multi-scale and multi- dimensional controls on biota and ... · multi-scale and multi-dimensional...

Multi-Scale and Multi-

Dimensional Controls on Biota

and Physical Habitat

Phil Kaufmann, Ken Bazata, Lyle Cowles,

and Dave Peck

Lawrence, KS

April 2001

EMAP-Surface Waters Objectives

• Estimate Extent of Lake and Stream Resource

– number/area of lakes, stream miles

• Estimate Current Status of Ecological Condition

– at regional scale with known confidence

• Estimate Changes and Trends in Condition

• Describe Associations between Status/Trends in

Conditions and Differences/Changes in Human

Activity

• Diagnose Probable Causes of Impairment

Biological Condition

(e.g., species richness)

Land Use

Human Disturbance Natural controls

(stream size, elevation, slope)

Chemical Habitat

Land use and many natural controls affect biota

indirectly through their effect on habitat.

Physical Habitat

HABITAT

... the set of conditions that support

and control the distribution and

abundance of aquatic organisms:

Physical

Chemical

Biological

Consider Landscape and Historical

Contexts

PHYSICAL HABITAT INDICATOR

DEVELOPMENT

Determine Aspects of Interest

Define Metrics to Quantify Aspects

Develop Field Monitoring Protocol

Quantify Variability, Precision

Demonstrate Ecological Relevance

-- biological associations

-- sensitivity to human disturbance

8/97

• Water Chemistry

• Nutrients, Temperature

• Biotic Interactions

Natural Controls & Human Influences

BENTHOS SUBSTRATE SIZE

Land and Water

Management

Sediment Supply:

* Geology, Relief

* Climate, Vegetation

* Disturbance

Stream Competence:

* Flow, Slope, Morphol.

Substrate Erosivity:

* Armoring

* Channel Complexity

Elements of Stream Physical Habitat

Channel Dimensions

Gradient

Substrate Size and Type

Hab. Complexity & Cover

Riparian Vegetation Cover and Structure

Channel-Riparian Interaction

Anthropogenic Alterations

Note: Chemistry, Nutrients, Temperature

11

Adequate Habitat Indicator?

• Accurate & Responsive -- Does it

measure what we intend ?

• Precise -- Can we separate changes or

differences from measurement error?

• Relevant -- To Biological needs?

Ecological processes? Societal values?

• Practical -- Can we do it? ...afford it?

12

Associations with Habitat Data

• Raw Measurements

• Habitat Characterization Metrics

• Measures of “Alteration” of Particular Habitat

Features

• Measures of “Quality” of Particular Features

• Integrated Habitat Disturbance Assessment

• Integrated Habitat Quality Assessment

• Multi-Dimensional Associations

• Multi-Scale Associations

13





Features






R7 IBI vs Substrate fines, sand+fines, mean diameter, and

bed stability (R=Upland,B=E.Lowland, G=W.Plains)

Streambed Stability vs Riparian Agriculture: Varies by Stream Size (R=Uplnd, B=E.Lowland, G=W.Plains)

W<5m W >5-<12 m

W > 12- <32 m W >32 m

16





Features






Habitat Quality

18





Features






Habitat “Response Curves” “Q

UA

LIT

Y”

1

MODELLED

RESPONSES :

* Monotonic Increase

* Monotonic Decrease

* Threshold Response

-- Hi, Low, Both

* Hyperbolic

1

0

0

HABITAT MEASUREMENT

20





Features






Physical Habitat Quality

Index (Sub-components)

