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Modelling of post-diapause development and spring emergence of Cydia nigricana (Lepidoptera: Tortricidae)

Published online by Cambridge University Press:  19 January 2021

Natalia Riemer Open the ORCID record for Natalia Riemer [Opens in a new window] ,
Manuela Schieler ,
Paolo Racca  and
Helmut Saucke
Natalia Riemer*
Affiliation:
Universitat Kassel, Nordbahnhofstr. 1a, Witzenhausen, Hesse37213, Germany
Manuela Schieler
Affiliation:
Central Institute for Decision Support Systems in Crop Protection (ZEPP), Rüdesheimer Str. 60-68, D-55545Bad Kreuznach, Germany
Paolo Racca
Affiliation:
Central Institute for Decision Support Systems in Crop Protection (ZEPP), Rüdesheimer Str. 60-68, D-55545Bad Kreuznach, Germany
Helmut Saucke
Affiliation:
Central Institute for Decision Support Systems in Crop Protection (ZEPP), Rüdesheimer Str. 60-68, D-55545Bad Kreuznach, Germany
*
Author for correspondence: Natalia Riemer, Email: [email protected]
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Abstract

The prediction of the post-diapause emergence is the first step towards a comprehensive decision support system that can contribute to a considerable reduction of pesticide use by forecasting a precise spraying date. The cumulative field emergence can be described as a function of the cumulative development rate. We investigated the impact of seven constant temperatures and five light regimes on post-diapause development in laboratory experiments. Development rate depended significantly on temperature but not on photoperiod. We therefore fit non-linear thermal performance curves, a better and more modern approach over past linear models, to describe the development rate as a function of temperature. The four parameter Brière function was the most suitable and was subsequently applied to temperature data from 36 previous pea fields, where pea moth emergence was measured with pheromone traps in Northern Hesse (Germany). In order to describe the variation in development times between individuals, we fit five nonlinear distribution models to the cumulative development rate as a function of cumulative field emergence. The three parameter Gompertz model was selected as the best fitted model. We validated the model performance with an independent field data set. The model correctly predicted the first moth in the trap and the peak emergence in 81.82% of cases, with an average deviation of only 2.00 and 2.09 days respectively.

Keywords

Development ratenonlinear modelparameter estimationpest managementphotoperiodtemperature
Type
Research Paper
Information
Bulletin of Entomological Research , Volume 111 , Issue 4 , August 2021 , pp. 402 - 410
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Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press

