Developing Wetland Bioassessment Models in

Support of Long Term Vital Signs Monitoring

Billy Schweiger, NPS ROMNJim Grace, USGSDon Schoolmaster, USGSDavid Cooper, CSUKatherine Driver, USFSBrian Mitchell, NPS NETNGlenn Guntenspergen, USGS

National Park Service and United States Geological SurveyU.S. Department of the Interior

George Wright Society, 2011

Acknowledgements

The Issue• Bioassessment

Strong appeal: synthetic and relevant to park visitors

• Models: MMI (IBI, IEI), O:E Great value in long term

monitoring

• Genesis Broad scale Application to scorecards Minimal ecological

interpretation

• Does this work at the scale of Parks? Scorecards and diagnostic

applicationFigure from Maddock 1999

US EPA Wadeable Streams Assessment

ROMO

GLAC

GRKO

FLFO

LIBI

GRSA

ROMO

GLAC

GRKO

FLFO

ROMO

GLAC

GRKO

FLFO

LIBI

GRSA

A graphical, causal network perspective Assess (human)

disturbances.

D_type 1 D_type 2 D_type 3

Human Disturbance Index

Construct disturbance

index.

Construct index of biotic or

ecologic integrity.

Index of Integrity

$

Select biotic response metrics.

metric M1 metric M2 metric M3

Environmental covariate(s)

directeffect

indirecteffect

Three scenarios of interest:(1) where metrics are only affected by HDI (“Clean”)(2) where metrics are also affected by independent E1 (“Dirty”)

(3) where metrics are also affected by an E2 that also influences patterns of human disturbance (“Really Dirty”)

HDI

M1

β1

M2

E1

β1βE

Environmental gradients: demonstrating their effect

E2

M3

β1 βE

βHE

Coefficients specify the true effects.

Metric 1 appears superior to Metric 2 because HDI’s true effect on M2 is obscured by additional variance resulting from the effects of E1. We would erroneously conclude that M1 was a better indicator of HDI.

Environmental gradients: demonstrating their effects

M1

HDI

M2

E1

0.60 0.600.84unobscured standardized effects

“Dirty Metrics”

Site Level HDI vs. Elevation by Wetland Type

-20 0 20 40 60 80 100 120

HDI

2200

2400

2600

2800

3000

3200

3400

3600

3800

4000

Site

_ELE

V_M

r2 = 0.1778; r = -0 .4216, p = 0.0000002; y = 3100.42329 - 5.4980911*x Fen Riparian W et M eadow

Apparent relationship between M3 and HDI stronger than true relationship – M3 incorrectly selected

Environmental gradients: demonstrating their effects

M3

E2

M1

HDI

0.60

0.60

0.80

0.60

“Really Dirty Metrics”

HDI0 10.3

Reference

Common approach to metric adjustment

“Reference Set Residualization”

Predict values at all sites based on residualization from ‘disturbance free’ reference set

Done sequentially with effects of covariates and HDI on metrics treated in series

0.84

M1

HDI

M2

E1

0.60 0.60

“Dirty Metrics”

0

M1

HDI

M2

E1

0.60 0.60

Reference set adjustment can work when conditions are right…

How well does reference set adjustment work?

Some of the signal from HDI to M3 removed by adjustmentIn this likely common situation, reference set residualization does not

work!

Large decrease in r… “overcleaned”

“Really Dirty metrics”

M3

E2

0.600.84

M1

HDI

M2

E1

0.60 0.60

0.80

0

How well does reference set adjustment work (cont.)?

0.84

Performance depends on purity

true signal

0 1 6 10 12 18 23 30 35 40

approx. signal loss, %

Performance depends on sample size

Assumptions of reference set residualization1. Large sample size for

reference set

2. HDI 0 in reference set, therefore E and H will be independent

3. The reference set is representative of the whole set and, therefore, Eref -> Mref = Eall -> Mall

4. Defining a priori meaningful reference condition in parks is even possible

What is the reference for a pristine fen, deep in the wilderness of Rocky?

