, Fathipour, Y and Talebi, AA (2014) Development response of Spodoptera exigua to eight constant temperatures: linear and nonlinear modeling. Journal of Asia-Pacific Entomology 17, 349–354.CrossRefGoogle Scholar
Kontodimas, DC, Eliopoulos, P, Stathas, GJ and Economou, L (2004) Comparative temperature-dependent development of Nephus includes (Kirsch) and Nephus bisignatus (Boheman) (Coleoptera: Cocinellidae) preying on Planococcus citri (Risso) (Homoptera: Pseudococcidae): evaluation of a linear and various nonlinear models using specific criteria. Environmental Entomology 33, 1–11.CrossRefGoogle Scholar
Lactin, DJ, Holliday, NJ, Johnson, DL and Craigen, R (1995) Improved rate model of temperature-dependent development by arthropods. Environmental Entomology 24, 68–75.CrossRefGoogle Scholar
Loarie, SR, Duffy, PB, Hamilton, H, Asner, GP, Field, CB and Ackerly, DD (2009) The velocity of climate change. Nature 463, 1052–1055.CrossRefGoogle Scholar
Logan, JA, Wolkind, DJ, Hoyt, SC and Tanigoshi, LK (1976) An analytic model for description of temperature dependent rate phenomena in arthropods. Environmental Entomology 5, 1133–1140.CrossRefGoogle Scholar
Maino, JL, Umina, PA and Hoffmann, AA (2018) Climate contributes to the evolution of pesticide resistance. Global Ecology and Biogeography 27, 223–232.CrossRefGoogle Scholar
Marcano, R (1991) Ciclo Biologico del perforador del fruto del tomate Neoleucinodes elegantalis (Guenée) (Lepidoptera: Pyralidae), usando berenjena (Solanum melongena) como alimento. Boletin de Entomologia Venezolana 6, 135–141.Google Scholar
Marchioro, CA, Krechemer, FS and Foester, LA (2017) Estimating the development rate of the tomato leaf miner, Tuta absoluta (Lepdoptera: Gelechiidae), using linear and non-linear models. Pest Management Science 73, 1486–1493.CrossRefGoogle Scholar
Miranda, MMM, Picanço, MC, Zanuncio, JC, Bacci, L and Silva, EM (2005) Impact of integrated pest management on the population of leafminers, fruit borers, and natural enemies in tomato. Ciência Rural 35, 204–208.CrossRefGoogle Scholar
Moraes, CP and Foerster, LA (2015) Thermal requirements, fertility, and number of generations of Neoleucinodes elegantalis (Guenée) (Lepidoptera: Crambidae). Neotropical Entomology 44, 338–344.CrossRefGoogle Scholar
Murray, MS (2008) Using degree-days to time treatments for insect pests. Utah State University Extension and Utah Plant Pest Diagnostic Laboratory, IPM 05–08.Google Scholar
Picanço, MC, Bacci, L, Silva, EM, Morais, EGF, Silva, GA and Silva, NR (2007) Manejo integrado das pragas do tomateiro no Brasil. In Silva, DJH and Vale, FXR (eds), Tomate: tecnologia de produção. Viçosa, UFV, pp. 199–232.Google Scholar
Porter, JH, Parry, ML and Carter, TR (1991) The potential effects of climatic change on agricultural insect pests. Agricultural and Forest Meteorology 57, 221–240.CrossRefGoogle Scholar
Pu, J, Wang, Z and Chung, H (2019) Climate change and the genetics of insecticide resistance. Pest Management Science 76, 846–852.CrossRefGoogle ScholarPubMed
Quinn, BK (2017) A critical review of the use and performance of different functions types for modelling temperature-dependent development of arthropod larvae. Journal of Thermal Biology 63, 65–77.CrossRefGoogle Scholar
R Core Team (2015) R: A language and environment for statistical computing. Vienna, R Foundation for Statistical Computing.Google Scholar
Rebaudo, F and Rabhi, VB (2018) Modeling temperature-dependent development rate and phenology in insects: review of major developments, challenges, and future directions. Entomologia Experimentalis et Applicata 166, 607–617.CrossRefGoogle Scholar
Rebaudo, F, Struelens, Q and Dangles, O (2018) Modelling temperature-dependent development rate and phenology in arthropods: the devRate package for R. Methods in Ecology and Evolution 9, 1144–1150.CrossRefGoogle Scholar
Roltsch, WJ, Zalom, FG, Strawn, AJ, Strand, JF and Pitcairn, MJ (1999) Evaluation of several degree-day estimation methods in California climates. International Journal of Biometeorology 42, 169–176.CrossRefGoogle Scholar
Roy, M, Brodeur, J and Cloutier, C (2002) Relationship between temperature and developmental rate of Stethorus punctillum (Coleoptera: Coccinellidae) and its prey Tetranychus mcdanieli (Acarina: Tetranychidae). Environmental Entomology 31, 177–187.CrossRefGoogle Scholar
Sandhu, HS, Nuessly, GS, Webb, SE, Cherry, RH and Gilbert, RA (2010) Temperature-dependent development of Elasmopalpus lignosellus (Lepidoptera: Pyralidae) on sugarcane under laboratory conditions. Environmental Entomology 39, 1012–1029.CrossRefGoogle ScholarPubMed
Sharpe, PJH and DeMichele, DW (1977) Reaction kinetics of poikilotherm development. Journal of Theoretical Biology 64, 649–670.CrossRefGoogle ScholarPubMed
Shi, P, Ge, F, Sun, Y and Chen, CA (2011) A simple model for describing the effect of temperature on insect developmental rate. Journal of Asia-Pacific Entomology 14, 15–20.CrossRefGoogle Scholar
Silva, RS, Kumar, L, Shabani, F and Picanço, MC (2017) Potential risk levels of invasive Neoleucinodes elegantalis (small tomato borer) in areas optimal for open-field Solanym lycopersicum (tomato) cultivation in the present and under predicted climate change. Pest Management Science 73, 616–627.CrossRefGoogle ScholarPubMed
Srinivasa Rao, M, Swathi, P, Rao, CAR, Rao, KV, Raju, BMK, Srinivas, K, Manimanjari, D and Majeswari, M (2015) Model and scenario variations in predicted number of generations of Spodoptera litura Fab. on peanut during future climate change scenario. PLoS ONE 10, e0116762.CrossRefGoogle Scholar
Srinivasa Rao, M, Dammu, M, Sengottaiyan, V, Ongolu, S, Biradar, AK, Kondru, VR, Karlapudi, S, Bellapukonda, MKR, Chitiprolu, BRA and Cherukumalli, SR (2016) Prediction of Helicoverpa armigera Hübner on pigeonpea during future climate change periods using MarkSim multimodel data. Agricultural and Forest Meteorology 229, 130–138.Google Scholar
Systat. Inc. (2002) Table Curve 2D. Chicago: Systat Software Inc.Google Scholar
Wagner, TL, Wu, H-I, Sharpe, PJH, Schoofield, RM and Coulson, RN (1984) Modeling insect development rates: literature review and application of a biophysical model. Annals of the Entomological Society of America 77, 208–220.CrossRefGoogle Scholar
Xavier, AC, King, CW and Scanlon, BR (2016) Daily gridded meteorological variables in Brazil (1980–2013). International Journal of Climatology 36, 2644–2659.CrossRefGoogle Scholar
Ziter, C, Robinson, EA and Newman, JA (2012) Climate change and voltinism in Californian insect pest species: sensitivity to location, scenario and climate model choice. Global Change Biology 18, 2771–2780.CrossRefGoogle ScholarPubMed