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References

Ahmad, TR (1988) Degree-days requirements for predicting emergence and flight of the codling moth Cydia pomonella (L.) (Lep., Olethreutidae). Journal of Applied Entomology 106, 345–349.CrossRefGoogle Scholar
Ahn, JJ, Yang, CY and Jung, C (2012) Model of Grapholita molesta spring emergence in pear orchards based on statistical information criteria. Journal of Asia-Pacific Entomology 15, 589–593.CrossRefGoogle Scholar
Akaike, H (1974) A new look at the statistical model identification. IEEE Transactions on Automatic Control 19, 716–723.CrossRefGoogle Scholar
Allen, JC (1976) A modified sine wave method calculating degree days. Environmental Entomology 5, 388–396.CrossRefGoogle Scholar
Angert, AL, Sheth, SN and Paul, JR (2011) Incorporating population-level variation in thermal performance into predictions of geographic range shifts. Integrative and Comparative Biology 51, 733–750.CrossRefGoogle ScholarPubMed
Bergh, JC and Judd, GJR (1993) Degree-day model for predicting emergence of pear rust mite (Acari: Eriophyidae) Deutogynes from overwintering sites. Environmental Entomology 22, 1325–1332.CrossRefGoogle Scholar
Bernal, J and Gonzalez, D (1993) Experimental assessment of a degree-day model for predicting the development of parasites in the field. Journal of Applied Entomology 116, 459–466.CrossRefGoogle Scholar
Bonhomme, R (2000) Bases and limits to using ‘degree⋅day’ units. European Journal of Agronomy 13, 1–10.CrossRefGoogle Scholar
Brière, J-F, Le Roux, A-Y and Pierre, J-S (1999) A novel rate model of temperature-dependent development for arthropods. Environmental Entomology 26, 22–29.CrossRefGoogle Scholar
Cameron, E (1938) A study of the natural control of the pea moth, Cydia nigricana Steph. Bulletin of Entomological Research 29, 277–313.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, 431–438.CrossRefGoogle Scholar
Corbet, PS (2003) A positive correlation between photoperiod and development rate in summer species of Odonata could help to make emergence date appropriate to latitude: a testable hypothesis. Journal of the Entomological Society of British Columbia 100, 3–17.Google Scholar
Damos, P and Savopoulou-Soultani, M (2012) Temperature-driven models for insect development and vital thermal requirements. Psyche: A Journal of Entomology 2012, 1–2, 1–13.CrossRefGoogle Scholar
Danks, HV (1987) Insect Dormancy: An Ecological Perspective, 1st Edn. Ottawa: Biological Survey of Canada (Terrestrial Arthropods).Google Scholar
Dann, C (1979) Untersuchungen zur Schadwirkung und Überwachung des Erbsenwicklers (Laspeyresia nigricana (Steph.)) im Trockenerbsenanbau des Bezirkes Halle (Doctoral thesis). Martin-Luther Univeristät Halle-Wittenberg, Halle-Wittenberg.Google Scholar
Fox, J and Weisberg, S (2011) An R Companion to Applied Regression, 2nd Edn. Los Angeles, London: Sage, New Delhi, Singapore, Washington DC.Google Scholar
Furlong, MJ and Zalucki, MP (2017) Climate change and biological control: the consequences of increasing temperatures on host-parasitoid interactions. Current opinion in insect science 20, 39–44.CrossRefGoogle ScholarPubMed
Gompertz, B (1825) On the nature of the function expressive of the law of human mortality, and on a new mode of determining the value of life contingencies. Philosophical Transactions of the Royal Society 115, 513–585.Google Scholar
Graham, JC (1984) Emergence, dispersal and reproductive biology of Cydia nigricana (F.) (Lepidoptera: Tortricidae) (Doctoral thesis). University of London, London.Google Scholar
Grothendieck, G (2013) Nls2: Non-linear regression with brute force. R package version 0.2. Available at http://CRAN.R-project.org/package=nls2.Google Scholar
Hanson, AJ and Webster, RL (1936) The pea moth. Laspeyresia nigricana Steph. Bulletin of the Agricultural Experiment Station of Washington 327, 1–22.Google Scholar
Honek, A (1996) Geographical variation in thermal requirements for insect development. European Journal of Entomology 93, 303–312.Google Scholar
Huusela-Veistola, E and Jauhiainen, L (2006) Expansion of pea cropping increases the risk of pea moth (Cydia nigricana; Lep., Tortricidae) infestation. Journal of Applied Entomology 130, 142–149.CrossRefGoogle Scholar
ICCP (2014) Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, R.K. Pachauri and L.A. Meyer (eds.)], Geneva, Switzerland.Google Scholar
Jones, VP, Hilton, R, Brunner, JF, Bentley, WJ, Alston, DG, Barrett, B, Van Steenwyk, RA, Hull, LA, Walgenbach, JF, Coates, WW and Smith, TJ (2013) Predicting the emergence of the codling moth, Cydia pomonella (Lepidoptera: Tortricidae), on a degree–day scale in North America. Pest Management Science 69 (12), 1393–1398.CrossRefGoogle Scholar
