13 Feb 2026

Evaluating Freshwater Mussel Sampling Methodologies Using a Simulation Model

Abstract: Field surveys form the basis of many research efforts and are the foundation for estimates of population size and density that inform conservation and management practices for imperiled species. As a result, evaluating the performance of different survey methods across a range of conditions that may be encountered in the field can increase understanding of the time and effort that may be required to ensure that survey results are sufficiently accurate and reliable for conservation goals. We used a spatially explicit agent-based model to simulate four commonly used freshwater mussel field survey methodologies: simple random sampling (SRS), transect random sampling (TRS), adaptive cluster sampling (ACS), and qualitative timed searches (QTS) to investigate the influence of sampling method, spatial distribution, and mussel density on the performance (i.e., accuracy, precision, and detection rate) of survey techniques. Our analysis suggests that mussel density, spatial distribution, and sampling effort influence sampling accuracy, precision, and species detection for all sampling methods. QTS produces highly variable catch-per-unit-effort (CPUE) metrics when mussels are dense and/or clustered, indicating the technique may be unreliable as a proxy for density. Quantitative methods like SRS and TRS may be well-suited for estimating population characteristics, but a high level of effort may be needed to obtain reasonable accuracy when mussels occur at low densities. ACS may be more efficient for mussels at low densities, but it can be challenging to plan for the level of effort required to complete an ACS protocol. Designing an ecological survey requires careful consideration of research objectives and available resources. Future research may consider the performance of qualitative and quantitative surveys in combination as a means of overcoming some of the practical challenges of applying individual survey methods.

11 Feb 2026

Oyster Reef Ecosystem Recovery Monitoring: A Habitat Case Study for the US Army Corps of Engineers Aquatic Restoration Monitoring for Ecosystem Recovery (ARMER) Network

Abstract: Oyster reefs are native to oceanic coasts of the contiguous United States, are great contributors to secondary production in estuaries, and provide food and other services to humans. Unfortunately, oyster reefs have become functionally extinct throughout much of their historical range due to overharvesting, disease, poor water quality, and weather-related drivers. Restoration efforts are underway in response to these population collapses and seek to replenish oyster populations to a level sustainable for ecosystem services. To evaluate effectiveness of these restoration interventions and characterize oyster reef recovery status on large scales, coordinated monitoring is needed to facilitate long-term collection, storage, and dissemination of data. The US Army Corps of Engineers has proposed the development of the Aquatic Restoration Monitoring for Ecosystem Recovery (ARMER) Network, a monitoring system composed of nationwide restoration and reference sites, to generate high-quality, replicated datasets to address large-scale ecosystem restoration challenges. This report details a framework of recovery attributes and associated monitoring metrics and methods proposed to characterize oyster reef habitat recovery following ecosystem restoration interventions. Monitoring recommendations, as well as existing monitoring networks and communities of practice, are discussed as key potential facets and partners in the operationalization of ARMER.

16 Dec 2025

Fiber-Optic Distributed Acoustic Sensing for Nondestructive Monitoring of Permafrost

Fiber-optic distributed acoustic sensing (DAS) has gained traction in recent years as a geophysical monitoring tool. Advancements in commercially available DAS have allowed for sub-10 m data resolution and high sampling rates (over 10 kHz), leading to the use of DAS for infrastructure change detection and localization monitoring. Using this technology, a team from the US Army Engineer Research and Development Center–Cold Regions Research and Engineering Laboratory (ERDC-CRREL) built a field campaign around monitoring changes in permafrost using DAS via a dispersion analysis of surface wave propagation. In May 2024, active seismic testing was performed on a rapidly deployed, surface-laid, nondestructive DAS array above CRREL’s permafrost tunnel. Active source testing was repeated in September 2024 to collect data that may indicate changes in the seismic response due to permafrost changes. DAS response data was also collected from an unmanned aerial system (UAS) to evaluate for potential use in standoff assessment of permafrost changes. The field campaign results indicate that nondestructive DAS arrays are likely useful in detecting and localizing changes in near-surface properties of the permafrost.

30 Jul 2021

Geotechnical Effects on Fiber Optic Distributed Acoustic Sensing Performance

Abstract: Distributed Acoustic Sensing (DAS) is a fiber optic sensing system that is used for vibration monitoring. At a minimum, DAS is composed of a fiber optic cable and an optic analyzer called an interrogator. The oil and gas industry has used DAS for over a decade to monitor infrastructure such as pipelines for leaks, and in recent years changes in DAS performance over time have been observed for DAS arrays that are buried in the ground. This dissertation investigates the effect that soil type, soil temperature, soil moisture, time in-situ, and vehicle loading have on DAS performance for fiber optic cables buried in soil. This was accomplished through a field testing program. Signal to Noise Ratio (SNR) of the DAS response was used for all the tests to evaluate the system performance. The results of the impact testing program indicated that the portions of the array in gravel performed more consistently over time. The results also indicated that time DAS performance does change somewhat over time. Performance variance increased in new portions of array in all material types through time. Overall, this dissertation provides guidance that can help inform the civil engineering community with respect to installation design recommendations related to DAS used for infrastructure monitoring.