Quantitative measure of fitness in tri-trophic interactions and its influence on diet breadth of insect herbivores

Inertial Sensor-Based Sport Activity Advisory System Using Machine Learning Algorithms

The aim of this study was to develop a physical activity advisory system supporting the correct implementation of sport exercises using inertial sensors and machine learning algorithms. Specifically, three mobile sensors (tags), six stationary anchors and a system-controlling server (gateway) were employed for 15 scenarios of the series of subsequent activities, namely squats, pull-ups and dips. The proposed solution consists of two modules: an activity recognition module (ARM) and a repetition-counting module (RCM). The former is responsible for extracting the series of subsequent activities (so-called scenario), and the latter determines the number of repetitions of a given activity in a single series. Data used in this study contained 488 three defined sport activity occurrences. Data processing was conducted to enhance performance, including an overlapping and non-overlapping window, raw and normalized data, a convolutional neural network (CNN) with an additional post-processing block (PPB) and repetition counting. The developed system achieved satisfactory accuracy: CNN + PPB: non-overlapping window and raw data, 0.88; non-overlapping window and normalized data, 0.78; overlapping window and raw data, 0.92; overlapping window and normalized data, 0.87. For repetition counting, the achieved accuracies were 0.93 and 0.97 within an error of ±1 and ±2 repetitions, respectively. The archived results indicate that the proposed system could be a helpful tool to support the correct implementation of sport exercises and could be successfully implemented in further work in the form of web application detecting the user's sport activity.

Geography is more important than host plant use for the population genetic structure of a generalist insect herbivore

Population divergence can occur due to mechanisms associated with geographic isolation and/or due to selection associated with different ecological niches. Much of the evidence for selection-driven speciation has come from studies of specialist insect herbivores that use different host plant species; however, the influence of host plant use on population divergence of generalist herbivores remains poorly understood. We tested how diet breadth, host plant species and geographic distance influence population divergence of the fall webworm (Hyphantria cunea; FW). FW is a broadly distributed, extreme generalist herbivore consisting of two morphotypes that have been argued to represent two different species: black-headed and red-headed. We characterized the differentiation of FW populations at two geographic scales. We first analysed the influence of host plant and geographic distance on genetic divergence across a broad continental scale for both colour types. We further analysed the influence of host plant, diet breadth and geographic distance on divergence at a finer geographic scale focusing on red-headed FW in Colorado. We found clear genetic and morphological distinction between red- and black-headed FW, and Colorado FW formed a genetic cluster distinct from other locations. Although both geographic distance and host plant use were correlated with genetic distance, geographic distance accounted for up to 3× more variation in genetic distance than did host plant use. As a rare study investigating the genetic structure of a widespread generalist herbivore over a broad geographic range (up to 3,000 km), our study supports a strong role for geographic isolation in divergence in this system.