Jyoti, JL, Shelton, AM and Barnard, J (2003) Evaluation of degree-day and Julian-day logistic models in predicting cabbage maggot (Diptera: Anthomyiidae) emergence and flight in upstate New York. Journal of Entomological Science 38, 525–532.CrossRefGoogle Scholar
Kaniuczak, Z (2010) Susceptibility of some field pea (Pisum arvense L.) cultivars to pod damage caused by pea moth (Laspeyresia nigricana Steph.). Journal of Plant Protection Research 50, 73.CrossRefGoogle Scholar
Knight, AL (2007) Adjusting the phenology model of codling moth (Lepidoptera: Tortricidae) in Washington State apple orchards. Environmental Entomology 36, 1485–1493.CrossRefGoogle ScholarPubMed
Koštál, V (2006) Eco-physiological phases of insect diapause. Journal of Insect Physiology 52, 113–127.CrossRefGoogle ScholarPubMed
Kumral, NA, Kovanci, B and Akbudak, B (2008) Using degree-day accumulations and host phenology for predicting larval emergence patterns of the olive psyllid, Euphyllura phillyreae. Journal of Pest Science 81, 63–69.CrossRefGoogle Scholar
Kuwayama, S (1937) Notes on the pea moth, Grapholita nigricana Steph. in Nippon. Kontyû 11, 1–11.Google 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
Lamb, RJ (1992) Developmental rate of Acyrthosiphon pisum (Homoptera: Aphididae) at low temperatures: implications for estimating rate parameters for insects. Environmental Entomology 21, 10–19.CrossRefGoogle Scholar
Langenbuch, R (1941) Zur Biologie des Erbsenwicklers Grapholitha nigricana Steph. Arbeiten zur Physiologischen und Angewandten Entomologie 8, 216–247.Google Scholar
Lewis, T and Sturgeon, DM (1978) Early warning and egg hatching in pea moth (Cydia nigricana). Annals of Applied Biology 88, 199–210.CrossRefGoogle Scholar
Lewis, T, Wall, C, Macaulay, EDM and Greenway, AR (1975) The behavioural basis of a pheromone monitoring system for pea moth, Cydia nigricana. Annals of Applied Biology 80, 257–274.CrossRefGoogle ScholarPubMed
Liu, S-S, Zhang, G-M and Zhu, J (1995) Influence of temperature variations on rate of development in insects: analysis of case studies from entomological literature. Annals of the Entomological Society of America 88, 107–119.CrossRefGoogle Scholar
Lutz, PE (1968) Effects of temperature and photoperiod on larval development in Lestes eurinus (Odonata: Lestidae). Ecology 49, 637–644.CrossRefGoogle Scholar
Macaulay, EDM, Etheridge, P, Garthwaite, DG, Greenway, AR, Wall, C and Goodchild, RE (1985) Prediction of optimum spraying dates against pea moth, Cydia nigricana (F.), using pheromone traps and temperature measurements. Crop Protection 4, 85–98.CrossRefGoogle Scholar
Marchioro, CA, Krechemer, FS, de Moraes, CP and Foerster, LA (2015) Reliability of degree-day models to predict the development time of Plutella xylostella (L.) under field conditions. Neotropical Entomology 44, 574–579.CrossRefGoogle ScholarPubMed
Moallem, Z, Karimi-Malati, A, Sahragard, A and Zibaee, A (2017) Modeling temperature-dependent development of Glyphodes pyloalis (Lepidoptera: Pyralidae). Journal of Insect Science 17, 1–8.CrossRefGoogle Scholar
Molnár, PK, Sckrabulis, JP, Altman, KA and Raffel, TR (2017) Thermal performance curves and the metabolic theory of ecology – a practical guide to models and experiments for parasitologists. The Journal of Parasitology 103, 423–439.CrossRefGoogle ScholarPubMed
Moore, JL and Remais, JV (2014) Developmental models for estimating ecological responses to environmental variability: structural, parametric, and experimental issues. Acta Biotheoretica 62, 69–90.CrossRefGoogle ScholarPubMed
Moore, JL, Liang, S, Akullian, A and Remais, JV (2012) Cautioning the use of degree-day models for climate change projections in the presence of parametric uncertainty. Ecological Applications 22, 2237–2247.CrossRefGoogle ScholarPubMed
Naves, P and de Sousa, E (2009) Threshold temperatures and degree-day estimates for development of post-dormancy larvae of Monochamus galloprovincialis (Coleoptera: Cerambycidae). Journal of Pest Science 82, 1–6.CrossRefGoogle Scholar
Ngowi, BV, Tonnang, HEZ, Mwangi, EM, Johansson, T, Ambale, J, Ndegwa, PN and Subramanian, S (2017) Temperature-dependent phenology of Plutella xylostella (Lepidoptera: Plutellidae): simulation and visualization of current and future distributions along the Eastern Afromontane. PLoS One 12, e0173590.CrossRefGoogle ScholarPubMed
Nicolaisen, W (1928) Der Erbsenwickler, Grapholita (Cydia, Laspeyresia) sp.; sein Schaden und seine Bekämpfung unter Berücksichtigung der Anfälligkeit verschiedener Erbsensorten. Kühn Archiv 19, 196–256.Google Scholar
Nußbaum, P. (1976) Über die Wirkzng exogener Faktoren auf den Massenwechsel des Erbsenwicklers (Laspeyresia nigricana Steph.) mit besonderer Berücksichtigung der Dormanzverhältnisse (Doctoral thesis). Friedrich-Schiller-Universität Jena, Jena.Google Scholar
