A model for the corn rootworm Diabrotica spp. combined with a temporally explicit model for development of corn roots across the soil profile was developed to link pest ecology, root damage and yield loss. Development of the model focused on simulating root damage from rootworm feeding in accordance with observations in the field to allow the virtual testing of efficacy from management interventions in the future. We present the model and demonstrate its applicability for simulating root damage by comparison between observed and simulated pest development and root damage (assessed according to the node injury scale from 0 to 3) for field studies from the literature conducted in Urbana, Illinois (US), between 1991 and 2014. The model simulated the first appearance of larvae and adults to within a week of that observed in 88 and 71 % of all years, respectively, and in all cases to within 2 weeks of the first sightings recorded for central Illinois. Furthermore, in 73 % of all years simulated root damage differed by <0.5 node injury scale points compared to the observations made in the field between 2005 and 2014 even though accurate information for initial pest pressure (i.e. number of eggs in the soil) was not measured at the sites or available from nearby locations. This is, to our knowledge, the first time that pest ecology, root damage and yield loss have been successfully interlinked to produce a virtual field. There are potential applications in investigating efficacy of different pest control measures and strategies. (open access in Journal of Pest Science)
The General Unified Threshold model of Survival (GUTS) provides a consistent mathematical framework for survival analysis. However, the calibration of GUTS models is computationally challenging. We present a novel algorithm and its fast implementation in our R package, GUTS, that help to overcome these challenges. We show a step-by-step application example consisting of model calibration and uncertainty estimation as well as making probabilistic predictions and validating the model with new data. Using self-defined wrapper functions, we show how to produce informative text printouts and plots without effort, for the inexperienced as well as the advanced user. The complete ready-to-run script is available as supplemental material. We expect that our software facilitates novel re-analysis of existing survival data as well as asking new research questions in a wide range of sciences. In particular the ability to quickly quantify stressor thresholds in conjunction with dynamic compensating processes, and their uncertainty, is an improvement that complements current survival analysis methods. PLoS Comput Biol 12(6): e1004978.
Environmental risk assessment of chemicals is essential but often relies on ethically controversial and expensive methods. We show that tests using cell cultures, combined with modeling of toxicological effects, can replace tests with juvenile fish. Hundreds of thousands of fish at this developmental stage are annually used to assess the influence of chemicals on growth. Juveniles are more sensitive than adult fish, and their growth can affect their chances to survive and reproduce. Thus, to reduce the number of fish used for such tests, we propose a method that can quantitatively predict chemical impact on fish growth based on in vitro data. Our model predicts reduced fish growth in two fish species in excellent agreement with measured in vivo data of two pesticides. This promising step toward alternatives to fish toxicity testing is simple, inexpensive, and fast and only requires in vitro data for model calibration. (Published in Science Advances, open access)