3 3 ( ) 3 ( ) 3( )M H E M E H M HEM b H b eqn. 1: est. effect of HDI on M3

3 3 ( ) 3( )ˆ

M H E M HEM b H eqn. 2: calculate part of M3 explained by HDI

Use the complete data set and simultaneously consider the effects of HDI and covariates on metrics in a single model, not a series of residualized adjustments M3

E2

M1

HDI

β1 β1 βE

βHEEnvironmental gradients: whole-set model-based adjustment

No longer any assumptions about properties of a reference set

Sample size still matters, but is less severe as all data are used

Environmental gradients: whole-set model-based adjustment

recall true effect = 0.60.

Efficient in complex and general cases

E2

M5

HDI E1

E3

“Really Really Dirty Metrics”

(no)Beaver Meadow, ROMO

The Real Data… • Wetland condition in

ROMN and NETN network parks Bioassessment

• Vegetation Covariates

• Sample design & Sites GRTS & Sentinel

• Wetland types Fen near Grand Ditch

Fen (peatland)

Riparian

Wet Meadow

Non forested (Acadia)

Category Metric r env adj r

adj p value Env. Gradient

Conservatism Mean C -0.610 -0.722 0.000 % Non Granitic

Nativity Absolute Cover Native Forbs -0.601 -0.636 0.000 Precipitation

Taxonomic composition

Richness Salicacae 0.597 0.514 0.000 % Granitic, %

Non Granitic

Conservatism Absolute Cover Intolerant -0.241 -0.488 0.001 % Glacial Till

Conservatism Cover Weighted FQI -0.455 -0.448 0.004

Taxonomic composition

Absolute Cover Poaceae 0.033 -0.431 0.006 Elevation,

Slope

Wetland status

Wet Indicator Native -0.330 -0.375 0.020

ROMO IBI (Riparian Example)• Final metrics

Other methods IBI vs. HDI

• Regression tree thresholds Park-scaled

reference condition

• Design-based inference to the park ~70% (+/-30) of

ROMOs riparian wetland in reference condition, 2007 - 2009

• Site scale: Empirical CDF ~50% (+/-15) of

sampled sites in reference

E m p i ri ca l CDF o f RO M O Rip a ri a n IB I

M e a n = 4 .7 8 8 5 1 3 , S td .De v = 1 .9 0 9 0 0 1 , N = 3 8

0 1 2 3 4 5 6 7 8 9

L a te st_ Rip _ IB I

0

1 0

2 0

3 0

4 0

5 0

6 0

7 0

8 0

9 0

1 0 0

Per

cent

E m p i ri ca l CDF No rm a l 9 5 % L o we r Co n fi d e n ce B a n d 9 5 % Up p e r Co n fi d e n ce B a n d

ROMO IBI Application

• Scorecard [DRAFT! - Park evaluating… don’t remember this!]• Complete set of

wetland responses

• Just the IBIs

• Useful result Better when also a

trend

• But not diagnostic…

General Causal Network Hypothesis

conductivity

Sphagnumspeciesrichnesstrees richness max ht Typha

surroundingdevelopment

hydrologicalteration

proportion of timesoil above water

recentsubstratedisturbance

Elementsof

Disturbance

EnvironmentalCovariates(mediators)

BiologicalMetrics

Acadia NP Non Forested

Wetlands

Note: causal networks are models that represent a causal hypothesis; it is not claimed that they, by themselves, prove causation.

• Bioassessment in National Parks Likely useful, but classic methods may not be ideal Needs to deal with environmental gradient covariates Interpretation and diagnostic needs

• Our new approach efficiently deals with covariance Will lead to more interpretable component metrics in

the IBI

• Rocky Mtn. NP IBI Current application is a scorecard

• Beginning to develop Causal Network Acadia

Conclusions

Thank You!