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For many marine species and habitats, climate change and overfishing present a double threat. To manage marine resources effectively, it is necessary to adapt management to changes in the physical environment. Simple relationships between environmental conditions and fish abundance have long been used in both fisheries and fishery management. In many cases, however, physical, biological, and human variables feed back on each other. For these systems, associations between variables can change as the system evolves in time. This can obscure relationships between population dynamics and environmental variability, undermining our ability to forecast changes in populations tied to physical processes. Here we present a methodology for identifying physical forcing variables based on nonlinear forecasting and show how the method provides a predictive understanding of the influence of physical forcing on Pacific sardine.ecosystem-based management | physical-biological interactions | state space reconstruction | complex systems | time series analysis E cosystem-based management (EBM) is an essential challenge that places strong demands on our understanding of coupled social-ecological systems. EBM requires an understanding of how human activities such as fishing influence and are influenced by other parts of the ecosystem. This includes accounting for the effects of the physical environment on exploited populations. However, the interactions between ecosystem components can be complex, and unraveling physical-biological interactions remains a challenge. For instance, a retrospective study of 35 exploited and unexploited species in the California Current shows that fishing pressure can amplify the influence of environmental forcing on populations by truncating the age structure (1). This study and others (2-4) demonstrate that the effect of environmental forcing on populations can be contingent on fishing effort, current abundance, and age structure. This raises an important issue: Ecosystem variables are not separate, decomposable forces. Instead, their interactions are state-dependent, meaning that the impact of one variable on another depends on the state of the variables.State-dependent behavior can confound many traditional statistical methods. Witness that valid correlations between physical and biological variables can be difficult to find (5) and can appear and disappear with time (6). In fact, nonlinear systems (systems with state-dependent interactions) can produce mirage correlations: variables that seem positively correlated over one period in time may seem negatively correlated or unrelated over another period (7). A meta-analysis of environment-recruitment relationships in marine populations shows that these correlations hold up poorly when retested with new data (6). Consequently, EBM requires more robust methods for identifying driving variables and understanding their influence on population and community dynamics. Here, we show that methods based on multivariate state space reconstruction (SSR) (8) offer ...

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