Response Design

Inland AquaticsStream Example

Anthony (Tony) R. OlsenUSEPA NHEERL Western

Ecology Division200 S.W. 35th StreetCorvallis, OR 97333Phone: 541 754-4790

Email: olsen.tony@epa.gov

Purpose

• Describe elements of response design• Examples and considerations• Indicator development and evaluation example• Focus on process rather than specific examples• What would you need to think about for your own

monitoring program?

Response Design - What Is It?

• Once you have selected a site to visit, how do you sample it for the selected indicators?

• Response design can have both a temporal and a spatial dimension.

• Requires defining the “target population” for which the design is applicable.

• Ultimately, it includes how you collapse the measurements into an “indicator”

• Integrated into a daily operational scenario that can be consistently implemented by a field crew at a lot of different stream types and still provide comparable data

Target Population

• The portion of the systems about which you want informatione.g. All 1st - 3rd order streams, all streams and rivers on non-private land, all streams and rivers as defined by 1:100,000 map scale, all lakes > 1 ha., emergent palustrine wetlands.

• Response design might vary with subpopulations within the target population

e.g., wadeable streams versus large rivers.

Response Design: Indicators

• Indicators of Condition: Vertebrate Assemblage Macro-invertebrate AssemblagePeriphyton Assemblage

• Indicators of Stress: Physical Habitat (in-stream and near-stream)Ambient Chemistry (nutrients, major ions)Fish Tissue Contamination (mercury, organic contaminants)Watershed/Landscape Characteristics

• Indicator can be derived as:Direct measure Metrics representing structural or functional attributes

Response Design Scales

Watershed

Riparian

Reach

Landscape

Response Design: Index Period

• When to sample• Desirable qualities

Stable conditions• Maximize among-site variability, minimize within-site variability of all

indicatorsBiota present and amenable to collection

• May require compromises for multiple indicators• Influenced by logistics

Number of sites (or sampling trips)Number of field crewsWhen are they available and for how long?

Response Design - Fish

0 10 20 30 40 50 60 70 800

20

40

60

80

100

Stream Length(Channel Width Units)

SpeciesRichness

(% of Maximum)

• 1-pass sampling• Spread effort throughout

reach• Get “common” species in

approx. relative abundance

Response Design: Benthos and Periphyton

A

B

CKJ

IH

G F

E

DFLOW

Distance between transects=4 times mean wetted width at X-site

X-site

Total reach length=40 times mean wetted width at X-site (minimum=150 m)

R C

L

C

RL

CR

L

SAMPLING POINTS• L=Left C=Center R=Right• First point (transect B)

determined at random• Subsequent points assigned in

order L, C, R

Response Design: Benthos• Composite sample from samples collected throughout reach

Many small better than a few large• 1 ft2 kick or sweep samples

• Sufficient material to enumerate 500 individuals11 samples

• Obtain sample from every streamSample at each transect

• Comparability with reference site study500 µ mesh net8 samples from riffles

• Minimize equipment1 net for all samples

Response Design: Periphyton

• Composite sample from samples collected throughout reachMany small better than a few large

• 12 cm2 scrub or slurp samples

• Sufficient material to enumerate 500 diatom valves, filter 50 mL for chlorophyll and biomass

11 samples• Obtain sample from every stream

Sample at each transect• Minimize effort

Sample at same points as for benthos

Essential Stream Physical Habitat Elements

•• Channel DimensionsChannel Dimensions: Nothing may be more important than space

Without it-- other elements do not matter

•• GradientGradient: hydraulic “energy” of a streamused with size to determine stream power and shear stress

•• SubstrateSubstrate Size and Type: important for fish, benthos, periphyton

•• Complexity & CoverComplexity & Cover: Niche diversity, protection from predation

Essential Stream Physical Habitat Elements

•• Riparian VegetationRiparian Vegetation Cover and Structure: Temperature, organic inputs, channel morphology

•• ChannelChannel--Riparian InteractionRiparian Interaction: Channel Characteristics altered by riparian and catchment land use, which in turn influence terrestrial-aquatic interactions

•• Anthropogenic AlterationsAnthropogenic Alterations: diagnose stream disturbance and “reference condition”

• Note: Chemistry, Nutrients, Temperature:Also need other physical and chemical data to interpret biological data

Physical Habitat Characterization(40 channel width study reach)

• Long. Profile at 100 equidistant points:Thalweg Depth, Surficial fines, Habitat Class

