Scientific motivation of the CHaMP project: How CHaMP data can be used to answer fish and habitat management questions Chris Jordan – NOAA-Fisheries Brice Semmens – Quantitative Consultants I Carol Volk – South Fork Research Inc. MP and ISEMP staff, collaborators, and project mana
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Chris Jordan – NOAA-Fisheries Brice Semmens – Quantitative Consultants Inc.
Scientific motivation of the CHaMP project: How CHaMP data can be used to answer fish and habitat management questions. CHaMP and ISEMP staff, collaborators, and project managers . Chris Jordan – NOAA-Fisheries Brice Semmens – Quantitative Consultants Inc. - PowerPoint PPT Presentation
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Scientific motivation of the CHaMP project: How CHaMP data can be used to answer fish and
habitat management questions
Chris Jordan – NOAA-FisheriesBrice Semmens – Quantitative Consultants Inc.Carol Volk – South Fork Research Inc.
CHaMP and ISEMP staff, collaborators, and project managers
Scientific motivation of the CHaMP project: How CHaMP data can be used to answer fish and
habitat management questions
Off-site mitigation strategy of the FCRPS Biological Opinion – stream habitat restoration can result in beneficial changes in salmon and steelhead populations.
How to show connection between habitat quantity and quality and freshwater survival?
•Formal, experimental manipulation of stream habitat at fish response variable scale (population or major, closed section of population).
•Mechanistic / process model to project population benefit based on per project change in habitat quality/quantity, habitat status, and fish response to habitat condition.
•Correlation of habitat quality/quantity status and fish status across gradient of actions and confounding covariates.
How to show connection between habitat quantity and quality and freshwater survival?
•Formal, experimental manipulation of stream habitat at fish response variable scale (population or major, closed section of population)
•Mechanistic / process model to project population benefit based on per project change in habitat quality/quantity, habitat status, and fish response to habitat condition.
•Correlation of habitat quality/quantity status and fish status across gradient of actions and confounding covariates.
•All need Habitat Quality and Quantity data• Indicators of habitat quality• Indicators of habitat quantity• Indicators of change
How to show connection between habitat quantity and quality and freshwater survival?
•Formal, experimental manipulation of stream habitat at fish response variable scale (population or major, closed section of population)
•Mechanistic / process model to project population benefit based on per project change in habitat quality/quantity, habitat status, and, fish response to habitat condition.
•Correlation of habitat quality/quantity status and fish status across gradient of actions and confounding covariates.
•All need Habitat Quality and Quantity data• Indicators of habitat quality• Indicators of habitat quantity• Indicators of change
Restoration applied 1 = YAT or year after treatment
Entiat IMW Experimental Design
How to show connection between habitat quantity and quality and freshwater survival?
•Formal, experimental manipulation of stream habitat at fish response variable scale (population or major, closed section of population)
•Mechanistic / process model to project population benefit based on per project change in habitat quality/quantity, habitat status, and, fish response to habitat condition.
•Correlation of habitat quality/quantity status and fish status across gradient of actions and confounding covariates.
•All need Habitat Quality and Quantity data• Indicators of habitat quality• Indicators of habitat quantity• Indicators of change
ISEMP Watershed Production Model
Habitat Quantity Habitat Quality
Channel Characteristics by Land Use Type: A. Relating habitat availability to capacity,
(ci) 13 and 14; B. Calibration using empirical and GIS data,
19-23; C. Hypothesis testing, 29 and 30 (cross-
sectional), 34-38 (pre/post).
Survival/Productivity by Life History Stage: A. Relating habitat quality to
survival/productivity, (pi) 15 and 16; B. Calibration using empirical estimates of
survival/productivity, 24-28; C. Hypothesis testing, 31 and 32 (cross-
sectional), 34-38 (pre/post).
Fry 1-3, (N3,t+1)
Parr 1-3, (N4,t+1)
Presmolt 1-3, (N5,t+1)
Smolt 1-3, (N6,t+2)
Egg 1-3, (N2,t)
Ocean Immature
Adult 8-10, (ot+x) 1-3, (N6,t+1)
Spawner 1-3, (N1,t)
Mature (Yes) 8-10, (ot+x)
Harvest (T) 11, (ot+x)
Survival (5-7), (Ot+x)
Mature (No)
Pool
RiffleAvailable Habitat: 23.4 kmLWD per km: 83.7 m3 Fine Sediment: 18.3 %D50: 53.5 mm
Bohannon Creek
n = 2
Pool
Riffle
Glide
Available Habitat: 86.2 kmLWD per km: 24.7 m3 Fine Sediment: 26.6 %D50: 22.3 mm
Kenny Creek
n = 3
Pool
Glide
Riffle Available Habitat: 64.0 kmLWD per km: 70.7 m3 Fine Sediment: 34.2 %D50: 29.3 mm
Canyon Creek
n = 12
Pool
Glide
Riffle
Available Habitat: 103.0 kmLWD per km: 45.9 m3 Fine Sediment: 20.8 %D50: 44.9 mm
Big Timber
n = 11
How to show connection between habitat quantity and quality and freshwater survival?
•Formal, experimental manipulation of stream habitat at fish response variable scale (population or major, closed section of population)
•Mechanistic / process model to project population benefit based on per project change in habitat quality/quantity, habitat status, and, fish response to habitat condition.
•Correlation of habitat quality/quantity status and fish status across gradient of actions and confounding covariates.
•All need Habitat Quality and Quantity data• Indicators of habitat quality• Indicators of habitat quantity• Indicators of change
Monitoring must detect spatial and temporal patterns in habitat quality and quantity
What If We Only Use CHaMP Indicators (Subset Wenachee ISEMP data)?
• Embeddedness of fast water cobble • Pool Frequency • Residual pool volume• LWD volume• Fish cover• Channel unit volume• Riffle particle size • Densiometer
2009: Within Site Variability (CHaMP Metrics Only)
• In 2009, all sites were surveyed multiple times (mostly 3 times) to get at observation error
wenachee repeats
NormaliseResemblance: D1 Euclidean distance
2D Stress: 0.13
V(Site)86%
V(Res)14%
Error Explained
How Much Error Due to Surveys?
PoolFreq
PoolSA
ResidPoolVol
Embed
CoarseGravel
Densiometer
LWD
FishCover
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
SiteResid
What About Trends?• Consider only the CHaMP indicators
• Interested in exploring linear trends
• Account for random effects of watershed, ownership, Strahler order, and nested effects of sites within these factors
• Use maximum likelihood and General Linear Mixed Models (GLMMs)
• Evaluate model parsimony via AIC
Fish cover• Best AIC: FC_Total~ Year + (1|ownership)+ (1|site)
Federal Private
Large Woody Debris• Best AIC: LWD ~ (1 | Strahler) + (1 | site) + (1 | ownership)
1 2 3
4 5
Relation to CHaMP?
• We expect reductions in observation error (residual error) associated with stream morphology when using total station to map stream features
• Demonstrates that coordinated monitoring yields a constellation of habitat data that, in concert, are powerful enough to detect differences among sites and changes though time at multiple levels of spatial organization
Monitoring must detect spatial and temporal patterns in habitat quality and quantity
• To evaluate the status and trends in salmon tributary habitat across the Columbia River basin, a basin-scale, consistent monitoring approach is required.
• To evaluate the effectiveness of habitat restoration strategies in terms of fish population processes, a basin-scale, consistent monitoring approach is required.