Standardization of multivariate regression models for estimation of the gregariousness level of the main pest locust

Abstract

Recurrent locust population outbreaks have a tremendous impact on ecosystems and economies around the globe. These population outbreaks are associated with a shift from the usual solitary locust phase to a gregarious one. Most molecular research on locusts is focused on uncovering the basis underlying the change to gregariousness. With the increasing availability of ‘omics’ data, the research on this subject is entering a functional testing era. In order to successfully test functionality of a gene or molecule, quantitative measurements of the level of gregariousness are needed. Currently no valid molecular marker is available, thus the assessment of the degree of locust gregariousness is based on mathematical modeling. However, the absence of one single model means heterogeneity and implies that researchers have to spend time and effort building one for their specific experiments. Here, we offer a script to simplify extraction of the data from locust behavioral video tracks. We also suggest two mathematical equations for assessing the levels of gregariousness of one of the most notorious pest locusts – Schistocerca gregaria (Forskål) (Orthoptera: Acrididae). One model uses morphometric variables and is valid for the comparison of nymphs, whereas the other has no morphometric variables and is for testing the adults and the same specimens of non-molted nymphs. The sensitivity of these models was optimized and the effects of sex, developmental stage, and locust size were considered. The models were tested with independent samples and were found to work both for solitary and gregarious locusts. They were also found to be quantitative and could be used to distinguish populations of different densities and states. We therefore offer accessible, reliable, time, and effort-saving tools for researchers to use. The use of these models by multiple laboratories would standardize and homogenize methodologies to the benefit of reliable results and interpretations.