Abstract. The impacts of climate change on forest ecosystems are likely to require changes in forest planning and natural resource management. Changes in tree growth, disturbance extent and intensity, and eventually species distributions are expected. In natural resource management and planning, ecosystem models are typically used to provide a ''best estimate'' about how forests might work in the future and thus guide decision-making. Ecosystem models can be used to develop forest management strategies that anticipate these changes, but limited experience with models and model output is a challenge for managers in thinking about how to address potential effects of climate change. What do decision makers need to know about climate models, ecological models used for impacts assessments, and the uncertainty in model projections in order to use model output in strategies for adaptation to climate change? We present approaches for understanding and reducing the uncertainty associated with modeling the effects of climate change on ecosystems, focusing on multi-model approaches to clarify the strengths and limits of projections and minimize vulnerability to undesirable consequences of climate change. Scientific uncertainties about changes in climate or projections of their impacts on resources do not present fundamental barriers to management and adaptation to climate change. Instead, many of these uncertainties can be controlled by characterizing their effects on models and future projections from those models. There is uncertainty in decision making that does not derive just from the complex interaction of climate and ecosystem models, but in how modeling is integrated with other aspects of the decision environment such as choice of objectives, monitoring, and approach to assessment. Adaptive management provides a hedge against uncertainty, such that climate and ecosystem models can inform decision making.

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Yellow cedar {Gbamaecyparis nootkatensis) is a valuable tree speeies that is experiencing an extensive forest decline on over 200 000 ha of unmanaged lorest in southeast Alaska. Biotic factors appear secondary and some abiotie factor is probably the primary eause of this naturally occurring deeline. A warming elimate, which coincided with the onset of extensive tree mortality about 100 years ago, may have triggered one of the possible abiotic causes such as freezing damage and/or soil toxieity.

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We used exogenous application of a phytohormone (gibberellin GA3, ‘GA’) to test the hypothesis that common perennial grasses may not be growing at all times to the limit of resource availability. Plants were taken from the field in winter, and again in summer and their responses to GA assessed under standard conditions, indoors, to reveal their ‘potential’ for growth at different times. Time of year, and associated developmental state, had a major impact on the capacity of plants to respond to exogenous GA, and less so their current growing conditions, temperature and N availability, during measurement. A major increase in dry matter (DM) production in winter‐derived plants took place at both low and high N, with no evidence of a reduction in N content in tissues. That ryegrass plant growth can be stimulated, without externally adding resources, supports the hypothesis there is an element of internal control in how plants respond to ‘signals’ in their environment, that might be manipulated. This offers prospects for reducing environmental impacts (leaching, N2O) compared with obtaining the same yield increase by adding fertilizer N in early season. Responses to exogenous GA were detected (as significant) but far smaller in summer‐derived plants. Molecular mechanisms of detection of N resource signals, developmental triggers and the role of endogenous gibberellin need to be unravelled to assess scope for breeding ryegrass germplasm to better match demands for increased production with greater resource‐use efficiency.

Control of Armillaria root rot through the use of resistant species, avoidance of hazardous sites, cultural manipulation, chemical applications, biological methods, and integrated biological methods are dis‐cussed. The need for a critical evaluation of disease impact and a financial analysis of control costs and benefits are emphasized. Avoiding establishment of plantations on sites likely to have a high disease hazard or the removal of substrate sources through uprooting stumps and dislodging root remanents currently appear to offer the most effective means of control.

Evaluation of the use of scientific information in developing the 1997 Forest plan for the Tongass National Forest. Gen. Tech. Rep. PNW-GTR-415. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. 69 p. (Shaw, Charles G., III, tech. coord.; Conservation and resource assessments for the Tongass land management plan revision). The Tongass National Forest is the largest remaining relatively unaltered coastal temperate rain forest in the world. The Forest consists of 16.9 million acres of land distributed across more that 22,000 islands and a narrow strip of mainland in southeast Alaska. The Forest contains abundant timber, wildlife, fisheries, mineral, and scenic resources. The authors participated as scientists on the Tongass Land Management Planning Team from 1995 to 1997. We joined the planning team as full members but maintained separate and distinct roles from National Forest System members. We were asked to assure that credible, value-neutral, scientific information was developed independently without reference to management decisions. We also displayed the likely levels of risk to resources and society associated with various management options. We examined how scientific information was used in making management decisions and evaluated whether the decisions were consistent with the available information. We developed and used a set of criteria to evaluate the way in which managers used scientific information in formulating decisions. This evaluation began while the final alternative was in the formative stages so that managers could alter their management approach, if they so desired, before the Forest plan was finalized. Many management decisions were altered during this "adaptive decisionmaking process" in which changes were made concurrent with iterations of this paper. Our conclusion was that the final management decisions made in developing the 1997 Forest plan achieved a high degree of consistency with the available scientific information. This paper does not consider any information gathered after the signing of the record of decision on May 23, 1997, or deal with subsequent implementation of the 1997 Tongass Forest plan.

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