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Commonwealth of Virginia Flow-Ecology. Project Meeting VDEQ January 24, 2012. The ELOHA Framework modified from Poff et al. (2010). Data Sources. Spatial distribution of benthic community samples (i.e., excluding presence/absence samples) by data source.

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commonwealth of virginia flow ecology

Commonwealth of VirginiaFlow-Ecology

Project Meeting

VDEQ

January 24, 2012

slide4

Spatial distribution of benthic community samples (i.e., excluding presence/absence samples) by data source

slide5

Spatial distribution of fish community samples (i.e., excluding presence/absence samples) by data source

slide6

Distribution of hydrological pour points by stream flow classes and sizes. CP = Coastal Plain; NCP = Non-Coastal Plain; PR = Perennial Runoff; SBF = Stable Base Flow; CSIIF = Coastal Swamp Intermittent/Intermittent Flashy

slide7

Numbers of sites of associated hydrologic and biological data, separated by flow class and stream size. CP = Coastal Plain; NCP = Non-Coastal Plain; PR = Perennial Runoff; SBF = Stable Base Flow; CSIIF = Coastal Swamp Intermittent/Intermittent Flashy

  • These 171 hydro points are associated with 846 biological samples:
  • 122 hydrologic sites associated with benthic samples
  • 212 hydrologic sites associated with fish samples
slide9

Distribution of hydrologic pour points around the Commonwealth of Virginia, with each point designated by flow class membership. Watersheds shown in green were included in analyses

slide11

NMS on fish community data for all stations (statewide), categorized (by color coding) according to selected classifications. A) Coastal Plain (CP) and Non-Coast Plain (NCP; B) Ecoregion (45=Piedmont; 63=Middle Atlantic Coastal Plain; 64=Northern Piedmont; 65=Southeastern Plains; 66=Blue Ridge; 67=Ridge and Valley; 69=Central Appalachians); and C) the major river basins used in the Virginia water quality standards (WQS) (1=Potomac-Shenandoah; 2=James; 3=Rappahannock; 4=Roanoke; 5=Chowan and Dismal Swamp; 6=Tennessee and Big Sandy; 7=Small Coastal Basin and Chesapeake Bay; 8=York; 9=New River; 10=Yadkin).

slide12

NMS on fish community data, by flow class for A) all (statewide) stations; B) Coastal Plain stations; and C) Non-Coastal Plain stations. (11 = PR1 = Perennial Runoff1; 12 = PR2 = Perennial Runoff2; 21 = SBF1 = Stable High Baseflow1; 22 = SBF2 = Stable High Baseflow2; 31 = CSI = Coastal, Swamp and Intermittent; 32 = IF = Intermittent Flashy).

slide13

NMS on fish community data, all stations, for A) axes 1 and 2; B) axes 1 and 3; and C) axes 2 and 3. Color coded by classification: Group 1=Non-Coastal Plain-Ohio Drainage; 2= Non-Coastal Plain-Atlantic Drainage; 3=Coastal Plain

slide16

NMS on fish community data, with results highlighted by stream order for A) axes 1 and 2; and B) axes 1 and 3; and the same NMS result with stations highlighted by stream order size groups, where group 1=stream orders 1-3, and group 2=stream orders 4-6, for C) axes 1 and 2, and D) axes 1 and 3

slide17

Color coding indicates class member ship (red=CP; orange=NCP-Atl; green=NCP-Ohio)

lithophilic fish (J) : r = -0.344 stream size accounts for <12% of the variation in this metric

flow class groupings
Flow Class Groupings
  • McManamay, Orth and others show hydrologic distinctions between classes, but
  • Some closely related flow classes have commonalities in some characteristics
  • Combined across some of these if need to increase ‘n’ and the biology also supported this:
    • PR-1
    • PR-2
    • SBF 1 & 2 combined
    • CSI & IF combined
slide24

