Evaluating freshwater mussel sampling methodologies using a simulation model
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.