• Woody Debris Tally (continuous)• 21 Equidistant Cross-Sections:

Width, Substrate• 11 Equidistant Cross-Sections & Plots:

Channel Measures: Slope, Bearing, Channel Dimensions, Fish Cover, Canopy Cover, Substrate EmbeddednessRiparian Measures: Bank Characteristics, Human Disturbance, Riparian Vegetation Type, Structure and Cover

• Whole Reach: Channel Constraint, Flood/Torrent Evidence• Near X-Site: Discharge

Response Design: Physical Habitat

H

I

J

K

E

D

C

G

B

F

A

C

Thalweg profileintervals

Channel/RiparianCross section

Transect

Upstream end ofsampling reach

Downstream end ofsampling reach

Riparian Vegetation &Human Disturbance

Substrate and ChannelMeasurements

Instream fish cover

10 m

10 m

10 m

Woody Debris Tally(between transects)

Meter ruler orcalibratedrod/poleSurveyor’s rod

or measuring tape

RightBank

25%WettedWidth

50%WettedWidth

75%WettedWidth

LeftBank

10 m

10 m

10 m

10 m

RIPARIANPLOT

(Left Bank)

RIPARIANPLOT

(Right Bank)

Flow

Cross-section Transect

5 m 5 m

Instream Fish Cover Plot

Physical Habitat “Quality” Metrics

• Riparian Vegetation: Complexity, Cover• Riparian Disturbance: Proximity-Weighted Tally• Substrate: Fines, Embeddedness, Bedrock, Macrophytes

Algae• Channel Alts: Pipes, Revetment, Rel. Bed Stability,

Deviation in Resid. Pool Vol• Volume : Width, Cross-Sectional Area, Residual Pool, %Dry• Complexity: CV Depth, Sinuosity• Cover: Separate and Sum of 6 Cover Types• Velocity: Slope, Shear Stress

• “Residual Pool Area”: Depths, slopes used to estimate volume of water remaining at zero flow

• Independent of discharge, sensitive to activities that alter LWD, sediment inputs

Quantifying Pools Using Residual Pool Concept

“Old growth” 120 yr after torrent

15 yr after torrent that deposited LWD, gravel

1 yr after torrent that severely scoured channel

0

20

40

60

80

100

120

0 1 2 3

R-poolSD-DpthX-Width

Spacing (ChW’s) on 40 ChW Reach

% o

f Tr

ue V

alue

Effect of Measurement Spacing (from Robison, 1998)

Signal to Noise Variance Ratio (MAHA 93-96) Streams : Replicates

0 5 10 15 20 25

Mean Substrate dia.% Canopy DensityResidual Pool Area% Sand + FinesBed StabilityRiparian Agriculture% Undercut Bk (visual)% Pool Hab (visual)"RBP" Habitat Score

The Long and WindingRoad… Interpretation &

Assessment

Metric/Indicator

Development &

Evaluation

Survey Analysis

Data Generation

Data Entry&

VerificationValidation

An Index - Bringing It All Together• Eliminate metrics with insufficient range (raw scores 0-2 or

less) - 15 metrics fail this test• Eliminate metrics with high variability (signal:noise ratio < 3) -

2 metrics fail this test• Correct remaining metrics for watershed size if necessary (n =

15) - these metrics normalized for 100 km2 watershed• Eliminate redundant metrics (Pearson r > 0.75) - 2 metrics fail

this test• Analyze metric responsiveness to disturbance - 10 most

responsive metrics retained• Score metrics using reference/test sites in calibration data (1

metric could not be calibrated)• 9 remaining metrics combined in final IBI• IBI tested for ability to discriminate known disturbance

gradients using test data set

The Process - Data Sets

• Agreement on data - Mid-Atlantic EMAP streams data, 1993-96 (excluding only Coastal Plain sites)

• Calibration data to include all sites with quantitative physical habitat data (n = 177)

• Validation data (set aside, and not used in IBI development) includes all remaining sites (n = 119)

