CAL Research

Here you will find selected articles, research papers, and resources that informed the Central American Locust (CAL) Project. Many of these can be accessed through the UTRGV Library or via their DOI / publisher links.

Click + to display the accordions with sources:
  • Poot-Pech, M. A., Ruiz-Sánchez, E., Gamboa-Angulo, M., Ballina-Gómez, H. S., & Reyes-Ramírez, A. (2018). Population fluctuation of Schistocerca piceifrons (Central American locust) (Orthoptera: Acrididae) in the Yucatan Peninsula and its relation with environmental conditions. Revista de Biología Tropical, 66(1), 403–416. Available via UTRGV Library: https://go.gale.com/ps/i.do?p=AONE&u=txshracd2633&id=GALE%7CA533000633&v=2.1&it=r&sid=bookmark-AONE&asid=6c405f59
     
  • Foquet, B., Little, D. W., Medina-Durán, J. H., & Song, H. (2022). The time course of behavioural phase change in the Central American locust Schistocerca piceifrons. Journal of Experimental Biology, 225(23), Article 244621.https://doi.org/10.1242/jeb.244621
  • Foquet, B., & Song, H. (2021). The role of the neuropeptide [His7]-corazonin on phase-related characteristics in the Central American locust. Journal of Insect Physiology, 131, Article 104244. https://doi.org/10.1016/j.jinsphys.2021.104244

  • Hernández-Zul, M. I., Quijano-Carranza, J. A., Yáñez-López, R., Ocampo-Velázquez, R. V., Torres-Pacheco, I., Guevara-González, R. G., & Castro-Ramírez, A. E. (2013). Dynamic simulation model of Central American locust Schistocerca piceifrons (Orthoptera: Acrididae). Florida Entomologist, 96(4), 1274–1283. https://doi.org/10.1653/024.096.0405
  • Puga-Patlán, P., Barrientos-Lozano, L., Sánchez-Reyes, U. J., Rocha-Sánchez, A. Y., & Almaguer-Sierra, P. (2024). Impact of climate change on the generation of new breeding areas of Schistocerca piceifrons piceifrons. Southwestern Entomologist, 49(1), 364–389. https://doi.org/10.3958/059.049.0130

  • Gay, P., Lecoq, M., & Piou, C. (2018). Improving preventive locust management: Insights from a multi-agent model. Pest Management Science, 74(1), 46–58. https://doi.org/10.1002/ps.4648
  • Frazier, A. E., & Song, L. (2024). Artificial intelligence in landscape ecology: Recent advances, perspectives, and opportunities. Current Landscape Ecology Reports, 10(1), Article 1. https://doi.org/10.1007/s40823-024-00103-7

  • Yin, W., Ragab, S. H., Tyshenko, M. G., Arroyo, T. F., & Oraby, T. (2025). Comparing machine learning, deep learning, and reinforcement learning performance in Culex pipiens predictive modeling. PLOS ONE, 20(11), e0333536. https://doi.org/10.1371/journal.pone.0333536

  • Riva, F., Martin, C. J., Galán Acedo, C., Bellon, E. N., Keil, P., Morán-Ordóñez, A., Fahrig, L., & Guisan, A. (2024). Incorporating effects of habitat patches into species distribution models. Journal of Ecology, 112(10), 2162–2182. https://doi.org/10.1111/1365-2745.14403
  • Poot-Pech, M. A., & Song, H. (2023). The Central American locust: Risk and prevention. In R. Sivanpillai & J. F. Shroder (Eds.), Biological and Environmental Hazards, Risks, and Disasters (2nd ed., pp. 129–154). Elsevier. https://doi.org/10.1016/B978-0-12-820509-9.00012-5 
  • Locust Watch – Sword Lab, Texas A&M University.
The Locust Watch project at Texas A&M focuses on monitoring the potential arrival of the Central American locust (Schistocerca piceifrons) in the United States and provides tools for reporting sightings and understanding risk along the U.S.–Mexico border. https://swordlab.tamu.edu/locust-watch/ 

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