Details

Autor(en) / Beteiligte

• Rogers, Tanya L.
• Bashevkin, Samuel M.
• Burdi, Christina E.
• Colombano, Denise D.
• Dudley, Peter N.
• Mahardja, Brian
• Mitchell, Lara
• Perry, Sarah
• Saffarinia, Parsa

Titel

Evaluating top‐down, bottom‐up, and environmental drivers of pelagic food web dynamics along an estuarine gradient

Ist Teil von

• Ecology (Durham), 2024-04, Vol.105 (4), p.e4274-n/a

Ort / Verlag

Hoboken, USA: John Wiley & Sons, Inc

Erscheinungsjahr

2024

Quelle

Access via Wiley Online Library

Beschreibungen/Notizen

• Identification of the key biotic and abiotic drivers within food webs is important for understanding species abundance changes in ecosystems, particularly across ecotones where there may be strong variation in interaction strengths. Using structural equation models (SEMs) and four decades of integrated data from the San Francisco Estuary, we investigated the relative effects of top‐down, bottom‐up, and environmental drivers on multiple trophic levels of the pelagic food web along an estuarine salinity gradient and at both annual and monthly temporal resolutions. We found that interactions varied across the estuarine gradient and that the detectability of different interactions depended on timescale. For example, for zooplankton and estuarine fishes, bottom‐up effects appeared to be stronger in the freshwater upstream regions, while top‐down effects were stronger in the brackish downstream regions. Some relationships (e.g., bottom‐up effects of phytoplankton on zooplankton) were seen primarily at annual timescales, whereas others (e.g., temperature effects) were only observed at monthly timescales. We also found that the net effect of environmental drivers was similar to or greater than bottom‐up and top‐down effects for all food web components. These findings can help identify which trophic levels or environmental factors could be targeted by management actions to have the greatest impact on estuarine forage fishes and the spatial and temporal scale at which responses might be observed. More broadly, this study highlights how environmental gradients can structure community interactions and how long‐term data sets can be leveraged to generate insights across multiple scales.