• 57 candidate metrics calculated

Candidate MetricsNATIVFAM Number of families representedNREPROS Number of reproductive guildsNSANGU number of anguilla speciesNSATHER number of atherin speciesNSBENT2 Number of native bent_inv species minus 3 taxaNSCATO number of sucker speciesNSCATO2 Num. of native intolerant CatostomidsNSCENT Sunfish Species RichnessNSCOLU number of water column speciesNSCOTT number of sculpin speciesNSCYPR2 number of intolerant cyprinid speciesNSDART number of darter speciesNSDRUMX number of drum speciesNSESOXX number of esox speciesNSFUND number of fundelis speciesNSGAMB number of gambusia speciesNSICTA number of ictalurid speciesNSINTOL number of intolerant speciesNSLAMP number of lamprey speciesNSPERCO number of percopsis speciesNSPPER number of perch speciesNSSALM Trout Species RichnessNSUMBR number of umbridae speciesNTROPH number of trophic guildsNUMFISH number of individuals in sampleNUMNATSP number of native speciesNUMSPEC Total number of fish speciesPANOM Proportion of individuals with anomoliesPATNG prop. of indiv. as attacher non-guarder

PBCLN prop. of indiv. as bc spwn clear substr.PBCST prop. of indiv. as broadcast spawnersPBENT prop. of fish as benthic insectivoresPBENTSP prop. of benthic hab. sp. in native sp.PCARN prop. piscivore-invert.(piscinv+pisciv)PCGBU prop. of indiv. as clear gravel buryersPCOLD1 Prop. of cold water individualsPCOLD2 Prop. of cold & cool water individualsPCOLSP prop. of column sp. in native sp.PCOTTID prop. of individuals as cottidsPCYPTL prop. of ind. as tolerant cyprinidsPEXOT prop. of individuals as introducedPGRAVEL prop. of simple lithophilsPHERB prop. of individuals as herbivoresPINSE prop. of indiv. as native insectivoresPINVERT prop. of invertivoresPMACRO prop. of macro-omnivoresPMICRO prop. of micro-omnivoresPMICRO2 Prop. of micro-omnivores minus RHINATROPNEST prop. of indiv. as nest associatesPNTGU prop. of indiv. as nester guarderPOMNI prop. Omninore individuals (pmicro+pmacro)POMNI_H prop. omni-herbiv.(pmicro+pmacro+herbiv)PPISC prop. of individuals as carnivoresPPISCIN2 Prop. of piscivore-insectiv. minus SEMOATROPPISCINV prop. of piscivore-insectivoresPTOLE prop. of individuals as tolerantPTREPRO prop. tolerant reproductive guild individuals

Range Test

Question: Do all metrics have enough of a range that they willcontribute useful information to an IBI?

Answer: No. Several metrics have values (only) of 0, 1 or 2. These metrics were dropped from the candidate list:

NSANGU NSATHERNSCATO2 NSDRUMXNSESOXX NSFUNDNSGAMB NSICTANSLAMP NSPERCONSPPER NSSALMNSUMBR

Signal:Noise Test

Question: Are all metrics sampled reliably, i.e., do repeated measurements at a single site yield the same results?

Answer: No. Two metrics have signal:noise ratios (ratio of within site variance to between site variance) less than 3. These metrics were dropped from the candidate list:

NTROPHPNEST

Watershed Correction

Question: Do metrics show strong correlations with watershed size, so that their scores need to be normalized (watershed size effect removed?)

Answer: Yes. These metrics need to be corrected for watershed size effects:

NATIVFAMNREPROSNSBENT2NSCATONSCENTNSCOLUNSCYPR2NSDARTNSINTOL

NUMFISHNUMNATSPNUMSPEC

PATNGPBENTPCARNPINSE

PINVERT

Watershed CorrectionApproach: Use relationships observed at reference sites

to define ‘natural’ element of watershed size effect

Reference Sites

Watershed Size (km2)

1 10 100

No.

Ben

thic

Spe

cies

(min

us 3

tole

rant

taxa

)

0

2

4

6

8All Sites

Watershed Size (km2)

1 10 100

No.

Ben

thic

Spe

cies

(min

us 3

tole

rant

taxa

)

0

2

4

6

8

3. Calculate ‘residuals’ for all sites.

2. Apply reference regression to all sites.

1. Calculate regression for reference sites

Watershed Size (km2)

1 10 100

Res

idua

l fro

m R

egre

ssio

n

-3-2-10123456789

10

Watershed CorrectionApproach: Use relationships observed at reference sites to define

‘natural’ element of watershed size effect

Watershed Size (km2)

1 10 100

No.

Ben

thic

Spe

cies

(min

us 3

tole

rant

taxa

)

0

2

4

6

8 1. Calculate expected value for reference data set at 100 km2

3. Add 100 km2expected value to residuals to create all positive metric, corrected for watershed size.

2. Take residuals from previous step.

5.6

Result: Each metric scored against its expected value in a reference site with watershed area = 100 km2

Redundancy Test

Question: Are all metrics independent?