1. Riparian Vegetation

2. Rip. Human Disturbance

3. Substrate

4. Channel Alterations

5. Habitat Volume

6. Structural Complexity

7. Fish Concealment

8. Water Velocity

Habitat Quality Index Components

• Rip. Veg. ----- Complexity, Cover

• Rip. Disturb-- Proximity-Weighted Tally

• Substrate --- Fines, Embeddedness, Bedrock, Macrophytes Algae

• Channel Alts-- Pipes, Revetment, Rel. Bed Stability,

Deviation in Resid. Pool Vol

• Volume ------ Width, X-Sect. Area, Resid. Pool, %Dry

• Complexity --- CV Depth, Sinuosity

• Cover --------- Separate and Sum of 6 Cover Types

• Velocity ----- Slope, Shear Stress

Habitat

Qualit

y

Most Hab Volume Var’s Disturb, % Dry,

Shear Stress, Fines

Ripar Veg Complexity Canopy Cover Most Fish Cover Var’s

Habitat Quality Index Calculation

(1/8)

Component 1 = Mean of Subcomponents

Component 2 = Mean of Subcomponents

--- etc for 8 Components

Quality Index = (1 x 2 x 3 x 4 x 5 x 6 x 7 x 8)

Reg7 IBI vs Habitat Quality Index (Red=Uplands, Blue=E.Lowlands, Green=W.Plains)

Width Classes for Reg 7

Streams

• Class 1 : < 5 m

• Class 2 : 5 - 13 m

• Class 3 : 13 - 32 m

• Class 4 : > 32 m

Reg7 IBI vs Habitat Quality Index by stream width

classes (Red=Uplands, Blue=E.Lowlands, Green=W.Plains)

W<5m W >5-<12 m

W > 12- <32 m W >32 m

Reg7 IBI vs Habitat Quality Index and Sub-Components (Red=Uplands, Blue=E.Lowlands, Green=W.Plains)

IBI vs Phab Correlations (Signif p<.05)

(Spearman r * >.10, ** >.20, *** >.30 etc.) Rveg Rdist Sub ChAlt Vol Comp Cov Velo QTPH1

====================================================================

Whole Reg . * *** *** . * * * ****

----------------------------------------------------------------------------------------------------

E. Lowland * . *** **** . ** . ** ****

Uplands . . . . . ** . **** **

W. Plains. . *** *** . . . . **** -------------------------------------------------------------------------------------------------------------------

Width <5m . . *** ** . . . . ***

Width 5-32 . * **** ***** . **** * *** *****

Width >32 (**) **** (**) (***) (**) (**) (**) . (**)

Reg7 IBI MLR by Width Class

<5m 5-32m >32m All+SbQual -ChDist +Cover -ChDist

+RipVeg +SbQual +SbQual +SbQual

-NPAI -SedMet +RipVeg

+Complx -NPAI

______ +Cover _______ ________

19% 38% 73% 22%

% Variance in Reg 7 IBI (6) Explained By

Indexes of Habitat and Chemistry

0

5

10

15

20

25

30

35

<5m 5-13m 13-32m >32m All

Habitat

SedMet

NPAgInd

Stream Width Classes

% Variance in Reg 7 IBI Explained By

Habitat Index and its Components

0

5

10

15

20

25

30

35

40

45

50

1 2 3 4 All

Index

Separates

Stream Width Classess

% Variance in Reg 7 IBI Explained By Hab Index

Components + Chemistry (NPAI + MTLINDX)

0

10

20

30

40

50

60

70

80

90

100

1 2 3 4 All

8 QP H's

8 QPH + 2 Chm

Stream Width Classess

34





Features






The Mid-Atlantic Highlands

Indicators of Condition: Fish Community Structure (IBI)

Macroinvertebrate Community Structure (EPT)

(Periphyton Community Structure)

Indicators of Stress: Physical Habitat (in-stream and near-stream)

Ambient Chemistry (nutrients, major ions)

Fish Tissue Contamination (mercury, organic contaminants)

Watershed Characteristics

MAHA Study Design: Indicators

MAHA Study Design: Probability Survey

MAHA Study Design: Sampling Design

Watershed

Riparian

Reach

Landscape

Fish IBI Results

17%

17%

36%

31%

Proportion of Stream Length

(Insufficient Data)

Good

Fair

Poor

10%

23%

37%31%

Valleys

10%15%

32%

43%

North-Central Appalachians

15%

28%

44%14%

Ridge and Blue Ridge

Fish IBI Results Geographic Distribution

35%

3%

32%

30%

Western Appalachians

(Insufficient Data)

