# Presentation: 495395

## Karen Blocksom and Joseph Flotemersch
EPA/ORD/NERL
NWQM Conference, San Jose, CA
May 11, 2006  

- EPA/ORD/NERL
- NWQM Conference, San Jose, CA
- May 11, 2006

***Effect of Taxonomic Resolution on Performance Characteristics of a Method for Sampling Large Rivers***

- *Although this work was reviewed by EPA and approved for publication, it may not necessarily reflect official Agency policy.

## Background

- A new bioassessment method developed for collecting macroinvertebrates in non-wadeable rivers (*In press*: River Research and Applications, vol. 22, July 2006)
- Study conducted to estimate performance characteristics using metrics: precision and sensitivity
- Evaluated at the lowest possible taxon (*species*) level
- Many states stop at *genus* or *family*, so relevant to evaluate performance characteristics at these levels as well

## Sample Collection and Processing

- 19 sites across 5 rivers
- Deep and shallow sites across disturbance gradient
- 3 replicate samples per site in field
- Also habitat, basic water chemistry collected
- Up to three 300-organism sorts per sample in the laboratory

## Expectations (Family/Genus relative to Species level)

- Richness metrics will have lower values in general and hence, lower variability (higher precision) at the *genus* and *family* levels (Total and EPT taxa richness)
- Tolerance and functional feeding group metrics will have lower variability because designations will apply to broader groups and should be more consistent at this level (% Tolerant individuals and % Collector-filterers)
- Laboratory variability will be lower because less chance for error than at *species* level
- Sensitivity will be poorer because of broader groups at the *family* level – fewer correlations with abiotic variables

## Precision - Methods

- Perform ANOVA with:
- Site ID as factor
- Multiple samples (A,B,C) as field replicates
- Multiple 300-organism sorts as lab replicates
- Use RMSE from this analysis to calculate:
- Detectable difference (DD) – minimum difference detectable based on sample size of 3
- Relative DD (as percentage of metric range)

## Precision - Field

## Precision - Field

## Precision - Laboratory

## Precision - Laboratory

## Results - Precision

- Taxa and EPT richness
- DD (variability) increased from *family* to *species* as expected
- BUT relative DD similar or slight decrease from *family* to *species*
- % Tolerant individuals
- For lab, DD (variability) increased *family* to *species*
- No strong pattern for field DD
- % Collector-filterers
*Genus* and *species* identical - same designations
- All measures similar among taxonomic levels

## Sensitivity - Methods

- Spearman rank correlations
*Family*, *genus*, and *species* levels
- Between mean metric values and potential gradients:
- Taxa richness, EPT richness, % Tolerant individuals, % Collector-filterers
- Conductivity, pH, DO, temperature, EMAP habitat and human influence variables, NWHI metrics

**Notes:**
Mention that metrics are selected to represent the range of metric types that would be susceptible to taxonomic effects (i.e., richness, functional feeding groups, tolerance).

## Sensitivity

## Sensitivity 

## Sensitivity

## Sensitivity

## Results – Sensitivity

- As expected, generally more significant correlations at species level
- Strength of common correlations similar among taxonomic levels
- Variability around trend lines tends to be greater for *genus* and *species* level data

## In the context of relative DD, taxonomic level had little to no effect

Sensitivity only slightly stronger at genus/species level than at the family level

Overall, taxonomic resolution did not strongly affect performance characteristics of the method

- Sensitivity only slightly stronger at *genus/species* level than at the family level
- Overall, taxonomic resolution did not strongly affect performance characteristics of the method

***Conclusions***