Our results suggest increasing the frequency of disturbances (a lower DRI) would reduce the percentage of high-severity fire on landscape but not the total amount of wildfire in general. In order to test the management component of the DRI, we developed management scenarios with forest managers and stakeholders in the region these scenarios were integrated into a mechanistic forest landscape model that also accounted for climate change, as well as natural disturbances of wildfire and insect outbreaks. We specifically investigated the consequences of DRI on the proportion of high-severity fire and the net sequestration of carbon. We applied the DRI to examine forest change in the Lake Tahoe Basin of California and Nevada. To account for the range of disturbance intensities and disturbance types (wildfire, bark beetles, and management), we developed a disturbance return interval (DRI) that represents the average return period for any disturbance, human or natural. However, management activities are only one part of a suite of disturbance vectors that shape forest conditions.
As fires become more severe, forest managers are searching for strategies that can restore forest health and reduce fire risk. This analysis provides an exposure–response function for acute exposures to tetrachloroethylene using categorical regression analysis.Because of past land use changes and changing climate, forests are moving outside of their historical range of variation. The stratified regression model allows human effect levels to be identified more confidently by basing the intercept on human data and the slope parameters on the combined data (on a C × T plot). More complex models with strata denned by sex and species did not improve the fit. A model with species‐specific concentration intercept terms for rat and human central nervous system data improved fit to the data compared with the base model (combined species). The mouse data were highly uncertain due to lack of data on effects of low concentrations and were excluded from the analysis. Studies containing information on acute effects of tetrachloroethylene in rats, mice, and humans were analyzed. The ability to treat partial information addresses the difficulties in assigning consistent severity scores. Stratification of the regression model addresses systematic differences among studies by allowing one or more model parameters to vary across strata denned, for example, by species and sex. A generalized linear model for ordinal data was used to estimate the probability of response associated with exposure and severity category. Stratified categorical regression is a form of meta‐analysis that addresses these needs by combining studies and analyzing response data expressed as ordinal severity categories.
Zhou, HaiboĮxposure‐response analysis of acute noncancer risks should consider both concentration (C) and duration (T) of exposure, as well as severity of response. Categorical Regression Analysis of Acute Exposure to Tetrachloroethylene Categorical Regression Analysis of Acute Exposure to Tetrachloroethylene
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