If dung beetles (Scarabaeidae: Scarabaeinae) arose in association with dinosaurs, did they also suffer a mass co-extinction at the K-Pg boundary?

The evolutionary success of beetles and numerous other terrestrial insects is generally attributed to co-radiation with flowering plants but most studies have focused on herbivorous or pollinating insects. Non-herbivores represent a significant proportion of beetle diversity yet potential factors that influence their diversification have been largely unexamined. In the present study, we examine the factors driving diversification within the Scarabaeidae, a speciose beetle family with a range of both herbivorous and non-herbivorous ecologies. In particular, it has been long debated whether the key event in the evolution of dung beetles (Scarabaeidae: Scarabaeinae) was an adaptation to feeding on dinosaur or mammalian dung. Here we present molecular evidence to show that the origin of dung beetles occurred in the middle of the Cretaceous, likely in association with dinosaur dung, but more surprisingly the timing is consistent with the rise of the angiosperms. We hypothesize that the switch in dinosaur diet to incorporate more nutritious and less fibrous angiosperm foliage provided a palatable dung source that ultimately created a new niche for diversification. Given the well-accepted mass extinction of non-avian dinosaurs at the Cretaceous-Paleogene boundary, we examine a potential co-extinction of dung beetles due to the loss of an important evolutionary resource, i.e., dinosaur dung. The biogeography of dung beetles is also examined to explore the previously proposed "out of Africa" hypothesis. Given the inferred age of Scarabaeinae as originating in the Lower Cretaceous, the major radiation of dung feeders prior to the Cenomanian, and the early divergence of both African and Gondwanan lineages, we hypothesise that that faunal exchange between Africa and Gondwanaland occurred during the earliest evolution of the Scarabaeinae. Therefore we propose that both Gondwanan vicariance and dispersal of African lineages is responsible for present day distribution of scarabaeine dung beetles and provide examples.

Assessment of crop insect damage using unmanned aerial systems: A machine learning approach

Agricultural pests are responsible for millions of dollars in crop losses and management costs every year. In order to implement optimal site-specific treatments and reduce control costs, new methods to accurately monitor and assess pest damage need to be investigated. In this paper we explore the combination of unmanned aerial vehicles (UAV), remote sensing and machine learning techniques as a promising technology to address this challenge. The deployment of UAVs as a sensor platform is a rapidly growing field of study for biosecurity and precision agriculture applications. In this experiment, a data collection campaign is performed over a sorghum crop severely damaged by white grubs (Coleoptera: Scarabaeidae). The larvae of these scarab beetles feed on the roots of plants, which in turn impairs root exploration of the soil profile. In the field, crop health status could be classified according to three levels: bare soil where plants were decimated, transition zones of reduced plant density and healthy canopy areas. In this study, we describe the UAV platform deployed to collect high-resolution RGB imagery as well as the image processing pipeline implemented to create an orthoimage. An unsupervised machine learning approach is formulated in order to create a meaningful partition of the image into each of the crop levels. The aim of the approach is to simplify the image analysis step by minimizing user input requirements and avoiding the manual data labeling necessary in supervised learning approaches. The implemented algorithm is based on the K-means clustering algorithm. In order to control high-frequency components present in the feature space, a neighbourhood-oriented parameter is introduced by applying Gaussian convolution kernels prior to K-means. The outcome of this approach is a soft K-means algorithm similar to the EM algorithm for Gaussian mixture models. The results show the algorithm delivers decision boundaries that consistently classify the field into three clusters, one for each crop health level. The methodology presented in this paper represents a venue for further research towards automated crop damage assessments and biosecurity surveillance.