Degradation data analysis is a powerful tool for reliability assessment. Useful reliability information is available from degradation data when there are few or even no failures. For some applications the degradation measurement process destroys or changes the physical/mechanical characteristics of test units. In such applications, only one meaningful measurement can be can be taken on each test unit. This is known as "destructive degradation. " Degradation tests are often accelerated by testing at higher than usual levels of accelerating variables like temperature. This chapter describes an important class of models for accelerated destructive degradation data. We use likelihood-based methods for inference on both the degradation and the induced failure-time distributions. The methods are illustrated with the results of an accelerated destructive degradation test for an adhesive bond. Disciplines Statistics and Probability Comments This preprint was published in Lindqvist, B. H. and K. A. Doksum, ed. Mathematical and Statistical Methods in Reliability.

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The June 199 1 eruption of Mt. Pinatubo had a marked cooling on global climate that was evident in the regional air temperatures of the northeastern U.S. This climatic perturbation coincided with the 199 l-l 994 pilot surveys initiated by the Environmental Monitoring and Assessment Program-Surface Waters (EMAP-SW). Regional air and summer profile water temperatures for 1992 were significantly lower (-l-2°C) than in the same July-August sampling period in 199 1, 1993, or 1994. Richness of major zooplankton groups such as rotifers and small cladocerans also varied markedly with the 1992 cooling. The median of 323 randomly selected survey lakes had 2-10 additional species of zooplankton per lake in 1992 than in the warmer years of 199 1, 1993, or 1994. This response represented an increase of 7-34% in local richness across lakes. Regional richness was also 17-32% higher in the cooler year. ANOVA showed that total zooplankton richness had a highly significant year effect, suggesting a response to annual climatic differences. The difference in total richness in 1992 was attributed primarily to richness of rotifers and small cladocerans. On the other hand, the richness of large cladocerans and copepods did not show significant year effects, suggesting that their responses may occur at time scales that are longer than the temporal scale of this 4-yr survey. These results indicate that local zooplankton richness can be effectively extrapolated to regional scales for the monitoring of lakes to climate change.

The U.S. Environmental Protection Agency has proposed a sample survey design to answer questions about the ecological condition and trends in condition of U.S. ecological resources. To meet the objectives, the design relies on a probability sample of the resource population of interest (e.g., a random sample of lakes) each year on which measurements are made during an index period. Natural spatial and temporal variability and variability in the sampling process all affect the ability to describe the status of a population and the sensitivity for trend detection. We describe the important components of variance and estimate their magnitude for indicators of trophic condition of lakes to illustrate the process. We also describe models for trend detection and use them to demonstrate the sensitivity of the proposed design to detect trends. If the variance structure that develops during the probability surveys is like that synthesized from available databases and the literature, then the trends in common indicators of trophic condition of the specified magnitude should be detectable within about a decade for Secchi disk transparency (0.5–1 percentiyear) and total phosphorus (2–3 percent/year), but not for chlorophyll‐a (> 3–4 percent/year), which will take longer.