IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v466y2022ics0304380022000242.html
   My bibliography  Save this article

Ground-truthing predictions of a demographic model driven by land surface temperatures with a weed biocontrol cage experiment

Author

Listed:
  • Augustinus, Benno A.
  • Blum, Moshe
  • Citterio, Sandra
  • Gentili, Rodolfo
  • Helman, David
  • Nestel, David
  • Schaffner, Urs
  • Müller-Schärer, Heinz
  • Lensky, Itamar M.

Abstract

Herbivorous insects play important roles in agriculture as pests or as weed biological control agents. Predicting the timing of herbivore insect population development can thus be of paramount importance for agricultural planning and sustainable land management. Numerical simulation models driven by temperature are often used to predict insect pest population build-up in agriculture. Such simulation models intend to use station-derived temperatures to drive the development of the target insect, although this temperature may differ substantially from that experienced by the insect on the plant. To improve the estimations, it has been suggested to replace air temperature in the model by land surface temperature (LST) data. Here, we use a numerical simulation model of insect population dynamics driven by either air temperature (combined with atmospheric temperature soundings) or land surface temperature derived from satellites to predict the population trends of the leaf beetle Ophraella communa, a potential biological control agent of Ambrosia artemisiifolia in Europe. For this, we conducted an extensive field experiment that included caged O. communa populations at five sites along an altitudinal gradient (125–1250 m a.s.l.) in Northern Italy during 2015 and 2016. We compared our model predictions using air or land surface temperature with observed beetle population build-up. Model predictions with both air and land surface temperatures predicted a similar phenology to observed populations but overestimated the abundance of the observed populations. When taking into consideration the error of the two measurement methods, the predictions of the model were in overlapping timeframes. Therefore, the current model driven by LST can be used as a proxy for herbivore impact, which is a novel tool for weed biocontrol.

