Using variance components to estimate power in a hierarchically nested sampling design

Abstract

We used variance components to assess allocation of sampling effort in a hierarchically nested sampling design for ongoing monitoring of early life history stages of the federally endangered Devils Hole pupfish (DHP) (Cyprinodon diabolis). Sampling design for larval DHP included surveys (5 days each spring 2007-2009), events, and plots. Each survey was comprised of three counting events, where DHP larvae on nine plots were counted plot by plot. Statistical analysis of larval abundance included three components: (1) evaluation of power from various sample size combinations, (2) comparison of power in fixed and random plot designs, and (3) assessment of yearly differences in the power of the survey. Results indicated that increasing the sample size at the lowest level of sampling represented the most realistic option to increase the survey's power, fixed plot designs had greater power than random plot designs, and the power of the larval survey varied by year. This study provides an example of how monitoring efforts may benefit from coupling variance components estimation with power analysis to assess sampling design. Abstract We used variance components to assess allocation of sampling effort in a hierarchically nested sampling design for ongoing monitoring of early life history stages of the federally endangered Devils Hole pupfish (DHP) (Cyprinodon diabolis). Sampling design for larval DHP included surveys (5 days each spring 2007-2009), events, and plots. Each survey was comprised of three counting events, where DHP larvae on nine plots were counted plot by plot. Statistical analysis of larval abundance included three components:(1) evaluation of power from various sample size combinations, (2) comparison of power in fixed and random plot designs, and (3) assessment of yearly differences in the power of the survey. Results indicated that increasing the sample size at the lowest level of sampling represented the most realistic option to increase the survey's power, fixed plot designs had greater power than random plot designs, and the power of the larval survey varied by year. This study provides an example of how monitoring efforts may benefit from coupling variance components estimation with power analysis to assess sampling design.

Sampling to elucidate the dynamics of infections in reservoir hosts

“…Furthermore, random samples may be clustered in time and space. Additionally, random selection of sampling locations may have less statistical power than intentional selection of sampling locations [71]. Moreover, truly random sampling may not make sense for certain taxa, such as central-place foragers (e.g.…”

Incorporating antenna detections into abundance estimates of fish

“…Extending the benefit of increased detection (from antennas) could improve statistical power of monitoring designs and better enable biologists to detect increasing or decreasing population trends (Peterman 1990;Taylor and Gerrodette 1993;Wagner et al 2013). Power analysis could be used to evaluate the statistical power of competing sampling designs (e.g., Carlson and Schmiegelow 2002;Dzul et al 2013) to determine whether or not antennas are beneficial. Specifically, rough estimates of physical capture probabilities, antenna detection probabilities, and the proportions of fish that are marked can be incorporated into simulations to estimate statistical power and assess sampling tradeoffs.…”