IMPROVING AQUATIC HABITAT CONDITIONS OVER TIME WHILE PRODUCING WOOD PRODUCTS: AN EXAMINATION OF OPTIONS1

Abstract

ABSTRACT: A land management activity scheduling model that can perform a multi‐period, simultaneous evaluation of aquatic habitat quality and commodity production goals was used to identify alternatives which would allow the improvement of aquatic habitat conditions over time, while producing wood products. The scheduling model has the ability to use stream sediment index levels, stream temperature index levels, and equivalent clearcut acres (ECA) levels as primary goals. A secondary goal imbedded in the model is the achievement of an even‐flow of timber harvest volume, and a tertiary goal is the achievement of maximum efficiency (maximum net present value). The scheduling model utilizes a heuristic programming technique (Tabu search) to guide the selection of timber harvests and road standards. A 14,643 acre case study watershed in eastern Oregon is used to illustrate several policy scenarios. Activities considered include: clearcutting and partial cutting; cable, skyline, ground‐based, and helicopter logging; road obliteration; requiring lower truck tire pressures on forest roads; and tree planting in riparian areas. The scheduling model produced land management plans which were spatially and temporally feasible over ten ten‐year time periods. Stream temperature was shown to be dramatically reduced if tree planting is performed in all riparian areas, regardless of whether harvesting activities occurred, and including meadows and forested areas where shade density is low. Timber harvest volume levels decreased 31 to 43 percent, and net present value levels decreased 36 to 46 percent, from an unconstrained case, when any of the following occurred: ECA was constrained to 15 percent, sediment index levels were required to decrease by 1 percent per decade, or temperature levels were constrained to “no harvest” levels. The use of a heuristic programming technique is a departure from traditional techniques that are commonly used in management plan development. Yet the heuristic technique allows the inclusion of complex management goals, many of which may be prohibited when using more traditional mathematical programming techniques. In addition, decision variables which require spatial information, requiring them to take on integer or non‐linear representations, can be accounted for without realizing the limitations of the traditional techniques.