STOCHASTIC SIMULATION FOR CHARACTERIZING ECOLOGICAL SPATIAL PATTERNS AND APPRAISING RISK

The theory and a case study are presented for a class of techniques kn own as stochastic simulation. Stochastic simulations can characterize the certainty of estimates of spatially and/or temporally correlated e cological variables. Rather than merely providing a unique estimate, a conditional probability distribution is built for the unsampled locat ion. This distribution provides the researcher with any summary statis tic or confidence limit desired. Moreover, the techniques are flexible enough to incorporate expected economic losses into the analysis. A s imple analogy of a jigsaw puzzle is used first to introduce key concep ts. Then, the mathematical highlights of two leading stochastic simula tion procedures are presented. Finally, one simulation method, known a s sequential Gaussian conditional simulation, is used to generate mult iple, equally probable images of adult corn rootworm densities over a large (225 x 150 km) area in northwestern Iowa during the summer of 19 89. The results show the simulated density of rootworms to be influenc ed strongly by the choice of summary statistic and density threshold. Economic risk is appraised fram the point of view of the farmer by inc orporating the expected economic losses due to the use of a soil insec ticide. Since the cost to the farmer of not using an insecticide when in fact it is needed is over three times greater than the cost of usin g one when it is not needed, the area identified as potentially requir ing treatment is much larger than when a summary statistic like the me an or median is used. Stochastic simulation allows the environmental r esearcher, policy-maker, or manager the opportunity to characterize un certainty and economic or other losses, and to determine areas requiri ng treatment and additional samples.