Random Search by Herbivorous Insects: A Simulation Model

Abstract

A stochastic simulation model that relates the success of various herbivore searching behaviors to the density and arrangement of food plants was developed. It was designed to be fieldtestable with all model parameters directly measurable in field experiments. The model uses probability distributions and parameter ranges that are in accordance with published herbivore movement data.In the model, herbivore insect searching success depends in a complex manner upon parameters such as insect move lengths, turning angle concentration, reactive distance, risk of mortality while searching, plant density, plant dispersion, and plant stand size. Simulation results preclude simple generalizations and indicate that the outcome of interactions between plant density, plant dispersion, and herbivore searching behavior depends heavily upon parameter values. I argue that species-tospecies shifts in the details of the simple searching process that I simulate could explain the (experimentally observed) high variation in herbivore response to vegetation texture.I discuss plant and insect strategies that are "optimal" within biological constraints. For example, results from the model suggest that under many conditions clumping by plants can be an effective escape strategy from searching herbivores. This contradicts the claim that plant spatial distribution does not affect insect searching success, a claim that is justified only if it is assumed that organisms search (infinitely long) without biological cost. The importance of considering the cost of ineffective search is emphasized.

Fractal reorientation clocks: Linking animal behavior to statistical patterns of search

“…In the simplest case (without intermittence) we model organisms' local scanning behavior by means of correlated random walks (CRW). CRW are a natural way to model the emergence of angular correlations in animal trajectories coming from local scanning behavior (2,(20)(21)(22)(23). In the intermittent model, we generate a class of random walks, named Lévy-modulated CRW (LMCRW), by incorporating a time-discrete reorientation behavior (with Lévy statistics) into the background continuous scanning process modeled as a CRW (7).…”

The green leafhopper,Cicadella viridis(Hemiptera, Auchenorrhyncha, Cicadellidae), jumps with near-constant acceleration

“…However, their small scale has made them easy targets for a wide number and variety of larger predators. During evolution they have refined many different escaping strategies (Cain, 1985;Masters and Eisner, 1990). One of the main methods used by insects to escape from predators is to increase the speed of locomotion (Bennet-Clark and Lucey, 1967;Evans, 1972;Christian, 1978;Brackenbury and Hunt, 1993;Maitland, 1992).…”

Prey aggregation is an effective olfactory predator avoidance strategy

“…Aggregation into groups is a common 42 response to the risk of predation (Krause and Ruxton 2002). Grouping individuals benefit 43 from the dilution effect if a predator is unable to consume all prey in a group (Foster and Prey detection is likely to be dependent on a predator's sensory acuity and modality 51 (Cain 1985). Theory predicts that as a group of prey grows, the ability of a visual predator to 52 detect the group will increase at a slower rate; that is, a group of N individuals should be less 53 than N times more detectable than a single individual (Brock and Riffenburgh 1960;54 Treisman 1975; Turner and Pitcher 1986).…”

“…Treisman (1975) suggests that a group of N individuals should be detectable by an 64 olfactory predator at a distance N times as great as that for a single prey, resulting in an area 65 in which the group can be detected N 2 times as large as for a single prey (or a volume N 3 66 times as large). If this is the case, encounter-dilution would not take place, and grouping 67 would not be favoured unless the predator is highly sensitive to olfactory cues and does not 68 preferentially target large groups over small ones (Cain 1985). Recent empirical data…”

“…The benefits of aggregation are likely to depend on the sensory abilities of the 374 predator and a predator that is unable to detect prey will approach random search efficiency 375 (Cain 1985). However, a predator that is able to detect the presence of prey and perhaps even 376 an indication of the number of prey should perform better than random by increased search 377 effort, especially if that effort can be focused in the general area surrounding prey.…”