Suggested Citation

  • Augustinus, Benno A. & Blum, Moshe & Citterio, Sandra & Gentili, Rodolfo & Helman, David & Nestel, David & Schaffner, Urs & Müller-Schärer, Heinz & Lensky, Itamar M., 2022. "Ground-truthing predictions of a demographic model driven by land surface temperatures with a weed biocontrol cage experiment," Ecological Modelling, Elsevier, vol. 466(C).
  • Handle: RePEc:eee:ecomod:v:466:y:2022:i:c:s0304380022000242
    DOI: 10.1016/j.ecolmodel.2022.109897
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380022000242
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ecolmodel.2022.109897?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Blum, Moshe & Nestel, David & Cohen, Yafit & Goldshtein, Eitan & Helman, David & Lensky, Itamar M., 2018. "Predicting Heliothis (Helicoverpa armigera) pest population dynamics with an age-structured insect population model driven by satellite data," Ecological Modelling, Elsevier, vol. 369(C), pages 1-12.
    2. Blum, Moshe & Lensky, Itamar M. & Rempoulakis, Polychronis & Nestel, David, 2015. "Modeling insect population fluctuations with satellite land surface temperature," Ecological Modelling, Elsevier, vol. 311(C), pages 39-47.
    3. Wickham, Hadley, 2007. "Reshaping Data with the reshape Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 21(i12).
    4. Urs Schaffner & Sandro Steinbach & Yan Sun & Carsten A. Skjøth & Letty A. Weger & Suzanne T. Lommen & Benno A. Augustinus & Maira Bonini & Gerhard Karrer & Branko Šikoparija & Michel Thibaudon & Heinz, 2020. "Biological weed control to relieve millions from Ambrosia allergies in Europe," Nature Communications, Nature, vol. 11(1), pages 1-7, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Siti Aisyah Ruslan & Farrah Melissa Muharam & Zed Zulkafli & Dzolkhifli Omar & Muhammad Pilus Zambri, 2019. "Using satellite-measured relative humidity for prediction of Metisa plana’s population in oil palm plantations: A comparative assessment of regression and artificial neural network models," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-15, October.
    2. Julio Cesar Alonso Cifuentes & Jaime Andres Carabali, 2019. "Breve Tuturial para visualizar y Calcular Métricas de Redes (grafos) en R (para Económisas)," Icesi Economics Lecture Notes 18170, Universidad Icesi.
    3. Tonnang, Henri E.Z. & Hervé, Bisseleua D.B. & Biber-Freudenberger, Lisa & Salifu, Daisy & Subramanian, Sevgan & Ngowi, Valentine B. & Guimapi, Ritter Y.A. & Anani, Bruce & Kakmeni, Francois M.M. & Aff, 2017. "Advances in crop insect modelling methods—Towards a whole system approach," Ecological Modelling, Elsevier, vol. 354(C), pages 88-103.
    4. Blum, Moshe & Nestel, David & Cohen, Yafit & Goldshtein, Eitan & Helman, David & Lensky, Itamar M., 2018. "Predicting Heliothis (Helicoverpa armigera) pest population dynamics with an age-structured insect population model driven by satellite data," Ecological Modelling, Elsevier, vol. 369(C), pages 1-12.
    5. Miller, Christine M.F. & Waterhouse, Hannah & Harter, Thomas & Fadel, James G. & Meyer, Deanne, 2020. "Quantifying the uncertainty in nitrogen application and groundwater nitrate leaching in manure based cropping systems," Agricultural Systems, Elsevier, vol. 184(C).
    6. Sarlas, Georgios & Páez, Antonio & Axhausen, Kay W., 2020. "Betweenness-accessibility: Estimating impacts of accessibility on networks," Journal of Transport Geography, Elsevier, vol. 84(C).
    7. Marin FOTACHE & Florin DUMITRU & Valerica GREAVU-SERBAN, 2015. "An Information Systems Master Programme in Romania. Some Commonalities and Specificities," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 19(3), pages 5-18.
    8. Martijn Van Heel & Dinska Van Gucht & Koen Vanbrabant & Frank Baeyens, 2017. "The Importance of Conditioned Stimuli in Cigarette and E-Cigarette Craving Reduction by E-Cigarettes," IJERPH, MDPI, vol. 14(2), pages 1-18, February.
    9. Sean McKenzie & Hilary Parkinson & Jane Mangold & Mary Burrows & Selena Ahmed & Fabian Menalled, 2018. "Perceptions, Experiences, and Priorities Supporting Agroecosystem Management Decisions Differ among Agricultural Producers, Consultants, and Researchers," Sustainability, MDPI, vol. 10(11), pages 1-19, November.
    10. Milad Abbasiharofteh & Tom Broekel, 2021. "Still in the shadow of the wall? The case of the Berlin biotechnology cluster," Environment and Planning A, , vol. 53(1), pages 73-94, February.
    11. Jill F. Lundell & Brennan Bean & Jürgen Symanzik, 2023. "Let’s talk about the weather: a cluster-based approach to weather forecast accuracy," Computational Statistics, Springer, vol. 38(3), pages 1135-1155, September.
    12. Andee J. Kaplan & Eric R. Hare, 2019. "Putting down roots: a graphical exploration of community attachment," Computational Statistics, Springer, vol. 34(4), pages 1449-1464, December.
    13. Haider, Saira M. & Benscoter, Allison M. & Pearlstine, Leonard & D'Acunto, Laura E. & Romañach, Stephanie S., 2021. "Landscape-scale drivers of endangered Cape Sable Seaside Sparrow (Ammospiza maritima mirabilis) presence using an ensemble modeling approach," Ecological Modelling, Elsevier, vol. 461(C).
    14. Melina Kourantidou & Laura N H Verbrugge & Phillip J Haubrock & Ross N Cuthbert & Elena Angulo & Inkeri Ahonen & Michelle Cleary & Jannike Falk-Andersson & Lena Granhag & Sindri Gíslason & Brooks Kais, 2022. "The economic costs, management and regulation of biological invasions in the Nordic countries," Post-Print hal-03860518, HAL.
    15. Fox, John & Carvalho, Marilia S., 2012. "The RcmdrPlugin.survival Package: Extending the R Commander Interface to Survival Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 49(i07).
    16. C. J. Torrecilla-Salinas & O. Troyer & M. J. Escalona & M. Mejías, 2019. "A Delphi-based expert judgment method applied to the validation of a mature Agile framework for Web development projects," Information Technology and Management, Springer, vol. 20(1), pages 9-40, March.
    17. Priyanga Dilini Talagala & Rob J Hyndman & Kate Smith-Miles & Sevvandi Kandanaarachchi & Mario A Munoz, 2018. "Anomaly detection in streaming nonstationary temporal data," Monash Econometrics and Business Statistics Working Papers 4/18, Monash University, Department of Econometrics and Business Statistics.
    18. Paul Battlay & Jonathan Wilson & Vanessa C. Bieker & Christopher Lee & Diana Prapas & Bent Petersen & Sam Craig & Lotte Boheemen & Romain Scalone & Nissanka P. Silva & Amit Sharma & Bojan Konstantinov, 2023. "Large haploblocks underlie rapid adaptation in the invasive weed Ambrosia artemisiifolia," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    19. Thelma Dede Baddoo & Zhijia Li & Yiqing Guan & Kenneth Rodolphe Chabi Boni & Isaac Kwesi Nooni, 2020. "Data-Driven Modeling and the Influence of Objective Function Selection on Model Performance in Limited Data Regions," IJERPH, MDPI, vol. 17(11), pages 1-26, June.
    20. Sólymos, Péter, 2009. "Processing Ecological Data in R with the mefa Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 29(i08).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecomod:v:466:y:2022:i:c:s0304380022000242. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/ecological-modelling .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.