Pebble Counts and TIVs

Sediment Guidance WorkgroupDaniels Fund Building

Nov 12, 2013

Today’s Topics

• Division’s protocol for pebble counts• Division’s current pebble count dataset• TIVs

Drunella doddsii

WQCD’s Pebble Count Methodology

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

Equipment

• Materials needed:– Surveyors tape & stakes– Sampling frame– Gravelometer– Waterproof field sheet

• Pencil

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

Field Method

• Define reach length– Calculate sampling reach length• 20 x avg. bankfull width = reach length

– Example: 20 x 13’ = 260’

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.

0’ 26’ 52’78’

104’

26’

Flow

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

2’

0’20’

Flow

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

Field Method

• Using the sampling frame– Place corner of frame on the transect tape– Particles are collected from under all four grid points

1

2

3

4 x 10 = 40counts per transect

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

Field Documentation

• Field sheet– Habitat, bank, and water line documented

Office - Data Entry

• Data entry from field sheet to pebble count uploader

• Uploader output = CSV file

Field Sheet Electronic Template

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

Pebble Count Dataset

• 504 pebble/bug 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

Pebble Count Dataset

• Retained pairings presently used in:– TIV calculations - % Fines– Stressor Identification

Tolerance Indicator Values“TIVs”

Brachycentrus americanus

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. RuhlNational Water Quality Assessment Program, U.S. Geological Survey

12201 Sunrise Valley Drive, MS 413, Reston, VA, USA

Published inEcological Indicators 7 (2007) 22-33

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

Method Background

• 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

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

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

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

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

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

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