PreprintArticleVersion 1Preserved in Portico This version is not peer-reviewed
The Multivariate Regression Models Suggested as Tools for Categorizing Solitarious and Gregarious Groups of the Main Pest Locust, Schistocerca gregaria, Produce Reproducible Results
Version 1
: Received: 20 September 2023 / Approved: 20 September 2023 / Online: 21 September 2023 (08:49:02 CEST)
How to cite:
Saadi, S.; Bakkali, N.; Blázquez, R.M.; Badih, A.; Bakkali, M. The Multivariate Regression Models Suggested as Tools for Categorizing Solitarious and Gregarious Groups of the Main Pest Locust, Schistocerca gregaria, Produce Reproducible Results. Preprints2023, 2023091438. https://doi.org/10.20944/preprints202309.1438.v1
Saadi, S.; Bakkali, N.; Blázquez, R.M.; Badih, A.; Bakkali, M. The Multivariate Regression Models Suggested as Tools for Categorizing Solitarious and Gregarious Groups of the Main Pest Locust, Schistocerca gregaria, Produce Reproducible Results. Preprints 2023, 2023091438. https://doi.org/10.20944/preprints202309.1438.v1
Saadi, S.; Bakkali, N.; Blázquez, R.M.; Badih, A.; Bakkali, M. The Multivariate Regression Models Suggested as Tools for Categorizing Solitarious and Gregarious Groups of the Main Pest Locust, Schistocerca gregaria, Produce Reproducible Results. Preprints2023, 2023091438. https://doi.org/10.20944/preprints202309.1438.v1
APA Style
Saadi, S., Bakkali, N., Blázquez, R.M., Badih, A., & Bakkali, M. (2023). The Multivariate Regression Models Suggested as Tools for Categorizing Solitarious and Gregarious Groups of the Main Pest Locust, Schistocerca gregaria, Produce Reproducible Results. Preprints. https://doi.org/10.20944/preprints202309.1438.v1
Chicago/Turabian Style
Saadi, S., Abdelmounim Badih and Mohammed Bakkali. 2023 "The Multivariate Regression Models Suggested as Tools for Categorizing Solitarious and Gregarious Groups of the Main Pest Locust, Schistocerca gregaria, Produce Reproducible Results" Preprints. https://doi.org/10.20944/preprints202309.1438.v1
Abstract
Outbreaks of the desert locust Schistocerca gregaria affect some of the poorest parts of Africa, causing devastating catastrophes. Key to understanding and dealing with this problematic adaptation to environmental changes is comparing locusts that are gregarious (associated with outbreak states) and solitarious (associated with non-outbreak states) either in nature or after experimental treatments in laboratories. Categorising locusts and detecting changes in their phase status is key to such comparisons. Such comparisons are hitherto based on applying mathematical models that use behavioural parameters and that each laboratory has to build a new for each experiment. All such models used thus far for research on locusts are different from each other. That implies differences in the tools used for the different experiments and by the different laboratories and, thus, potential noise in the scientific results and interpretations too. Standardizing the way how we categorise locusts between laboratories and experiments is needed if we want to reduce noise and errors. It is even a must if we are to make the results and interpretations transferable and comparable between experiments and laboratories that work in such an important research area. Here, we use samples from independent S. gregaria population in order to further test the two models that were suggested earlier as standardizing tools for S. gregaria categorization. The outcomes of both models were largely replicated and reproducible. We report on how successful the two models were at categorizing solitarious, intermediate (transient) and gregarious nymph and adult samples. We highlight shortcomings and make more specific recommendations on the use of these models based on the differences they show as to their precision when categorizing the solitarious and gregarious S. gregaria nymph and adult samples.
Keywords
Locust; Phase change; Outbreak; Solitarious; Gregarious; Model
Subject
Biology and Life Sciences, Insect Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
