A population model is developed and used in conjunction with the results of a study of an unexploited population of paua (Haliotis iris Martyn) to examine the historical pattern of recruitment and yield per recruit As H iris cannot yet be aged, the population model uses size rather than age classes, but is structurally similar to the Leslie matrix model Simulations suggest that the observed population size structure resulted from a short (about 5 year) period of high recruitment, preceded and followed by longer periods of low recruitment Yield per recruit analysis shows that the present minimum legal size for the fishery (127 mm) provides close to the maximum yield per recruit for most stocks, although yield per recruit could be increased in some areas by a reduction in minimum legal size

Population dynamics and fishery management of the paua, Haliotis iris

Simulations of the kind considered in this review are models of nature, living nature. As'Such, they are subject not only to the physical constraints on flow of matter­ energy, but must conform to the multitudinous constraints required by the many species present in even the simplest of ecosystems. Indeed, it pays the ecological modeler to heed the words quoted above and to use the natural world, if not as teacher, as his guide when resolving questions of model form and function. The results of ignoring this advice are at best useless and at worst positively dangerous, for we live in a world where, more and more, decisions are shaped by the products of science and technology. One of those products that has heralded an exciting new area of ecology research is computer simulation modeling of ecosystems. I propose in this review to outline the origins, structure, and possible future of such models. My dictionary defines simulation as "the act of feigning to be what one is not; the assumption of a deceitful appearance or character." Despite the somewhat pejorative character of this definition, it does describe well enough the modeler's intent, to simplify or substitute a model for the real thing, the ecosystem. But this "simulation model" should reflect the true nature of one or more aspects of reality and should not be a "dissimulation" model, one which, by definition, conceals the truth. In scope, simulation models vary from the detailed but small models of simple thermal communities (108) or microcosms (96) to simulation models of the world (52). A word is needed about the scope of this review. I have deliberately avoided discussion of single population models or of two populations or competition models when these primarily concern predator-prey or competition theory. In the first place, the literature of predation and competition models is too vast an assemblage

Simulation Models of Ecosystems

Microcomputer fisheries simulations as training and teaching tools

The application of systems science techniques to insect pest management is a recent development, with the earliest literature appearing about 1961 (163). Since that time, many scientists across the United States and around the world have become involved. This article is neither a review of all papers published on the subject nor a review of all current research. Instead, it presents my ideas as to how systems science should be used in insect pest management. It also documents past develop­ ments to demonstrate where we stand today and speculates as to where we might go in the next few years. As my special interest is field crop entomology, the material cited in this review emphasizes the applications of the systems approach to crop protection.

Approach to pest management

An interactive optimization model for training resource managers is described. The model is presented in the form of a game in which the user plays the role of an environmental consultant to an electric utility company. The objective is to purchase land for a proposed power plant such that ecological damage is minimized, subject to a minimum acreage requirement and a maximum budget. After one or more attempts to minimize ecological damage, the user may invoke a linear programming model to find an optimal solution. The game introduces students to the complexities and uncertainties of assigning ecological value and the utility of linear programming as a management tool. The model is programmed in BASIC and will run on Tektronix 4052 (64K) or similar computer (when modified appropriately).

G.J. Paulik

Papers

9 – Digital Simulation Modeling in Resource Management and the Training of Applied Ecologists