We describe methodologies currently in use or those under development containing features for estimating fire occurrence risk assessment. We describe two major categories of fire risk assessment tools: those that predict fire under current conditions, assuming that vegetation, climate, and the interactions between them and fire remain relatively similar to their condition during recent history, and those that anticipate changes in fire risk as climate and vegetation communities change through time. Three types of models have proven useful for predicting fire under current conditions: (1) biophysical models that predict fire from vegetation type, fuel load, and climate; (2) statistical models; and (3) fire behavior models. Programs such as LANDFIRE have great promise for using biophysical properties to estimate risk. Statistical models that use historical data to predict fire probabilities if landscape-fire relationships continue to remain relatively unchanged, are gaining interest as more data become available. Fire behavior models are producing accurate predictions of the ways individual fires will move across the landscape. For longer periods, fire risk needs to be evaluated by models that predict the ways vegetation communities will change over time because these changes will alter fire probabilities. We identified models capable of being used to track changes in vegetation and the resulting effect on changes in fire frequency. Risk systems need to be designed to track changes in fire susceptibility as the climate changes, using models such as MAPSS. Prediction of fire occurrence is just the first part of a complete analysis of risks associated with fire. Fire occurrence risk needs to be combined with models that determine the risk of the effects of fire. Models that predict mortality, fuel consumption, smoke production, and soil heating caused by prescribed fire or wildfire should be used, as well as those capable of evaluating second order effects, such as changes in site productivity, animal use, insects, and disease. Fire must be looked at in the context of other stresses, such as invasive insects and pathogens, encroaching urbanization, and loss of critical habitat. There are interactions among stresses that play a role in affecting the frequency and intensity of fire, and fire, in turn, can affect the probability of those stresses. Consequently, risk evaluation systems need to be created that can simultaneously estimate the probability of other major stresses influencing ecosystem development.

We present a Bayesian parameter estimation technique for improving estimates of simulation model input parameters and apply the technique to the nitrogen cycle model SINIC, which has been used to simulate the streamflow and streamflow nitrate flux at Hubbard Brook Watershed 6, New Hampshire, during the 1964-1994 period. Uncertainty in initial estimates of model input parameters was incorporated by replacing each estimate with a probability distribution of values, or "prior" distribution, usually centered at the initial estimate and having a large variance. These prior distributions were then ``updated" by incorporating available data on model output variables, producing a "posterior" probability distribution of parameter values. Several key parameters used for calculating the N mineralization rate were identified as controlling the predicted nitrate export from this watershed. The level of uncertainty in these parameters was substantially reduced by incorporating the observations on streamflow and streamflow nitrate flux. The posterior distribution of predicted yearly streamflow nitrate flux shifted from year to year, with relatively large uncertainties in years with high streamflow nitrate flux.

During this project we experimentally evaluated the below-ground biomass and carbon allocation and partitioning of four different fast- and slow-growing families of loblolly pine located in Scotland County, NC, in an effort to increase the long-term performance of the crop. The trees were subjected to optimal nutrition and control since planting in 1993. Destructive harvests in 1998 and 2000 were used for whole?plant biomass estimates and to identify possible family differences in carbon acquisition (photosynthesis) and water use efficiency. At regular intervals throughout each year we sampled tissues for carbohydrate analyses to assess differences in whole-tree carbon storage. Mini rhizotron observation tubes were installed to monitor root system production and turnover. Stable isotope analysis was used to examine possible functional differences in water and nutrient acquisition of root systems between the various families. A genetic dissection of root ontogenic and architectural traits, including biomass partitioning, was conducted using molecular markers to better understand the functional implications of these traits on resource acquisition and whole-plant carbon allocation.

Currently, assessments of how environmental stresses such as tropospheric ozone affect forests employ point estimates of factors such as ozone dose and species sensitivity. However, there is substantial regional heterogeneity in such factors. Hence, we have developed an approach for incorporating probabilistic analysis in estimating ecological risk at a regional scale. As an example, we model the effects of tropospheric ozone on the growth of loblolly pine stands in the southeastern USA. Our approach links software capable of automated Monte Carlo simulation to a Geographic Information System in order to assess the influence of uncertainty in factors such as ozone dose, soil moisture availability, and climate on regional patterns of loblolly growth rate. We demonstrate that this methodology may improve assessments of ecological risk by quantitating regional patterns in the influence of various factors on the predicted response of forests to ozone as well as identifying regions in which uncertainty in model predictions is the greatest.

Regionally distributed pollutants (e.g., tropospheric ozone and CO[sub 2]) can influence the growth of terrestrial plants. The mosaic of genotypes in natural populations makes it difficult to predict the ecological consequences of pollutants throughout a species' distribution. We simulated the response of Pinus ponderosa Laws to ambient, sub-ambient and above-ambient troposopheric O[sub 3] for 3 years using TREGRO, a physiologically based three growth model. Parameters controlling growth and carbon allocation were obtained from the literature and were varied to simulate intravarietal and intervarietal genotypes (western var. Ponderosa and eastern var. Scopulorum) of Ponderosa Pine. Parameter differences between the varieties include physiology, carbon allocation and phenoloy. Ozone altered 3 year biomass gain (+6% to 61%) and fine root to leaf mass ratio ([minus]8% to [minus]14%) in spite of a small effect on photosynthesis ([<=] 10%). Overall, O[sub 3] caused growth differences between varieties to be reduced. The reduction in growth differences between genotypes due to ozone has consequences for regional identification of populations sensitive to the effects of tropospheric ozone.

There is increasing potential for shifts in ecosystem boundaries in the face of widespread climate changes, stemming from increases in carbon dioxide levels. Initial attempts at predicting these shifts have assumed that biomes will reestablish boundaries in accordance with the governs of precipitation, temperature, and evapotranspiration (Emanuel et al. 1985). Such work has been based on the associations between the present distribution of natural vegetation zones and climatic regions (Holdridge 1964; Trewartha 1968). These models update the world map of average annual precipitation and temperature, using predictions from global climate models, and then remap vegetation zones, assuming that the correlation between vegetation zones and climate will remain intact. While this seems like a tenuous assumption at best, evidence from paleoecological studies in the eastern United States suggests that ecosystem distribution may maintain a relationship with climatic variables when quantified at time scales of thousands of years (Delcourt and Delcourt 1987).

To determine man’s role in changing natural landscapes, one must first develop methods for measuring landscape attributes, patterns, and rates of change. Classically, a large body of methodologies has been developed for classifying and ordering patterns in landscapes using fairly sophisticated mathematical procedures (a sampling of such procedures is given in Whittaker 1978 a, b). However, most of these methodologies have little analytical power in terms of noting changes in the fundamental dynamics of landscape systems.

A human ecosystem model, NUÑOA, simulates the yearly energy balance of individuals, families, and extended families in a hypothetical farming and herding community of Quechua Indians in the high Andes. The yearly energy demand of each family, based on the caloric requirements of its members, is computed by simulation of agricultural and herding activities in response to stochastic environmental conditions. The family energy balance is used in determining births, deaths, marriages, and resource sharing. The model user has the opportunity to investigate the effect of changes in marriage patterns, resource sharing patterns, or subsistence activities on the ability of the human population to survive in the harsh Andean environment. Results from the model suggest that the substructuring of a population into extended families provides a mechanism for sheltering the population from control by exogenous influences. A population without substructures for resource sharing is shown to be unstable in such an unpredictable environment.