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Robert J. Steidl

Publications and source records attributed to Robert J. Steidl.

2 recordsLinked to original sources

Power to detect trends in abundance of secretive marsh birds: effects of species traits and sampling effort

Standardized protocols for surveying secretive marsh birds have been implemented across North America, but the efficacy of surveys to detect population trends has not been evaluated. We used survey data collected from populations of marsh birds across North America and simulations to explore how characteristics of bird populations (proportion of survey stations occupied, abundance at occupied stations, and detection probability) and aspects of sampling effort (numbers of survey routes, stations/route, and surveys/station/year) affect statistical power to detect trends in abundance of marsh bird populations. In general, the proportion of survey stations along a route occupied by a species had a greater relative effect on power to detect trends than did the number of birds detected per survey at occupied stations. Uncertainty introduced by imperfect detection during surveys reduced power to detect trends considerably, but across the range of detection probabilities for most species of marsh birds, variation in detection probability had only a minor influence on power. For species that occupy a relatively high proportion of survey stations (0.20), have relatively high abundances at occupied stations (2.0 birds/station), and have high detection probability (0.50), ≥40 routes with 10 survey stations per route surveyed 3 times per year would provide an 80% chance of detecting a 3% annual decrease in abundance after 20 years of surveys. Under the same assumptions but for species that are less common, ≥100 routes would be needed to achieve the same power. Our results can help inform the design of programs to monitor trends in abundance of marsh bird populations, especially with regards to the amount of sampling effort necessary to meet programmatic goals.

Journal of Wildlife Management

Statistical power analysis in wildlife research

Statistical power analysis can be used to increase the efficiency of research efforts and to clarify research results. Power analysis is most valuable in the design or planning phases of research efforts. Such prospective (a priori) power analyses can be used to guide research design and to estimate the number of samples necessary to achieve a high probability of detecting biologically significant effects. Retrospective (a posteriori) power analysis has been advocated as a method to increase information about hypothesis tests that were not rejected. However, estimating power for tests of null hypotheses that were not rejected with the effect size observed in the study is incorrect; these power estimates will always be ≤ 0.50 when bias adjusted and have no relation to true power. Therefore, retrospective power estimates based on the observed effect size for hypothesis tests that were not rejected are misleading; retrospective power estimates are only meaningful when based on effect sizes other than the observed effect size, such as those effect sizes hypothesized to be biologically significant. Retrospective power analysis can be used effectively to estimate the number of samples or effect size that would have been necessary for a completed study to have rejected a specific null hypothesis. Simply presenting confidence intervals can provide additional information about null hypotheses that were not rejected, including information about the size of the true effect and whether or not there is adequate evidence to 'accept' a null hypothesis as true. We suggest that (1) statistical power analyses be routinely incorporated into research planning efforts to increase their efficiency, (2) confidence intervals be used in lieu of retrospective power analyses for null hypotheses that were not rejected to assess the likely size of the true effect, (3) minimum biologically significant effect sizes be used for all power analyses, and (4) if retrospective power estimates are to be reported, then the α -level, effect sizes, and sample sizes used in calculations must also be reported.

Journal of Wildlife Management