Fish predation on a landscape scale. Issue 6 (21st June 2020)

• Record Type: Journal Article
• Authors: Michel, Cyril J.; Henderson, Mark J.; Loomis, Christopher M.; Smith, Joseph M.; Demetras, Nicholas J.; Iglesias, Ilysa S.; Lehman, Brendan M.; Huff, David D.
• Is Part Of: Ecosphere. Volume 11:Issue 6(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-06-21
• Subjects: landscape ecology -- non‐native species -- predator–prey -- research design -- salmon -- trophic interaction; Ecology -- Periodicals; Ecology; Periodicals; 577.05
• DOI: 10.1002/ecs2.3168
• Languages: English
• ISSNs: 2150-8925

Abstract

Abstract: Predator–prey dynamics can have landscape‐level impacts on ecosystems, and yet, spatial patterns and environmental predictors of predator–prey dynamics are often investigated at discrete locations, limiting our understanding of the broader impacts. At these broader scales, landscapes often contain multiple complex and heterogeneous habitats, requiring a spatially representative sampling design. This challenge is especially pronounced in California's Sacramento–San Joaquin River Delta, where managers require information on the landscape‐scale impacts of non‐native fish predators on multiple imperiled native prey fish populations. We quantified relative predation risk in the southern half of the Delta (South Delta) in 2017 using floating baited tethers that record the exact time and location of predation events. We selected 20 study sites using a generalized random tessellation stratified survey design, which allowed us to infer relationships between key environmental covariates and predation across a broader spatial scale than previous studies. Covariates included distance‐to‐nearest predators, water temperature, turbidity, depth, bottom slope, bottom roughness, water velocity, and distance‐to‐nearest riverbank and nearest aquatic vegetation bed. Model selection determined the covariates that best predicted relative predation risk: water temperature, time of day, mean predator distance, and river bottom roughness. Using this model, we estimated predation risk for the South Delta landscape at a 1‐day and 1‐km resolution. This effort identified hot spots of predation risk and allowed us to generate predicted survival for migrating fish transiting the South Delta. This methodology can be applied to other systems to evaluate spatio‐temporal dynamics in predation risk, and their biotic and abiotic predictors.