Ranking of Potential Stressors

0% 10% 20% 30% 40%

Introduced Fish 34%

% of Stream Length

0% 10% 20% 30% 40%

Riparian Habitat

Sedimentation

Mine Drainage

Acidic Deposition

Tissue Contamination

Phosphorus

Acid Mine Drainage

24%

25%

14%

11%

10%

5%

1%

Nitrogen

5%

Linkages Among Indicators - MAHA streams

MLR Models for Native Fish Spp Richness (n=122)

• Drainage area 0.44 0.44

• Stream order 0.15 0.52

• Sinuosity 0.15 0.59

• SO4 0.14 0.64

• Fish Cover--Rocks 0.16 0.70

• Riparian Disturbance 0.12 0.73

• DOC 0.04 0.75

• Bed Stability 0.06 0.77

• Ripar. Canopy Cover 0.13 0.79

Variable Partial R2 Model R2

Native Fish Species Richness MLR All MAHA Streams

Natural

Controls

58%

Physical

Habitat

14%

Chemical

Habitat

7%

Unexplained

21%

MAHA Native Fish Species Richness MLR High Gradient Ecoregions

Natural

Controls

66%

Physical

Habitat

15%

Chemical

Habitat

2%

Unexplained

17%

Low Gradient Ecoregions

Physical

Habitat

12%

Chemical

Habitat

19%

Unexplained

5%

Natural

Controls

64%

Linkages Among Indicators - MAHA streams MLR Models predicting Chemical and Physical

Habitat (n=122)

Model R2: .59 .35 .23 .49 .47 .25

Predictor Variables:

Drainage Area X X X X X

Elevation X X

Slope X X

Runoff X

Pop. Density X

Road Density X

% Urban X X X

% Forest X X X X

% Agriculture X X X X

% Barren X X X X

TN TP SO4 %SaFn RP %Pool

% Variance Explained Using Different

Habitat Assessment Approaches in MLR

0

10

20

30

40

50

60

70

80

90

Fish Spp IBI EPT_taxa HBI

RBP

QPH

PHab

Mid-Atlantic Region Streams (7/97)

Watershed Quality vs:

1) Riparian Quality

2) Channel Phab

3) Chan-Ripar P-Hab

4) Chloride Conc.

Condition of watershed, riparian, channel, and water do not always agree !

Controls on Fish IBI -- Mid-Atlantic Region:

Single Watershed Var:

* Landcover Disturbed (-19)

* Road Density (-20)

* Human Pop. Density (-30)

Multi-Dimension/Single Scale:

* Watershed Condit. (+24)

* Riparian Condition (+08)

* Channel Phab Qual (+29)

Multi-Dimension/Multi-Scale:

* Chan.-Riparian Condit (+27)

* Wtrshed-Ripar Condit (+35)

* Chan-Ripar-Wtrshed (+45)

Red ------ pH<5.0

Scarlet -- SO4 >5,000 ueq/L

Multi-Scale and Multi-Dimensional Controls

on Biota and Physical Habitat Conclusions (1)

• Direct linkages between human land use and biotic condition are often difficult to draw.

• Geomorphology (watershed size, slope, elevation) causes substantial natural variability in habitat and biota.

Multi-Scale and Multi-Dimensional

Controls on Biota and Physical Habitat Conclusions (2)

• After accounting for geomorphic controls, Spp Richness (fish, benthos) and many other metrics are usually best predicted by a combination of channel (physical & chemical), riparian, and watershed variables.

• Scoring of ecological condition indices like IBI aims to minimize the influence of natural controls.



• The aspects of physical and chemical habitat most important to biota differ by ecoregion and taxonomic group.

• The primary mechanism of human land use impact on aquatic biota appears to be its influence on chemical and physical habitat.



• Best Assessments make use of combined single and integrated (multi-dimensional) variables derived from data at collected and summarized at multiple scales.

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