Answer: No. Two pairs of metrics have Pearson r > 0.75. Only one of each pair can be used in final IBI. These metrics were dropped from the candidate list:

NCOLD1 (redundant with PCOLD2)PBCLN (redundant with PMACRO)

ResponsivenessDisturbance Metrics

(each metric evaluated for responseto each of 18 disturbance gradients)

Chemical:•pH•sulfate concentration•total nitrogen concentration•total phosphorus concentration•chloride concentration

Habitat:•Percent Sands and Fines•Bed Stability•Density of Large Woody Debris•Fish Cover•Riparian Disturbance•Channel and Riparian Disturbance Index•Watershed Quality Index•Watershed & Riparian Quality Index•Watershed, Riparian & Channel Habitat

Quality Index•Channel Habitat Quality Index

Integrated Measures:•Disturbance Class

(Mine Drainage, Acid Rain,Nutrients, etc.)

•Watershed Condition Class (Bryce et al., 1999) Natural drivers (included as a check):

•Reach Slope

Responsiveness - ExampleNumber of Intolerant Taxa (Adjusted for Watershed Size)

Condition Class

1 2 3 4 5

No.

Into

lera

nt T

axa

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Disturbance Class

No.

Into

lera

nt T

axa

0.0

0.5

1.0

1.5

2.0

2.5

3.0

AcidRain

MineDrainage

MixedImpacts

Nutrients Reference

pH

5 6 7 8 9

No.

Into

lera

nt T

axa

0

1

2

3

4

5

6

7Sulfate

101 102 103 1040

1

2

3

4

5

6

7Chloride

101 102 103 1040

1

2

3

4

5

6

7Total

Nitrogen

101 102 1030

1

2

3

4

5

6

7

TotalPhosphorus

100 101 102

No.

Into

lera

nt T

axa

0

1

2

3

4

5

6

7

Riparian Disturbance

0.0 0.2 0.4 0.6 0.8 1.0

No.

Into

lera

nt T

axa

0

1

2

3

4

5

6

7Watershed

Quality Index

0.2 0.4 0.6 0.8 1.00

1

2

3

4

5

6

7Channel

& RiparianDisturbance

0.3 0.4 0.5 0.6 0.7 0.80

1

2

3

4

5

6

7

% Sandsand Fines

0 20 40 60 80 1000

1

2

3

4

5

6

7Bed

Stability

-3 -2 -1 0 1 20

1

2

3

4

5

6

7Fish Cover

0.00 0.05 0.10 0.150

1

2

3

4

5

6

7

Watershed& Riparian

Quality Index

0.2 0.4 0.6 0.8 1.00

1

2

3

4

5

6

7

Watershed,Riparian

& ChannelQuality Index

0.2 0.3 0.4 0.5 0.6 0.7 0.8

No.

Into

lera

nt T

axa

0

1

2

3

4

5

6

7Channel Habitat

Index

0.2 0.3 0.4 0.5 0.6 0.70

1

2

3

4

5

6

7Large

Woody Debris

0.000 0.005 0.010 0.015 0.0200

1

2

3

4

5

6

7Log(Slope)

-1 0 10

1

2

3

4

5

6

7

(Plots outlined in red illustrate good metric

response)

Special Case #1Number of Fish Metric

Reference Non-Reference

No.

Fis

h C

olle

cted

/100

km

2 wat

ersh

ed

0

100

200

300

400

500

600

700

Result: While this metric passed all of our tests, if scored like all other metrics, more than half of sites would score 10. The amount of

information gained by its use is too small to include it in final IBI

Median from reference sites = 1010th Percentile from test sites = 0

Special Case #2 - ‘Fishless’ SitesIf fishless sites are scored as IBI=0

Percent Undisturbed Land Cover0 10 20 30 40 50 60 70 80 90 100

IBI S

core

0

10

20

30

40

50

60

70

80

90

100

{

These zero valuesmay be reasonable

{But what about

these?

Habitat Volume

Habitat Volume Index

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

Num

ber o

f Ind

ivid

uals

Col

lect

ed

0

200

400

600

800

1000

1200

1400

1600

Conclusion: High probability of ‘fishless’ streams when Habitat Volume Index falls below 0.4

Watershed Size (km2)0 5 10 15 20 25

Hab

itat V

olum

e In

dex

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Minimum Watershed Size

Conclusion: Habitat Volume Index Values < 0.4 common in watersheds less than 2 km2. Below this threshold, we cannot confidently expect to

encounter fish - set IBI to missing when number of fish is < 10.

