Load BLKDTA Simulation control dat JA BOWA the main program 100 call QUERY displays parameter and program information al ch Kfirst=O call LOAD * The Shannon index of species diversity, H' = IE-pilogpi, where pi is the probability of selecting an individual of species i by a random selection process, is used (Pielou 1966).

The conservation of biological diversity has become one of the important goals of managing forests in an ecologically sustainable way. Ecologists and forest resource managers need measures to judge the success or failure of management regimes designed to sustain biological diversity. The relationships between potential indicator species and total biodiversity are not well established. Carefully designed studies are required to test relationships between the presence and abundance of potential indicator species and other taxa and the maintenance of critical ecosystem processes in forests. Other indicators of biological diversity in forests, in addition or as alternatives to indicator species, include what we call structure‐based indicators. These are stand‐level and landscape‐level (spatial) features of forests such as stand structural complexity and plant species composition, connectivity, and heterogeneity. Although the adoption of practices to sustain (or recreate) key characteristics of forest ecosystems appear intuitively sensible and broadly consistent with current knowledge, information is lacking to determine whether such stand‐ and landscape‐level features of forests will serve as successful indices of (and help conserve) biodiversity. Given our limited knowledge of both indicator species and structure‐based indicators, we advocate the following four approaches to enhance biodiversity conservation in forests: (1) establish biodiversity priority areas (e.g., reserves) managed primarily for the conservation of biological diversity; (2) within production forests, apply structure‐based indicators including structural complexity, connectivity, and heterogeneity; (3) using multiple conservation strategies at multiple spatial scales, spread out risk in wood production forests; and (4) adopt an adaptive management approach to test the validity of structure‐based indices of biological diversity by treating management practices as experiments. These approaches would aim to provide new knowledge to managers and improve the effectiveness of current management strategies.

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Simulations of cool-temperate forest growth in response to climatic change using the JABOWA computer model show that a decrease of 600 growing degree-days (equivalent to a 2°C decrease in mean annual temperature) causes red spruce (Picea rubens) to replace sugar maple (Acer saccharum) as the dominant tree. These changes are delayed 100–200 yr after the climatic cooling, producing gradual forest changes in response to abrupt temperature changes, and reducing the amplitude of response to brief climatic events. Soils and disturbances affect the speed and magnitude of forest response. The delayed responses are caused by the difference in sensitivity of adult trees and younger stages. The length of the delay depends on the life history characteristics of the dominant species. Delayed responses imply that fossil pollen deposits, even if they faithfully record the abundances of trees in forests, may not be able to resolve climatic changes within 100–200 yr, or to record very brief climatic events. This explains why pollen deposits do not as yet show responses to climatic changes during the past 100 yr. Only the Little Ice Age, which lasted several centuries, caused sufficient forest change to be recorded in fossil pollen, and only at certain sites.

JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.. Ecological Society of America is collaborating with JSTOR to digitize, preserve and extend access to Ecology.Abstract. The spatial pattern of and the transition rates between forest ecological states were inferred for g260 000 pixel-sized (3600 M2) landscape units using satellite remote sensing. Transition rates were estimated from 1973 to 1983 Landsat images of the study area, classified into ecological states associated with forest succession. The effects of classification error on transition rate estimates were modeled and error adjustments made. Classification of the 1973 and 1983 Landsat images of the 900 km2 study region required a relatively small set of ground-observed and photo-interpreted plots in 1983, with a total area of just 1.62 km2. An innovative technique for correcting multiyear Landsat images for between-image differences in atmospheric effects and sensor calibration, permitted classification of the 1973 Landsat image using 1983 ground observations. Given current Landsat data, and ground observations in one year, this technique would permit monitoring of forest succession and dynamics for nearly a 20-yr period.Results of applying these techniques to a forest ecosystem showed that during the 10yr observation period it was patchy and dynamic. For both a wilderness and a nonwilderness area in the study region, sizeable values of transition rates were observed and over half of the landscape units were observed to change state; however, a Markov analysis, using the observed transition probabilities, suggests that at the regional level neither the wilderness nor the nonwilderness areal proportions of ecological states are undergoing rapid change.

We describe broadly applicable principles for the conservation of wild living resources and mechanisms for their implementation. These principles were engendered from three starting points. First, a set of principles for the conservation of wild living resources (Holt and Talbot 1978) required reexamination and updating. Second, those principles lacked mechanisms for implementation and consequently were not as effective as they might have been. Third, all conservation problems have scientific, economic, and social aspects, and although the mix may vary from problem to problem, all three aspects must be included in problem solving. We illustrate the derivation of, and amplify the meaning of, the principles, and discuss mechanisms for their implementation. The principles are: Principle I. Maintenance of healthy populations of wild living resources in perpetuity is inconsistent with unlimited growth of human consumption of and demand for those resources. Principle II. The goal of conservation should be to secure present and future options by maintaining biological diversity at genetic, species, population, and ecosystem levels; as a general rule neither the resource nor other components of the ecosystem should be perturbed beyond natural boundaries of variation. Principle III. Assessment of the possible ecological and sociological effects of resource use should precede both proposed use and proposed restriction or expansion of ongoing use of a resource. Principle IV. Regulation of the use of living resources must be based on understanding the structure and dynamics of the ecosystem of which the resource is a part and must take into account the ecological and sociological influences that directly and indirectly affect resource use. Principle V. The full range of knowledge and skills from the natural and social sciences must be brought to bear on conservation problems. Principle VI. Effective conservation requires understanding and taking account of the motives, interests, and values of all users and stakeholders, but not by simply averaging their positions. Principle VII. Effective conservation requires communication that is interactive, reciprocal, and continuous. Mechanisms for implementation of the principles are discussed.

Predictions of phytoplankton growth dynamics and nutrient assimilation by a computer simulation model are consistent with studies of field and laboratory populations. The model simulates population dynamics and gross physiology of phytoplankton species in