ADAPTIVE FREQUENCY CLASSIFICATION - A NEW METHODOLOGY FOR PEST MONITORING AND ITS APPLICATION TO EUROPEAN RED MITE (PANONYCHUS-ULMI, ACARI,TETRANYCHIDAE)

We developed a new method for monitoring pests over a growing season a nd applied the method to monitoring the European red spider mite (Pano nychus ulmi Koch) in apples. We evaluated the performance of the monit oring method in this system by simulation experiments and a held test in 28 orchard blocks in New York State, USA in 1995. The method is bas ed on serially linking sample occasions (bouts) in time. At each sampl e bout, the monitoring procedure decides between intervening or not in tervening. in the case of no intervention, the procedure schedules the next sample bout on the basis of an estimate of the current density, a descriptive population growth model (exponential for mites) and inte rvention thresholds. The next sample bout is scheduled when the risk o f the pest density becoming greater than a future intervention thresho ld exceeds a specified tolerance. The name of the sampling method refl ects this adaptiveness of the sampling frequency. The sampling protoco l is constructed by combining a sequential probability ratio test for taking time-efficient invervention decisions at high density with a fi xed sample size estimation at low density. The low-density estimates a re used for calculating the waiting times until the next sample. The p robabilities of the intervene or wait decisions and the expected sampl e sizes for these sampling protocols were calculated as functions of t he pest density by Monte Carlo simulation. The expected performance ch aracteristics of the monitoring method for a given population trajecto ry are estimated by integrating the performance criteria of the sampli ng protocols over a population trajectory. We simulated the performanc e of the monitoring method for two sets of 400 simulated population tr ajectories. The trajectories were calculated with an exponential polyn omial equation with random parameters. The trajectories in the first s et were characterized by low growth rates and a maximum density below the intervention threshold while the trajectories in the other set had high growth rates and a maximum density above the threshold. As perfo rmance criteria we used the probability of intervening. the number of scheduled sample bouts, the total number of sample units. the cumulati ve mite density per leaf (mite days) and the mite density at the time of intervention. Simulation indicated that the adaptive frequency clas sification would have similar performance characteristics to existing monitoring methods when monitoring rapidly growing populations, while substantial savings on the number of sample bouts would be expected wi th slowly growing populations. The field test confirmed these predicti ons and demonstrated the savings on sample bouts that are obtained by using the adaptive frequency classification.