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 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

Geographic Upper Columbia Wenatchee/Entiat Mid Columbia John Day Snake Salmon ISEMP Experimental Watersheds Topical Status/Trends Population / Habitat Effectiveness Monitoring IMWs and extensive

Bridge Creek IMW Murderers Creek Bear Creek Gable Creek Treatment Control 10 km

Entiat River IMW

Lemhi River IMW

Restoration applied 1 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

Pool Riffle Available Habitat: 23.4 km LWD per km: 83.7 m 3 Fine Sediment: 18.3 % D 50 : 53.5 mm Bohannon Creek n = 2 Pool Riffle Glide Available Habitat: 86.2 km LWD per km: 24.7 m 3 Fine Sediment: 26.6 % D 50 : 22.3 mm Kenny Creek n = 3 Pool Glide Riffle Available Habitat: 64.0 km LWD per km: 70.7 m 3 Fine Sediment: 34.2 % D 50 : 29.3 mm Canyon Creek n = 12 Pool Glide Riffle Available Habitat: km LWD per km: 45.9 m 3 Fine Sediment: 20.8 % D 50 : 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 within and across watersheds Average Alkalinity Average Conductivity Average pH Growth Potential Percent Below Summer T° Threshold Percent Above Winter T° Threshold Velocity Heterogeneity Embeddedness of Fastwater Cobble Pool Frequency Channel Complexity Channel Score Residual Pool Volume Subsurface Fines Total Drift Biomass Bank Angle LWD Volume Fish Cover Channel Unit Volume Channel Unit Complexity Riffle Particle Size Riparian Structure Solar Input Survey design Within watershed patterns Between watershed patterns ChaMP Habitat Quality and Quantity Indicators

Wind River GRTS Master Sample

Wind River CHaMP Survey Design

Monitoring must detect spatial and temporal patterns in habitat quality and quantity within and across watersheds Average Alkalinity Average Conductivity Average pH Growth Potential Percent Below Summer T° Threshold Percent Above Winter T° Threshold Velocity Heterogeneity Embeddedness of Fastwater Cobble Pool Frequency Channel Complexity Channel Score Residual Pool Volume Subsurface Fines Total Drift Biomass Bank Angle LWD Volume Fish Cover Channel Unit Volume Channel Unit Complexity Riffle Particle Size Riparian Structure Solar Input Survey design Within watershed patterns Between watershed patterns ChaMP Habitat Quality and Quantity Indicators

Analysis of habitat monitoring data Used 30 habitat metrics from ISEMP monitoring program in Wenachee Sub-basin 25 annual panel sites, visited Included stream morphology, riparian veg., woody debris, fish cover, pool features, sediment features, bank stability Transformed and normalized Status -- Use PERMANOVA to partition variance in multivariate habitat data Trends -- Fit GLMMs to evaluate evidence of trends in habitat indicators through time across hierarchies of site organization

Ordination By Ownership

Ordination By Strahler

Ordination By Watershed

Ordination By Year

PERMANOVA With Strahler Source df SS MSPseudo-FP(perm) Year Strahler Ownership YearxStrahler YearxOwnership StrahlerxOwnership SiteName(StrahlerxOwnership) YearxStrahlerxOwnership YearxSiteName(StrahlerxOwnership) Res Total

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

Ordination by Strahler wenachee repeats PC P C 2 Strahler FC_Total TotalWoodVol_n_SiteLengthr AvgOfResidualPoolDepthr AvgOfDensiometerReadingr AvgOfStationEmbeddednessr PercentCoarseGravelr PoolsPerKmr SA_pools

PERMANOVA With Strahler Source df SS MSPseudo-FP(perm) Year Strahler Ownership YearxStrahler YearxOwnership StrahlerxOwnership SiteName(StrahlerxOwnership) YearxStrahlerxOwnership YearxSiteName(StrahlerxOwnership) Res Total

2009: Within Site Variability (CHaMP Metrics Only) In 2009, all sites were surveyed multiple times (mostly 3 times) to get at observation error Error Explained

How Much Error Due to Surveys?

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) FederalPrivate

Large Woody Debris Best AIC: LWD ~ (1 | Strahler) + (1 | site) + (1 | ownership)

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 within and across watersheds ChaMP Habitat Quality and Quantity Indicators Average Alkalinity Average Conductivity Average pH Growth Potential Percent Below Summer T° Threshold Percent Above Winter T° Threshold Velocity Heterogeneity Embeddedness of Fastwater Cobble Pool Frequency Channel Complexity Channel Score Residual Pool Volume Subsurface Fines Total Drift Biomass Bank Angle LWD Volume Fish Cover Channel Unit Volume Channel Unit Complexity Riffle Particle Size Riparian Structure Solar Input Survey design Within watershed patterns Between watershed patterns

Geomorphic & climate based watershed classification

Human disturbance based watershed classification

CHaMP watersheds relative to ICRB steelhead and sp/su Chinook population

Take Home Message 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.