Evaluating Bioassessment Designs and Decision Thresholds Using Simulation Techniques

Abstract

Natural resource managers face numerous choices when developing bioassessment programs but seldom have the opportunity to compare the performance of alternative designs. As a result, managers often lack a basis for establishing decision thresholds based on their objectives for evaluating resource condition, accounting for uncertainty, and controlling costs. In this chapter, we illustrate how simulation techniques may be used to optimize bioassessment decision thresholds and sampling designs with a case study of benthic macroinvertebrate communities in Shenandoah National Park, USA. We evaluated the effects of sampling effort (6 levels) and taxonomic resolution (family vs. genus) on the sensitivity of a commonly used index of stream condition (Macroinvertebrate Biotic Integrity Index, MBII) to classify resource condition as affected by ecological change. We computed expected utility values to compare decision thresholds, which integrated statistical power and differential risk tolerance for misclassification (i.e., type I and II error rates). Our analysis revealed important differences among bioassessment designs. MBII sensitivity increased with sampling effort, but improvements were modest across the highest sampling levels. Genus-level assessments were generally most sensitive to ecological change, even though precision increased at the family level due to decreased variation in reference communities. However, the sensitivity-cost relationship revealed no single, optimal combination of taxonomic resolution and sampling effort. Rather, we found that for a given cost, equivalent sensitivities could be obtained from larger samples at the family-level or smaller samples at the genus level. An analysis of expected utility demonstrated that the optimal decision threshold depends on prior probability of resource condition, i.e., reference, early warning, or impaired. We conclude that simulation methods provide a flexible approach to evaluate and optimize bioassessment designs and decision thresholds based on objective-specific utility values.

The role of hydro‐environmental factors in Mayfly (Ephemeroptera, Insecta) community structure: Identifying threshold responses

“…Knowledge of mayfly community composition, seasonal dynamics, distribution, and their narrow habitat sensitivity extends their utility value beyond just indicators and surrogates of habitat change (Bauernfeind & Moog, 2000;Vilenica, Ivković, Sartori, & Mihaljević, 2017) to being agents for adaptive and holistic conservation planning of freshwater resources (Ramulifho, Rivers-Moore, Dallas, & Foord, 2018). The choice of mayflies in river monitoring programs lies in the low cost of sampling associated with their collection and their high sensitivity level to water quality parameters (Snyder, Hitt, Smith, & Daily, 2014).…”

Predicting reference assemblages for freshwater bioassessment with limiting environmental difference analysis

“…Hence, they did not test ability to detect colonization of streams by invertebrate taxa as a result of taxon introductions or anthropogenic environmental changes that favor taxa not naturally present. This limitation also applies to many previous impact simulations used to evaluate bioassessment methods (Cao and Hawkins 2005, Mazor et al 2006, Cao and Epifanio 2010, Hawkins et al 2010a, Jackson et al 2010, Downie 2011, Bailey et al 2012, Snyder et al 2014), which only eliminated or changed the abundances of taxa that were initially present. However, a capacity to detect taxon additions could be important in operational bioassessment.…”

Computer-Assisted Bioidentification Using Freshwater Macroinvertebrates: A Scoping Review

“…In this one, data processing and multivariate predictive analysis of organisms and populations already classified by human experts were carried out through specialized software [22][23][24][25][26][27][28], statistical [29][30][31], and automatic algorithms [32][33][34][35][36][37]. In these studies, biological indicators such as EPTC (Ephemeroptera commonly known as Mayfly; Plecoptera, commonly called stoneflies; and Trichoptera, commonly known as caddisflies taxa) [38,39], MBII (Macroinvertebrate Biotic Integrity Index) [40], or BBI (Belgium Biotic Index) [41] were used. The data used are related to the number of species, variability in sample size, biotic factors such as life cycle and morphological development, or abiotic factors such as hydromorphological conditions of water bodies and pollution [42].…”