Abstract
A power analysis allows estimation of the probability of detecting upward or downward trends in abundance using linear regression, given number of samples and estimates of sample variability and rate of change. Alternatively, the minimum number or precision of samples required to detect trends with a given degree of confidence can be computed. The results are applicable to an experimental situation in which samples are taken at regular intervals in time or space. The effects of linear and exponential change and of having sample variability be a function of abundance are investigated. Results are summarized graphically and, as an example, applied to the monitoring of the California sea otter population with aerial surveys.
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