Statewide

The degree and types of changes in IHA metrics varies across classes, but the CP/NCP classification captures a lot of these

slide28

In this case, SBF-2 small streams show greater hydrologic changes than the other classes. May need to focus on this under-represented class in the future.

slide29

CSI and IF classes were combined for analyses – justified based on biological community similarities, but the IF class shows much larger increases in spring flows, which should be considered in the future.

biological response
Biological response
  • Flow
    • Flow velocity
    • Shear
    • Thickness of laminar layer
  • Habitat
    • Water
    • Substrate
    • What changes habitat?
      • Stream power – ability to carry & alter sediment
      • Bed stability
  • How do IHA metrics affect these?
stream size with respect to iha metrics
Stream size with respect to IHA metrics
  • Most IHA metrics include flow, hence are controlled by overall size (flow)
  • IHA flow metrics (peak flows, fall rates, etc.) are distinct power functions of mean annual flow (in modeled streams)
slide42

Summary and Implications:

  • Benthos did not show strong variation across stream sizes
  • Fish showed some variation, but not necessarily at a level that warranted correction of fish metrics to account for stream size
  • IHA metrics do bear a relationship to stream size, but this is based on modeled hydrologic outputs
  • Further consideration should be given to accounting for stream size – e.g., investigate further using gaged data
potential problem with use of percent change
Potential Problem with Use of Percent Change
  • Percent change = (current-baseline)/baseline
  • Percent change removes effect of average flow
  • BUT, percent change has potential problem depending on shape of response to flow metrics: do biological metrics show response to IHA metrics or to % change?
  • Why? And is this a problem?........... Yes
slide44

10

Response to percent hydrological change

Response

5

0

10

Hydrological metric

0

5

10

Baseline = 1, current = 2; change=100%

Response

5

0

0

100%

200%

Hydrological Change, %

slide45

10

Response to percent hydrological change

Response

5

0

10

Hydrological metric

0

5

10

Baseline = 5, current = 10; change=100%

Response

5

0

0

100%

200%

Hydrological Change, %

slide46

10

Response to percent hydrological change

Response

5

0

10

Hydrological metric

0

5

10

Baseline = 5, current = 5; change = 0%

Response

5

0

0

100%

200%

Hydrological Change, %

slide47

10

Response to percent hydrological change

Response

5

0

10

Hydrological metric

0

5

10

Baseline = 1, current = 3; change = 200%

Response

5

0

0

100%

200%

Hydrological Change, %

slide48

10

Response to percent hydrological change

Response

5

0

10

Hydrological metric

0

5

10

Baseline = 3, current = 9; change = 200%

Response

5

0

0

100%

200%

Hydrological Change, %

slide49

10

Response to percent hydrological change

Response

5

0

10

Hydrological metric

0

5

10

Baseline = 3, current = 3; change = 0%

Response

5

0

0

100%

200%

Hydrological Change, %

slide50

10

Response

5

0

10

Hydrological metric

0

5

10

What is response to percent hydrological change?

Response

5

0

0

100%

200%

Hydrological Change, %

slide51

Implications:

  • Depending on response, % change IHA metrics may not be meaningful for comparison to “current” biological metrics
  • Possible solutions:
    • Use residuals of IHA-mean flow regressions for current biological metrics
    • Log-transformed delta of IHA metric
slide52

Scope of potential change

Large flood rise: flow 0.69

slide54

A

D

C

B

pca on size flow adjusted iha metrics
PCA on size (flow) adjusted IHA metrics
  • Groups A and C – one axis
  • Predominance of short-term metrics, related to storm events, flashiness
  • Groups B and D – another axis
  • Predominance of annual flow characteristics
pca on size flow adjusted iha metrics2
PCA on size (flow) adjusted IHA metrics
  • PCA on modeled IHA metrics appears 2-dimensional
  • PCA on gaged data (e.g., McManamay et al. 2012) appears to be more complex/3 (or more) dimensional
slide59

