A process-based, stage-structured model of potato cyst nematode population dynamics: Effects of temperature and resistance

Development and Validation of a Resistance Management Model for the Soybean Cyst Nematode, Heterodera glycines

Plant-parasitic nematodes are a key yield-limiting pest of crops around the world. Deployment of plant resistance genes are an important management tactic for many economically important plant-parasitic nematodes. The selection for virulence in nematode populations is a major threat to the effectiveness of resistance gene-based management. Little research has gone into resistance management modelling despite the importance of both plant-parasitic nematodes and resistance genes for their management. In this paper, we report on a cyst nematode resistance management model created to explore the factors which are most important for determining the durability of resistance genes to this important family of plant-parasitic nematodes. The relative dominance of virulence expression, the level of inbreeding, and the number of generations per cropping season were the most important factors in predicting resistance gene durability. Aspects of cyst nematode biology that reduce the number of generations per season for a portion of the population had a much smaller effect on the durability of resistance genes. These factors included delayed hatching within a season and early dormancy. The accuracy and utility of the model was tested using the soybean cyst nematode (SCN) rhg1-mediated resistance system. The model accurately predicted the rate at which virulence to the rhg1b resistance gene developed in Iowa over a two-decade period. The model suggested resistance gene pyramids as the most durable management solution for SCN with multiple possible avenues to obtain acceptable efficacy and durability.

Stochastic modeling of Dalbulus maidis, vector of maize diseases

We developed a simple linear stochastic model for Dalbulus maidis dependent exclusively on temperature, whose parameters were determined from published field and laboratory studies performed at different temperatures. This model takes into account the principal stages and events of the life cycle of this pest, which is vector of maize diseases. We implemented the effect of distributed delays or Linear Chain Trick (LCT) considering a fixed number of sub-stages for egg and nymph stages of Dalbulus maidis in order to accurately represent what is observed in nature. A sensitivity analysis allows us to observe that the speed of the dynamics is sensitive to changes in the development rates, but not to the longevity of each stage or the fecundity, which almost exclusively affect insect abundance. We used our model to study its predictive and explanatory capacity considering a published experiment as a case study. Although the simulation results show a behavior qualitatively equivalent to that observed in the experimental results it is not possible to explain accurately the magnitude, nor the times in which the maximum abundances of second-generation nymphs and adults are reached. Therefore, we evaluated three possible scenarios for the insect that allow us to glimpse some of the advantages of having a computational model in order to find out what processes, taken into account in the model, may explain the differences observed between published experimental results and model results. The three proposed scenarios, based on variations in the parameterized rates of the model, can satisfactorily explain the experimental observations. We observed that in order to better simulate the experimental results it is not necessary to modify fecundity or mortality rates. However, it is necessary to accelerate the average development rates of our model by 20 to 40 %, compatible with extreme values of the rates close to the upper edges of the confidence bands of our parameterization rate curves, according to insects with faster development rates already reported in literature.

Temperature sensitivity of pest reproductive numbers in age-structured PDE models, with a focus on the invasive spotted lanternfly

Invasive pest establishment is a pervasive threat to global ecosystems, agriculture, and public health. The recent establishment of the invasive spotted lanternfly in the northeastern United States has proven devastating to farms and vineyards, necessitating urgent development of population dynamical models and effective control practices. In this paper, we propose a stage-age-structured system of PDEs to model insect pest populations, in which underlying dynamics are dictated by ambient temperature through rates of development, fecundity, and mortality. The model incorporates diapause and non-diapause pathways, and is calibrated to experimental and field data on the spotted lanternfly. We develop a novel moving mesh method for capturing age-advection accurately, even for coarse discretization parameters. We define a one-year reproductive number ([Formula: see text]) from the spectrum of a one-year solution operator, and study its sensitivity to variations in the mean and amplitude of the annual temperature profile. We quantify assumptions sufficient to give rise to the low-rank structure of the solution operator characteristic of part of the parameter domain. We discuss establishment potential as it results from the pairing of a favorable [Formula: see text] value and transient population survival, and address implications for pest control strategies.