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.

中文翻译：

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评估沿河口梯度的远洋食物网动态的自上而下、自下而上和环境驱动因素

识别食物网内的关键生物和非生物驱动因素对于了解生态系统中物种丰度的变化非常重要，特别是在相互作用强度可能存在很大差异的生态交错带中。利用结构方程模型 (SEM) 和旧金山河口四十年的综合数据，我们研究了自上而下、自下而上和环境驱动因素对沿河口盐度梯度的中上层食物网多个营养级的相对影响以及年度和月度时间分辨率。我们发现，相互作用在河口梯度上有所不同，并且不同相互作用的可检测性取决于时间尺度。例如，对于浮游动物和河口鱼类，自下而上的效应似乎在淡水上游区域更强，而自上而下的效应在咸水下游区域更强。一些关系（例如，浮游植物对浮游动物的自下而上的影响）主要在年度时间尺度上观察到，而其他关系（例如，温度影响）仅在每月时间尺度上观察到。我们还发现，环境驱动因素的净效应类似于或大于所有食物网组成部分的自下而上和自上而下的效应。这些发现可以帮助确定管理行动可以针对哪些营养水平或环境因素对河口饲料鱼产生最大的影响，以及可以观察到反应的空间和时间尺度。更广泛地说，这项研究强调了环境梯度如何构建社区互动，以及如何利用长期数据集来产生跨多个尺度的见解。