1. Sediment Guidance Workgroup Daniels Fund Building Nov 12, 2013
Pebble Counts and TIVs
Sediment Guidance Workgroup
Daniels Fund Building
Nov 12, 2013

2. Today’s Topics Division’s protocol for pebble counts
Division’s current pebble count dataset
TIVs
Drunella doddsii

3. WQCD’s Pebble Count Methodology

4. Methods for Pebble Counts
Std sampling of surface and subsurface particle-size distributions in wadeable streams
Based on methods of USFS Rocky Mountain Research Station – report by K. Bunte/S. Abt
Effort results in 400 pebble counts

5. Equipment Materials needed: Surveyors tape & stakes Sampling frame
Gravelometer
Waterproof field sheet
Pencil

6. Field Method Define reach length Locate bankfull
The place on each bank where the stream rises during a large water event, a 1-2 year flood event
Measure bankfull-to-bankfull width
Avg. 3 bankfull widths

7. Field Method Define reach length Calculate sampling reach length
20 x avg. bankfull width = reach length
Example: 20 x 13’ = 260’

8. Field Method Set transects Set survey tape across bottom of reach (0’)
Equidistantly space transects 1/10th the total sampling reach length
Example: 260’ / 10 = 26’ (between transects)
Move upstream to 26’ transect, 52’, etc.
26’ 0’
104’
26’
52’
Flow
78’

9. Field Method Measuring along the transect
Measure bankfull to bankfull length (Ex. 20’)
Divide length by 10 (Ex. 20’ / 10 = 2’)
This is the distance in which to move the sampling frame along the transect tape
Begin at left or right bankfull position
Flow 2’
20’ 0’

10. Sampling Frame
The sampling frame consists of 4 aluminum bars that are connected to form a square
Elastic white bands are stretched horizontally across the frame
The spacing of the grid points is adjusted to a size equal to or larger than the dominant large particle size (=D95)
95th percentile grain diameter
60 cm

11. x 10 = 40 Field Method Using the sampling frame
Place corner of frame on the transect tape
Particles are collected from under all four grid points 1
x 10
= 40 4 2
counts per transect 3

12. Measurements Gravelometer A sturdy, aluminum template
Holes correspond to Wentworth particle size classes
Determine a particle’s sieve diameter in terms of “smaller than” the hole of a given size
Example:
A rock with a 60 cm b-axis (or intermediate axis) would be tallied as smaller than 64 mm in the “smaller than” approach

13. Field Documentation Field sheet
Habitat, bank, and water line documented

14. Office - Data Entry
Data entry from field sheet to pebble count uploader
Uploader output = CSV file
Field Sheet
Electronic Template

15. Pebble Count Database
CSV files appended to Division’s pebble count db on annual basis
% fines can be queried and matched to macroinvertebrate MMIs or metrics

16. Pebble Count Dataset 504 pebble/bug pairings 390 usable pairings
289 WQCD
106 EPA REMAP
54 EPA NRSA
41 EPA WEMAP
14 USFS
390 usable pairings
Older pairs, winter mos., low counts removed
85 reference sites

17. Pebble Count Dataset Retained pairings presently used in:
TIV calculations - % Fines
Stressor Identification

18. Tolerance Indicator Values “TIVs”
Brachycentrus americanus

19. Literature Source
Estimation and application of indicator values for common macroinvertebrate genera and families of the United States
Daren M. Carlisle, Michael R. Meador, Stephen R. Moulton II, Peter M. Ruhl
National Water Quality Assessment Program, U.S. Geological Survey
12201 Sunrise Valley Drive, MS 413, Reston, VA, USA
Published in
Ecological Indicators 7 (2007) 22-33

20. Highlights
Tolerance of macroinvertebrate taxa to chemical and physical stressors is widely used in interpretation of bioassessment data
If taxa are sensitive to specific pollutants, TIVs will help in diagnosing potential causes of impairment
Estimated genus- and family-level indicator values from the NAWQA dataset
Weighted averages were calculated for 3 synthetic gradients and 2 uncorrelated physical variables

21. Method Background Substrate size estimation Study area
45 major drainage basin across continental U.S.
Including Colorado basins
Biological sampling
Richest-targeted habitat (RTH)
Chemical sampling
Substrate size estimation
For % fines

22. TIV Estimation
Estimated TIVs by calculating abundance-weighted averages (WA) of chemical and physical variables
Statistical steps
Manage left-censored data
Principle component analysis
Generate independent stressor gradients
Correlations
Transform WAs into ordinal ranks (10-pt scale)
Assigned genus TIVs to each synthetic stress gradient & variable
1 = sensitive; 10 = tolerant

23. Weighted Average Results
Synthetic gradients
Ionic concentration – sulfate, conductivity, pH
Nutrient concentration – phosphorus, NO5 (inc. chloride)
D.O./water temp
Uncorrelated physical variables
Suspended sediment
Percent fines

24. USGS Results
Calculated TIVs for 102 genera and 67 families for each synthetic stress gradient and variable
Used common genera
Used 300 organism fixed-count method
100 of those 102 genera are documented in Colorado
Source: CO EDAS

25. Application The next logical step in Colorado bioassessment
EDAS MMI Thresholds Stressor ID
Colorado’s MMI is a tool that is specifically calibrated to detect impairment to aquatic life…but it is limited to detecting stress to the aquatic community, not the specific stressor(s)
TIVs provide the means to identify specific stressor(s) by understanding the sensitivities and tolerances of genera to those stressors

26. Testing TIV Concept in Colorado
Tested % fines and phosphorus in 2012
Presented findings at Sep 25, 2012 Listing Methodology workgroup meeting (in Avon, CO)
Workgroup saw merit in TIVs but recommended CO-specific TIVs because many genera common to CO were missing
303(d) cycle cancelled

27. Current and Future Work
CO-specific common species
CO-specific TIVs and Stressor Identification
Test Site Analysis - multimetric and multivariate metrics are used as the raw data to compare the biological condition of a test site and comparable reference sites
Method includes all available biological information, accounts for and identifies redundant information which decreases the probability of misclassifying a test site