SAMPL: An agent-based model to evaluate spatial sampling strategies

Without accurate and reliable information about populations, it is challenging to make informed species management decisions. To fill this critical need, a multitude of sampling techniques have been developed. However, it is often difficult to evaluate the efficacy of sampling techniques because the true parameters of natural populations are rarely known. In simulation models, the population distribution and density of target species can be defined and manipulated, making them a useful tool for evaluating sampling techniques. In this paper, we introduce Survey Analysis for Monitoring Population Levels (SAMPL), a spatially explicit, agent-based model designed to simulate and compare ecological sampling techniques. While originally designed for simulating freshwater mussel surveys in riverine environments, SAMPL is highly adaptable and can be used to assess sampling for various habitats and organisms. SAMPL features four built-in sampling strategies commonly used in field-based ecological sampling; three are quantitative (simple random sampling, transect sampling, and adaptive cluster sampling) and one is qualitative (qualitative timed search sampling). Users can compare the utility of each sampling method for estimating the density of populations and for optimizing detection rate. We then demonstrate the use of SAMPL in a case study examining the effects of declining detectability on catch-per-unit-effort (CPUE) estimates for qualitative freshwater mussel surveys. Our case study showed that reductions in detectability resulted in disproportionate reductions in CPUE, emphasizing the potential challenges associated with using qualitative surveys to assess freshwater mussel abundance.