A landscape perspective of stream food webs: Exploring cumulative effects and defining biotic thresholds

Objectives To better understand how stream food webs are embedded in their landscapes To better understand how stream food webs are embedded in their landscapes To quantify stream food webs across the watershed To quantify stream food webs across the watershed Explore the effects of watershed characteristics on stream food webs and energy fluxes at different spatial scales Explore the effects of watershed characteristics on stream food webs and energy fluxes at different spatial scales To identify thresholds below which critical habitat coupling mechanisms are disabled and stream food webs become compromised To identify thresholds below which critical habitat coupling mechanisms are disabled and stream food webs become compromised

Experimental Approach Mica Creek Mica Creek Beaver Creek – North Fork of Clearwater Beaver Creek – North Fork of Clearwater East Fork of the Potlatch East Fork of the Potlatch Systematic sampling Systematic sampling n=7 per watershed n=7 per watershed Nested experimental design incorporating three spatial scales: upper watershed, mid + upper watershed, and entire watershed Nested experimental design incorporating three spatial scales: upper watershed, mid + upper watershed, and entire watershed

GIS/Remote Sensing Harvest blocks’ extent and age Percent wetlands within watershed Road density Temperature and precipitation Mean stand age of forested area % conifer/% deciduous Riparian buffer extent and age Width of riparian zone Stream order Sediment loading Drainage area Elevation Latitude and longitude

Physical Field Data Stream slope Stream slope Substrate characteristics Substrate characteristics Buffer dominant tree species Buffer dominant tree species Stream temperature Stream temperature Age of buffer dominant tree species Age of buffer dominant tree species Rapid geomorphic condition assessment Rapid geomorphic condition assessment Rapid habitat assessment (RBP/RHA) Rapid habitat assessment (RBP/RHA) Flow mapping Flow mapping Bankfull width Bankfull width DO, pH, Conductivity DO, pH, Conductivity Bankfull depth Bankfull depth Buffer vegetation layers Buffer vegetation layers Channel canopy Channel canopy Buffer % conifers/% deciduous Buffer % conifers/% deciduous LWD/Leaf cover LWD/Leaf cover

Biological Data Collection of biota will serve to: Collection of biota will serve to: characterize riverine faunal assemblages characterize riverine faunal assemblages model stream food webs model stream food webs trace habitat-coupling linkages trace habitat-coupling linkages To accomplish this, we will be surveying fish assemblages, investigating focal species, and sampling potential food sources to isolate isotopic signatures. To accomplish this, we will be surveying fish assemblages, investigating focal species, and sampling potential food sources to isolate isotopic signatures.

Macroinvertebrates Across-community comparisons Across-community comparisons Food web analysis Food web analysis Stable isotopes Stable isotopes Terrestrial invertebrate fall into the stream at each reach will be also be quantified Terrestrial invertebrate fall into the stream at each reach will be also be quantified Quantitative analysis of benthic Quantitative analysis of benthic CPOM storage and epilithic algae

Fish Across-community comparisons Across-community comparisons Food web analysis using brook trout Food web analysis using brook trout Stable isotope and gut-content analyses Stable isotope and gut-content analyses

Birds American Dipper American Dipper Tree Swallow? Tree Swallow? Blood and feather samples for stable isotope analysis Blood and feather samples for stable isotope analysis We will also collect various riparian and terrestrial insects, spiders, and vegetative matter that are potential food sources for the focal bird species We will also collect various riparian and terrestrial insects, spiders, and vegetative matter that are potential food sources for the focal bird species These will be processed to determine C and N isotopic signatures These will be processed to determine C and N isotopic signatures

Analysis A variety of statistical methods will be used to address our objectives. A variety of statistical methods will be used to address our objectives. Contrasts in aquatic fish and bird assemblages across the three scales will be explored using non-metric multidimensional scaling (NMS). Contrasts in aquatic fish and bird assemblages across the three scales will be explored using non-metric multidimensional scaling (NMS). Comparisons of species richness, abundance, and biomass between the three scales will be made using analysis of variance (ANOVA). Comparisons of species richness, abundance, and biomass between the three scales will be made using analysis of variance (ANOVA). Habitat and food resource data will be compared among reach type and stream order using multivariate analysis of variance (MANOVA), followed by univariate ANOVA’s when significant differences are detected. Habitat and food resource data will be compared among reach type and stream order using multivariate analysis of variance (MANOVA), followed by univariate ANOVA’s when significant differences are detected. To quantify the effects of watershed characteristics, stream habitat, and food resources on stream food webs at different spatial scales; we will create a series of models (based on information theoretic approach), one set for each increasing spatial scale. These models will then be compared across spatial scales to determine if the same factors exert similar cumulative influences on stream food webs across spatial scales. First, we will create a set of a priori models, having selected independent. To quantify the effects of watershed characteristics, stream habitat, and food resources on stream food webs at different spatial scales; we will create a series of models (based on information theoretic approach), one set for each increasing spatial scale. These models will then be compared across spatial scales to determine if the same factors exert similar cumulative influences on stream food webs across spatial scales. First, we will create a set of a priori models, having selected independent. Alternatively, we may use principal components analysis (PCA) to extract gradients in watershed characteristics, stream habitat, and food resources, and follow this with multiple regression. Alternatively, we may use principal components analysis (PCA) to extract gradients in watershed characteristics, stream habitat, and food resources, and follow this with multiple regression. Finally, we will use changepoint analysis to look for physical conditions that effect threshold responses in stream food web structure across the watershed. Finally, we will use changepoint analysis to look for physical conditions that effect threshold responses in stream food web structure across the watershed.

Changepoint Analysis Predictor X Response Y mean Courtesy of Naomi Detenbeck, US EPA Mid-Continent Ecology Division