Variation, replication, and power analysis ofMyriophyllumspp. microcosm toxicity data

Abstract

Myriophyllum spp. have been proposed as a new standard laboratory aquatic macrophyte test species for the registration of pesticides. The main objectives of this investigation were to determine the power of Myriophyllum sibiricum and Myriophyllum spicatum toxicity data derived from an outdoor microcosm bioassay, to evaluate the variation of 10 different aquatic plant endpoints and to calculate the minimum detectable difference for these endpoints, to determine the replication required to detect ecologically significant changes from control for these endpoints, and to make recommendations for future field studies with Myriophyllum spp. Control data from four different studies that characterized haloacetic acid toxicity with Myriophyllum spp. for durations of three to six weeks during the summer of 1999 with five treatment levels (n = 3), including control, were examined. Endpoint coefficient of variation ranged, on average, from 6 to 28%. Node number and plant length endpoints were consistently the most statistically powerful for both plant species. It was possible to detect approximately 30% change from control in both endpoints with high statistical power (beta = 0.2, alpha = 0.05, n = 3). The range of minimum detectable differences was 40 to 60% for the other endpoints. Replication to detect a > or = 25% change from control would require an n of 2 to 21, depending on the endpoint. Myriophyllum sibiricum had slightly lower coefficients of variation and thus required fewer replicates than M. spicatum to be statistically significantly different from control values. Variation within microcosm studies was not significantly different from that of controlled laboratory studies, implying that most of the variation observed in the field is inherent in the plants. Based on statistical sensitivity, ecological relevance, and toxicological sensitivity, we recommend using plant length and root endpoints as indicators of toxicity under field conditions.

The minimum detectable difference (MDD) and the interpretation of treatment-related effects of pesticides in experimental ecosystems

“…Furthermore, up to now, relatively few scientific publications reported and discussed MDDs for toxicity endpoints derived from microcosm/mesocosm tests (e.g. Hanson et al 2003 ; Sanderson et al 2009 ).…”

Herbicide Exposure and Toxicity to Aquatic Primary Producers

“…The OECD proposed growth inhibition and yield of total shoot length, fresh weight and dry weight as endpoints for a sediment-free test and additionally qualitative observations of symptoms such as chlorosis, necrosis and growth deformities for a water-sediment test with rooting macrophytes (OECD 2014a, b). For this group of primary producers, somatic endpoints like total plant length, main shoot length, fresh weight and root length are more sensitive than pigment endpoints, similar as for floating macrophytes (Hanson et al 2003;Brain et al 2004;Knauer et al 2006). For soil and sediment exposure of aquatic plants to herbicides, development of endpoints related to root morphology and root metabolism could provide insights into early impact of herbicides on exposed plant parts.…”

“…For soil and sediment exposure of aquatic plants to herbicides, development of endpoints related to root morphology and root metabolism could provide insights into early impact of herbicides on exposed plant parts. For auxin-type acting herbicides, root endpoints are the most sensitive somatic endpoints for rooting macrophytes (Hanson et al 2003;Arts et al 2008). However, development of belowground endpoints is still challenging since root development is also strongly impacted by available nutrients and redox conditions in the sediment (Barko et al 1991;Boros et al 2011).…”

Better define beta–optimizing MDD (minimum detectable difference) when interpreting treatment-related effects of pesticides in semi-field and field studies

“…Since the statistical power (1-β) depends on the parameters alpha (α), sample size (n), and standard deviation (σ), changing the value of one of those parameters will result in changing the statistical power (Hanson et al 2003;Quinn and Keough 2002). Figures 3, 4, 5 illustrate the relationships between statistical power and MDD according to those parameters; these examples are based on the dataset of Chironomus sp.…”