Pollard, CP, Griffin, CT, de Andrade Moral, R, Duffy, C, Chuche, J, Gaffney, MT, Fealy, RM and Fealy, R (2020) Phenmodel: a temperature-dependent phenology/voltinism model for a herbivorous insect incorporating facultative diapause and budburst. Ecological Modelling 416, 108910.CrossRefGoogle Scholar
Processors and Growers Research Organisation (2019) Pea moth. Available at https://www.pgro.org/pea-moth/.Google Scholar
Racca, P, Zeuner, T, Jung, J and Kleinhenz, B (2010) Model validation and use of geographic information systems in crop protection warning service. In Oerke, E-C, Gerhards, R, Menz, G and Sikora, RA (eds), Precision Crop Protection – The Challenge and Use of Heterogeneity. Dordrecht: Springer Science + Business Media B.V., pp. 259–276.CrossRefGoogle Scholar
Racca, P, Kleinhenz, B, Zeuner, T, Keil, B, Tschope, B and Jung, J (2011) Decision support systems in agriculture: administration of meteorological data, use of geographic information systems(GIS) and validation methods in crop protection warning service. In Jao, C. (Ed.), Efficient decision support systems – practice and challenges from current to future. InTech, London, pp. 331–354.Google Scholar
Rebaudo, F and Rabhi, V-B (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
Sawchyn, WW and Church, NS (1973) The effects of temperature and photoperiod on diapause development in the eggs of four species of Lestes (Odonata: Zygoptera). Canadian Journal of Zoology 51, 1257–1265.CrossRefGoogle Scholar
Sharpe, PJH and DeMichele, DW (1977) Reaction kinetics of poikilotherm development. Journal of Theoretical Biology 64, 649–670.CrossRefGoogle ScholarPubMed
Stenmark, A (1971) Studies on the pea moth (Laspeyresia nigricana Steph.) in Central Sweden 3. Statens Växtskyddsamstalt Meddelanden 15, 90–110.Google Scholar
Stenmark, A (1974) Studies on the pea moth (Laspeyresia nigricana Steph.) in Central Sweden. Statens Växtskyddsamstalt Meddelanden 15, 451–472.Google Scholar
Stevenson, DE, Michels, GJ, Bible, JB, Jackman, JA and Harris, MK (2008) Physiological time model for predicting adult emergence of western corn rootworm (Coleopters: Chrysomelidae) in the Texas High Plains. Journal of Economic Entomology 101, 1584–1593.CrossRefGoogle Scholar
Tauber, MJ and Tauber, CA (1976) Insect seasonality: diapause maintenance, termination, and postdiapause development. Annual Review of Entomology 21, 81–107.CrossRefGoogle Scholar
Thöming, G and Saucke, H (2010) Key factors affecting the spring emergence of pea moth (Cydia nigricana). Bulletin of Entomological Research 101, 127–133.CrossRefGoogle Scholar
Thöming, G and Knudsen, GK (2014) Attraction of pea moth Cydia nigricana to pea flower volatiles. Phytochemistry 100, 66–75.CrossRefGoogle ScholarPubMed
Thöming, G and Norli, HR (2015) Olfactory cues from different plant species in host selection by female pea moths. Journal of Agricultural and Food Chemistry 63, 2127–2136.CrossRefGoogle ScholarPubMed
Thöming, G, Norli, HR, Saucke, H and Knudsen, GK (2014) Pea plant volatiles guide host location behaviour in the pea moth. Arthropod–Plant Interactions 8, 109–122.CrossRefGoogle Scholar
Touvinen, T (1982) Prognosis of injury by Cydia nigricana (Lepedoptera, Tortricidae) using ‘the oecos pea moth monitoring system’: a preliminary report. Acta Entomologica Fennica 40, 35–38.Google Scholar
Verhulst, PF (1838) A note on population growth. Correspondence in Máthamatique and Physique 10, 113–121.Google Scholar
Wagner, TL, Wu, H-I, Sharpe, PJH and Coulson, RN (1984) Modeling distributions of insect development time: a literature review and application of the Weibull function. Annals of the Entomological Society of America 77, 475–487.CrossRefGoogle Scholar
Weibull, W (1951) A statistical distribution function of wide applicability. Journal of Applied Mechanics – Transactions of the American Society of Mechanical Engineers, 293–297.CrossRefGoogle Scholar
Wheatley, GA and Dunn, JA (1962) The influence of diapause on the time of emergence of the pea moth Laspereyresia nigricana (Steph). Annals of Applied Biology 50, 609–611.CrossRefGoogle Scholar
Wright, DW and Geering, QA (1948) The biology and control of the pea moth, Laspeyresia nigricana, Steph. Bulletin of Entomological Research 39, 57–84.CrossRefGoogle Scholar
Wright, DW, Geering, QA and Dunn, JA (1951) Varietal differences in the susceptibility of peas to attack by the pea moth, Laspeyresia nigricana (Steph.). Bulletin of Entomological Research 41, 663.CrossRefGoogle Scholar
Zeuner, T (2007) Landwirtschaftliche Schaderregerprognose mit Hilfe von Geographischen Informationssystemen (Doctoral thesis). Johannes Gutenberg – Universität Mainz, Mainz.Google Scholar