No abstract

/ The risk tropospheric ozone poses to forests in the United States is dependent on the variation in ozone exposure across the distribution of the forests in question and the various environmental and climate factors predominant in the region. All these factors have a spatial nature, and consequently an approach to characterization of ozone risk is presented that places ozone exposure-response functions for species as seedlings and model-simulated tree and stand responses in a spatial context using a geographical information systems (GIS). The GIS is used to aggregate factors considered important in a risk characterization, including: (1) estimated ozone exposures over forested regions, (2) measures of ozone effects on species' and stand growth, and (3) spatially distributed environmental, genetic, and exposure influences on species' response to ozone. The GIS-based risk characterization provides an estimation of the extent and magnitude of the potential ozone impact on forests. A preliminary risk characterization demonstrating this approach considered only the eastern United States and only the limited empirical data quantifying the effect of ozone exposures on forest tree species as seedlings. The area-weighted response of the annual seedling biomass loss formed the basis for a sensitivity ranking: sensitive-aspen and black cherry (14%-33% biomass loss over 50% of their distribution); moderately sensitive-tulip popular, loblolly pine, eastern white pine, and sugar maple (5%-13% biomass loss); insensitive-Virginia pine and red maple (0%-1% loss). In the future, the GIS-based risk characterization will include process-based model simulations of the three- to 5-year growth response of individual species as large trees with relevant environmental interactions and model simulated response of mixed stands. The interactive nature of GIS provides a tool to explore consequences of the range of climate conditions across a species' distribution, forest management practices, changing ozone precursors, regulatory control strategies, and other factors influencing the spatial distribution of ozone over time as more information becomes available.KEY WORDS: Ecological risk assessment; GIS; Ozone; Risk characterization; Forests; Trees

This paper addresses common patterns of plant C allocation in response to stress. The ROPIS studies used species ranging from slow growing, long‐lived evergreen trees (red spruce [Picea rubens Sarg.] and ponderosa pine [Pinus ponderosa Dougl.]), to fast growing evergreen and deciduous trees (loblolly pine [Pinus taeda L.] and aspen [Populus tremulaides Michx.]) and annuals (radish [Raphanus sativus L.]). Several factors helped to explain the effects of ozone, the common stress in all ROPIS experiments, on allocation in these diverse species. Species with high relative growth rates readily changed allocation in response to stress. For example, radish and aspen allocated C to produce new leaves in response to ozone and allocated C to roots in response to water and N deficits. In contrast, red spruce had the lowest relative growth rate, and neither total plant biomass nor C allocation were affected by ozone after four growing seasons. However, partitioning of C to foliar starch reserves was reduced. Ponderosa pine and loblolly pine had intermediate relative growth rates. Ozone reduced total plant biomass and allocation to coarse roots in ponderosa pine, while total plant biomass but not allocation was reduced in loblolly pine. Radish, aspen, and ponderosa pine all maintain low foliar starch reserves and experienced ozone‐induced foliar senescence. In contrast, red spruce and loblolly pine maintain substantial foliar starch reserves, which were reduced by ozone. However, they did not experience ozone‐induced senescence. While fast growing species showed the greatest changes in allocation in response to stress, we do not suggest that rapidly growing plants are more sensitive to stress. We suggest they have a higher capacity to allocate C to compensate for the stress. Slower growing species rely more on C storage or multiple shoot growth periods within the growing season to respond to stresses that alter the pattern of C allocation in faster growing species.

Characterizing species at risk II: using Bayesian belief networks as decision support tools to determine species conservation categories under the Northwest Forest Plan. Ecology and Society 11(2): 12.