Generating Within-Plant Spatial Distributions of an Insect Herbivore Based on Aggregation Patterns and Per-Node Infestation Probabilities.

Most predator-prey models extrapolate functional responses from small-scale experiments assuming spatially uniform within-plant predator-prey interactions. However, some predators focus their search in certain plant regions, and herbivores tend to select leaves to balance their nutrient uptake and exposure to plant defenses. Individual-based models that account for heterogeneous within-plant predator-prey interactions can be used to scale-up functional responses, but they would require the generation of explicit prey spatial distributions within-plant architecture models. The silverleaf whitefly, Bemisia tabaci biotype B (Gennadius) (Hemiptera: Aleyrodidae), is a significant pest of tomato crops worldwide that exhibits highly aggregated populations at several spatial scales, including within the plant. As part of an analytical framework to understand predator-silverleaf whitefly interactions, the objective of this research was to develop an algorithm to generate explicit spatial counts of silverleaf whitefly nymphs within tomato plants. The algorithm requires the plant size and the number of silverleaf whitefly individuals to distribute as inputs, and includes models that describe infestation probabilities per leaf nodal position and the aggregation pattern of the silverleaf whitefly within tomato plants and leaves. The output is a simulated number of silverleaf whitefly individuals for each leaf and leaflet on one or more plants. Parameter estimation was performed using nymph counts per leaflet censused from 30 artificially infested tomato plants. Validation revealed a substantial agreement between algorithm outputs and independent data that included the distribution of counts of both eggs and nymphs. This algorithm can be used in simulation models that explore the effect of local heterogeneity on whitefly-predator dynamics.