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The purpose of this study was to evaluate the reliability and validity of the raw accelerometry output from research-grade and consumer wearable devices compared to accelerations produced by a mechanical shaker table. Raw accelerometry data from a total of 40 devices (i.e., n = 10 ActiGraph wGT3X-BT, n = 10 Apple Watch Series 7, n = 10 Garmin Vivoactive 4S, and n = 10 Fitbit Sense) were compared to reference accelerations produced by an orbital shaker table at speeds ranging from 0.6 Hz (4.4 milligravity-mg) to 3.2 Hz (124.7mg). Two-way random effects absolute intraclass correlation coefficients (ICC) tested inter-device reliability. Pearson product moment, Lin’s concordance correlation coefficient (CCC), absolute error, mean bias, and equivalence testing were calculated to assess the validity between the raw estimates from the devices and the reference metric. Estimates from Apple, ActiGraph, Garmin, and Fitbit were reliable, with ICCs = 0.99, 0.97, 0.88, and 0.88, respectively. Estimates from ActiGraph, Apple, and Fitbit devices exhibited excellent concordance with the reference CCCs = 0.88, 0.83, and 0.85, respectively, while estimates from Garmin exhibited moderate concordance CCC = 0.59 based on the mean aggregation method. ActiGraph, Apple, and Fitbit produced similar absolute errors = 16.9mg, 21.6mg, and 22.0mg, respectively, while Garmin produced higher absolute error = 32.5mg compared to the reference. ActiGraph produced the lowest mean bias 0.0mg (95%CI = -40.0, 41.0). Equivalence testing revealed raw accelerometry data from all devices were not statistically significantly within the equivalence bounds of the shaker speed. Findings from this study provide evidence that raw accelerometry data from Apple, Garmin, and Fitbit devices can be used to reliably estimate movement; however, no estimates were statistically significantly equivalent to the reference. Future studies could explore device-agnostic and harmonization methods for estimating physical activity using the raw accelerometry signals from the consumer wearables studied herein. 

Restoration of degraded estuarine oyster reefs typically involves deploying recycled oyster shell. In low‐salinity, low‐predation areas of estuaries, high‐volume shell deployments are known to improve flow conditions and thus oyster survival and growth. It is also hypothesized that the physical structure of restored reefs could suppress foraging by oyster predators in high‐salinity, high‐predation zones. That hypothesis is untested. Given limited resources, it is important to determine how much shell is needed for successful restoration and whether there are diminishing returns in shell addition. In Apalachicola Bay, Florida, we manipulated shell volume on an oyster reef to create three 0.4 ha areas of low (no shell addition), moderate (153 m³shell), and high (306 m³shell) habitat structure. We repeated experiments and surveys over 2 years to determine if restoration success increased with habitat structure. Predation on oysters was greater on the non‐shelled area than on the reshelled reefs, but similar between the two reshelled reefs. Oyster larval supply did not differ among the reef areas, but by the end of the experiment, oyster density (per unit area) increased quadratically with habitat structure, plateauing at high levels of structure. Model selection indicated that the most parsimonious explanation for these patterns was that increased habitat structure reduced predation and increased overall recruitment, but that the higher reshelling treatment did not have better outcomes than moderate reshelling. Thus, restoration could be optimized by deploying a moderate amount of shell per unit area.