Summary of selected correlation results – fish, flow-adjusted IHA metrics (current)

slide61

Summary of selected correlation results - fish

  • Among the fish metrics:
  • Flow preference traits good – especially fast and slow (mod flow preference weaker)
  • Lithophilic showed good relationships with some IHA metrics
  • Non-native fish, natives, native benthic taxa all fairly responsive
  • Suckers, gamefish not especially responsive
  • Most of these linear relationships modest (not strong)
  • Responsive IHA metrics seem to include:
  • Some ‘annual’ metrics - 7Q10, base flow index, 30-day min, small flood duration, large flood timing
  • Some storm or flashiness metrics – 1-day max, 1-day min, date of minimum
slide62

Summary of selected correlation results – benthos, flow-adjusted IHA metrics (current)

slide64

Summary of selected correlation results - benthos

  • Among the benthic metrics:
  • ET-NoH (pi), scrapers (pi), diptera (pi), intolerant taxa (pt), eveness, HBI, SCI
  • Filterers not very responsive
  • Most of these linear relationships modest (not strong)
  • Responsive IHA metrics seem to include:
  • Some ‘annual’ metrics - 7Q10, 90-day max, 90-day min
  • Some storm or flashiness metrics – # reversals, high flow rise rate, RBI
flow preference fish metrics 1 day minimum flows

Statewide

Flow preference fish metrics, 1-day minimum flows

1-day minimum flow not well adjusted for stream size using mean annual flow (power relationship 0.88) – still residual relationship embedded in scatter plot

flow preference fish metrics 1 day maximum flows

Statewide

Flow preference fish metrics, 1-day maximum flows

1-day maximum flow is reasonably well adjusted for stream size using mean annual flow, but apparent correlation driven by single ‘outlier’

selected fish metrics 30 day maximum flows
Selected fish metrics, 30-day maximum flows

30-day maximum flow is reasonably well adjusted for stream size using mean annual flow, but apparent correlation driven by single ‘outlier’. Range on x-axis excluding that point may be sufficient to evaluate a biological relationship.

selected fish metrics 7q10
Selected fish metrics, 7Q10

7Q10 has residual relationship with stream size. But can start considering how the relationships would be represented (modeled)

selected fish metrics high flow rise rate

Statewide

Selected fish metrics, high flow rise rate

Some unusual patterns that should be understood. The correlation relationship may not be as meaningful as the decline in fast-preference or lithophilic fish with decreasing rise rate in the range shown.

slide72

Statewide

Selected fish metrics, 30-day minimum

May have to evaluate relationship without the ‘outlying’ point.

slide73

Statewide

Selected fish metrics, small flood duration

How to represent the relationship…………….

slide74

Selected benthic metrics, 7Q10

How to represent the relationship…………….

Significant correlations, but under-represented in the higher size-adjusted 7Q10 range

slide75

Selected benthic metrics, high flow rise rate

How to represent the relationship…………….

Significant correlations, but under-represented in the higher size-adjusted IHA range

fish native taxa and 7q10
FishNative taxa and 7Q10
  • Classification affects scale - of both metrics in this case
  • Direction of the response relationship – CP not well predicted by statewide model
  • Differences in apparent relationship between the 2 NCP classes – this relationship may not be important in the NCP-OH class

CP

Statewide

NCP-Atl

NCP-OH

benthos pt noh 1 day maximum
BenthosPT-NoH, 1-day maximum

Statewide

CP

NCP

  • Scale of the IHA metric changes among classes
  • Not so much difference in scale of benthic metric among classes
  • Direction of responses differs among classes
benthos scapers 90 day maximum
BenthosScapers, 90-day maximum

Statewide

CP

NCP

  • Scale of the IHA metric does not change much with classification
  • Same with benthic metric
  • Direction of responses similar among classes
benthos intolerant taxa 30 day maximum
BenthosIntolerant taxa, 30-day maximum

