The Multivariate Regression Models Suggested as Standardising Tools for Categorising Solitarious and Gregarious Groups of the Main Pest Locust, Schistocerca gregaria, Produce Reproducible Results

Abstract

Outbreaks of the desert locust Schistocerca gregaria affect some of the poorest parts of Africa, with devastating outcomes. The key to understanding and dealing with this problematic adaptation to environmental changes is comparing gregarious and solitarious locusts, either in nature or in laboratories. Categorising locusts and detecting changes in their phase status is key to such comparisons, which have been hitherto based on applying mathematical models that use behavioural parameters and that each laboratory has to build anew for each experiment. All the models used thus far are different from one another. This implies differences in the tools used for the different experiments and by the different laboratories and, thus, potential noise in the results and interpretations. Standardising the way locusts are categorised is necessary if we want to reduce noise and errors. It is crucial if we seek to make the results and interpretations transferable and comparable between experiments and laboratories for such an important research area. To tackle this problem, we suggested two models as possible standardising tools. However, the problem of a lack of standardised tools re-emerged due to the doubts cast on the validity of those models. Here, we use samples from independent S. gregaria populations in order to test and validate those models. We discuss how successful the two models were at categorising solitarious, intermediate (transient), and gregarious nymph and adult S. gregaria samples. We highlight shortcomings and make more specific recommendations on the use of those models based on the precision differences they show when categorising solitarious and gregarious S. gregaria nymph and adult samples. Overall, both models have proven to be valid since their results were largely replicated and seem reproducible.