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Modeling insect population fluctuations with satellite land surface temperature

Author

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  • Blum, Moshe
  • Lensky, Itamar M.
  • Rempoulakis, Polychronis
  • Nestel, David

Abstract

The simulation of insect pest populations in agricultural and forest ecosystems is an important and useful tool for integrated pest management (IPM). Insect population models are mainly driven by environmental temperature data, which are usually collected from agrometeorological stations or derived from geographic statistical extrapolations. The present study describes the modeling of olive fly (Bactrocera oleae) populations in the Eastern Mediterranean region using the MODIS (Moderate Resolution Image Spectro Radiometer) land surface temperature (LST) product from NASA TERRA satellite. These data, together with in situ temperature data, were used to estimate the tree-canopy temperatures at the pixel resolution (1km). The estimated canopy temperature was used as input for the olive fly population model. Our main aim was to demonstrate the use of satellite-acquired information for modelling biological and ecological phenomena. Eleven years (2001–2012) of olive fly population fluctuations were simulated for three different geographic locations, representing different geo-climatic conditions. The model successfully simulated the seasonal population fluctuations throughout the 11-year period and did a good job of connecting all of the life stages of the insect. To evaluate the validity of these findings, we compared them with adult olive-fly trapping data. We observed a high degree of correlation between the trapping data and our model's predictions. Here, we demonstrate that satellite thermal data can be used to predict insect pest population fluctuations for IPM purposes. The study also advances some new modelling concepts, such as the “window of opportunity” which links physiological development with chronological age.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ecomod:v:311:y:2015:i:c:p:39-47
    DOI: 10.1016/j.ecolmodel.2015.05.005
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    References listed on IDEAS

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    1. Emile Faye & Mario Herrera & Lucio Bellomo & Jean-François Silvain & Olivier Dangles, 2014. "Strong Discrepancies between Local Temperature Mapping and Interpolated Climatic Grids in Tropical Mountainous Agricultural Landscapes," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-11, August.
    2. Fand, Babasaheb B. & Tonnang, Henri E.Z. & Kumar, Mahesh & Bal, Santanu K. & Singh, Naveen P. & Rao, D.V.K.N. & Kamble, Ankush L. & Nangare, Dhananjay D. & Minhas, Paramjit S., 2014. "Predicting the impact of climate change on regional and seasonal abundance of the mealybug Phenacoccus solenopsis Tinsley (Hemiptera: Pseudococcidae) using temperature-driven phenology model linked to," Ecological Modelling, Elsevier, vol. 288(C), pages 62-78.
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    Cited by:

    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. 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).
    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.

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