NCP

Statewide

CP

  • Scale of the IHA metric changes with classification
  • Same with benthic metric – scale changed only slightly
  • Direction of responses similar among classes
  • Pattern in coastal plain class unconvincing
considerations regarding classification
Considerations Regarding Classification
  • Classification can influence the direction, strength, potentially the form of response relationships
  • At this point, many are data limited – can’t accommodate separate evaluation of all flow or other classes
  • Even in data-rich classes, flow-ecology responses are often weak/problematic
  • In many cases, separation of coastal plain and non-coastal plain (with separation of Atlantic and Ohio drainages for fish) capture many basic biological and hydrologic differences – advantages of grouping to fewer classes
  • Further separation of flow classes may still be valuable:
    • Example – large flow changes seen in IF streams but not CSI streams
  • Classification by stream size may also be important, though not necessarily possible without more data
slide85

ET-NoH (Ephemeroptera and Trichoptera without Hydropsychidae) (calculated using on genus-level data) and the percent change in 1-day maximum flow

slide86

NCP-Atl

CP

Statewide

NCP-OH

PR2_small

CSI-IF_large

SBF_small

PR1_small

PR1_large

PR2_large

SBF_large

Lithophilic fish and 7Q10

classification and data limitations1
Classification and data limitations
  • Data especially limited for:
  • Spatially small classes (e.g., SBF-1)
  • Under-sampled size classes
  • Headwater streams, generally orders 1 & 2
  • Intermittent streams (e.g. CSI and IF)
why are flow ecology relationships weak
Why are flow-ecology relationships weak?
  • Organisms don’t respond to flow, they respond to:
    • Habitat
    • Physics of flow at organism scale – IHA doesn’t necessarily capture
    • IHA is an indirect measure of these
  • All our hydrology is modeled – no empirical results
    • Organisms depend on percent change – accuracy needs to be high
  • In the data set itself, we may not have scope of (big) hydrologic changes needed to see relationships
    • Have to go look for these
discussion points summary and potential recommendations
Discussion Points – Summary and Potential Recommendations
  • Modeled hydrologic data appears ‘simplified’ compared to gaged data
  • Our PCA had 2 major axes, while McManamay, Orth and colleagues had 3 (or more) based on gaged data
  • Modeled hydrology may not capture enough variation to develop biological relationships
  • Compare model with gage results
  • Need better data representation in areas of known change (urban, dams, withdrawals)
  • This may include gaged as well as biological data
data tools
Data Tools
  • Describe the process used for data gathering, processing, and analysis.
  • Presented as an overview.
biological data
Biological Data

Various Biological Data Sources (NAWQA, Prob Mon, MAIA, MAHA, MARIS, INSTAR, DGIF, TN)

ELOHA db (Access)

(assemble data into consistent format)

Queries and then Form/VBA code to output all bio metrics

Saved as tab-delimited file for use in R (include both bio and hydro station IDs)

Various analyses in R using different scripts

hydrologic data iha metric calculation
Hydrologic Data - IHA Metric Calculation

COVA hydro model

Output available on web on a per site basis

R script to download

Outputs Excel files

IHA v7.0 batch import

R script to assemble for use with IHA

Save as tab delimited for use in R. Some work done in Excel.

Excel VBA macro to update Excel format

R script to assemble results

iha metric data
IHA Metric Data

Most analysis and all plotting done in R. Worked with biological data as well.

IHA metrics

Saved as tab-delimited files for use in R.

Stored in ELOHA db (Access)

Stored in Excel.

Interim analysis performed in Excel.

slide97

Distribution of hydrologic pour points around the Commonwealth of Virginia, with each point designated by flow class membership. Watersheds shown in green were included in analyses

flow classes4
Flow Classes

from McManamay et al. 2011

flow classes5
Flow Classes

from McManamay et al. 2011

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