Quantifying the statistical power of monitoring programs for marine protected areas

Abstract

Marine Protected Areas (MPAs) are increasingly established globally as a spatial management tool to aid in conservation and fisheries management objectives. Assessing whether MPAs are having the desired effects on populations requires effective monitoring programs. A cornerstone of an effective monitoring program is an assessment of the statistical power of sampling designs to detect changes when they occur. We present a novel approach to power assessment that combines spatial point process models, integral projection models (IPMs) and sampling simulations to assess the power of different sample designs across a network of MPAs. We focus on the use of remotely operated vehicle (ROV) video cameras as the sampling method, though the results could be extended to other sampling methods. We use empirical data from baseline surveys of an example indicator fish species across three MPAs in California, USA as a case study. Spatial models simulated time series of spatial distributions across sites that accounted for the effects of environmental covariates, while IPMs simulated expected trends over time in abundances and sizes of fish. We tested the power of different levels of sampling effort (i.e., the number of 500-m ROV transects) and temporal replication (every 1-3 yr) to detect expected post-MPA changes in fish abundance and biomass. We found that changes in biomass are detectable earlier than changes in abundance. We also found that detectability of MPA effects was higher in sites with higher initial densities. Increasing the sampling effort had a greater effect than increasing sampling frequency on the time taken to achieve high power. High power was best achieved by combining data from multiple sites. Our approach provides a powerful tool to explore the interaction between sampling effort, spatial distributions, population dynamics, and metrics for detecting change in previously fished populations.

Analysis of fish population size distributions confirms cessation of fishing in marine protected areas

“…Importantly, our model does not require extensive, professionally-collected data on fish or fishers, as the inputs are simply a time series of fish numbers and sizes. With the increasing availability of inexpensive, video cameras configured in stereo, fish sizes can be readily and accurately surveyed by stakeholders or citizen scientists (Lowry, Folpp, Gregson, & Suthers, 2012), or potentially using less labor-intensive remote methods, such as remote operated vehicles (Perkins et al, 2020) or baited remote underwater video (Langlois et al, 2020). Elsewhere we have investigated the data requirements for our method, and discovered that unbiased estimates of harvest rates require observations of approximately 100 individuals per year for seven years (with fewer years possible if the size distribution is better characterized; Yamane, L., et al, unpublished manuscript).…”

Sampling re-design increases power to detect change in the Great Barrier Reef’s inshore water quality

“…For example, if a more complex model explained more of the total data variability then this may increase the power to detect trends. As our study was comparative, we did not explore more complex models in detail, however, models including spatial components would be a likely next step [ 8 ].…”

“…The study further required knowledge of the purpose and intervention implementation, consistency in measurement practices, and the resources to complete an assessment of the impact. Modern examples of power analysis in ecological monitoring typically assess the capacity of a current monitoring program to answer key questions aligned with its objectives [60][61][62][63] and/or simulate prospective optimal sampling re-designs that integrate new methodologies (analytical or experimental) and constraints on resources [8,64,65]. O'Hare et al [6] simulated the effect of network re-design on the statistical power to detect long-term trends across a national monitoring network of three key ecological indicators.…”

“…Further, O’Hare et al [ 6 ] simulated the effect of network re-design on the statistical power to detect long-term trends across a national monitoring network of three key ecological indicators. Data collected within a rigorously designed monitoring program provide sufficient statistical power to assess change in indicators aligned with monitoring objectives, providing a mechanism for management intervention or adjustments [ 1 , 7 , 8 ]. Low power indicates a high probability of concluding that no environmental impact has occurred when in reality it has, resulting in substantial short- and long-term costs due to necessary action not being taken [ 1 ].…”