Final MetricsClass of Metric Metric

NameDescription Responds to:

Tolerance Metrics NSINTOL4 No. Intolerant Taxa Chemistry, Channel Habitat, WatershedCondition

PTOLE Proportion of Tolerant Taxa Chemistry, Channel Habitat, WatershedCondition

Count Metrics NUMFISH Number of Fish Collected Nutrients (positive response)

Reproductive Metrics PGRAVEL Proportion of Simple Lithophils Channel Habitat

Habitat Metrics PCOTTID Proportion of Cottids Nutrients, All Habitat measures

NSBENT23 Number of Benthic Species Disturbance Classes

NSCYPR3 Number of Cyprinid Species Condition Classes

Alien Metrics PEXOT Proportion of Introduced Individuals Introduced Species

Trophic Metrics PMACRO Proportion of Macro-omnivores Nutrients

PPISCIN2 Proportion of Piscivore/Insectivores All Habitat measures

Metric Scoring

• All metrics scored on continuous scale, from 0 to 10• Scoring based on distributions of reference and test site

scores in calibration data• Upper limit (10 set by 50th percentile score in the

reference distribution• Lower limit (0) set by 10th percentile score in the non-

reference distribution

IBI ThresholdsHow to set thresholds for IBI assessment?

Goal: Use the distribution of IBI scores in reference sites to set thresholds between good, fair and poor IBI scores:

IBI > 25th reference percentile = good5th < IBI < 25th reference percentile = fair

IBI < 5th reference percentile = poor

One difficulty: There are multiple ways to define reference, and each gives a different reference distribution:

• least restrictive: based on chemical and RBP habitat filters(n = 27, good geographic coverage)

• moderately restrictive: adds quantitative habitat filters(n = 23, good geographic coverage)

• most restrictive: adds watershed condition class (1 or 2)(n =12, restricted geographic coverage)

Final IBI Responsiveness

Calibration Data Set

Watershed Condition ClassPristine Degraded

1 2 3 4 5

IBI S

core

0

10

20

30

40

50

60

70

80

90

100

Watershed Condition Classes from Bryce et al., 1999, JAWRA

Validation Data Set

Degraded Reference

IBI S

core

0

10

20

30

40

50

60

70

80

90

100

Kruskal-Wallis Test: p = 0.0002

Using Reference and Test Site “Filters”

IBI ThresholdsHow to set thresholds for IBI assessment?

Goal: Use the distribution of IBI scores in reference sites to set thresholds between good, fair and poor IBI scores:

IBI > 25th reference percentile = good1st < IBI< 25th reference percentile = fair

IBI < 1st reference percentile = poor

One difficulty: There are multiple ways to define reference, and each gives a different reference distribution:

• least restrictive: based on chemical and RBP habitat filters (n = 27, good geographic coverage)

• moderately restrictive: adds quantitative habitat filters(n = 23, good geographic coverage)

• most restrictive: adds watershed condition class (1 or 2)(n =12, restricted geographic coverage)

IBI ThresholdsSolution? Use information from all 3 reference definitions to set

thresholds - acknowledge uncertainty involved in any one definition

Calibration Data Set

Restrictions on Reference Definition

Low Medium High

IBI S

core

50

60

70

80

90

100

Good

Fair

Poor

Mean of 25th %tiles

Mean of 1st %tiles

Fish IBI Results

17%

17%

36%

31%

Proportion of Stream Length

(InsufficientData)

Good

Fair

Poor

IBI ResultsGeographic Distribution

10%21%

34%28%

Valleys

10% 14%

31%

41%

North-Central Appalachians

15%

22%

35%11%

Ridge and Blue Ridge

35%

3%

28%

26%

Western Appalachians

(InsufficientData)

Response Design Summary• Involves entire process from obtaining measurements at a

site through calculation of indicators for the site.• Field plot design has both spatial and temporal dimensions

Size of the support for the plotSampling restricted to index period during the year

• Integrated to provide cost-effective, consistent data when implemented by multiple field crews

• Metrics and Indicators are calculated with respect to the elements of the target population

• Indicators must be calibrated so that their scores have the same meaning for any element in the target population

• Assessment decisions are categorical indicators.