Wavelet analysis is a powerful tool that is already in use throughout science and engineering. The versatility and attractiveness of the wavelet approach lie in its decomposition properties, principally its time-scale localization. It is especially relevant to the analysis of non-stationary systems, i.e., systems with short-lived transient components, like those observed in ecological systems. Here, we review the basic properties of the wavelet approach for time-series analysis from an ecological perspective. Wavelet decomposition offers several advantages that are discussed in this paper and illustrated by appropriate synthetic and ecological examples. Wavelet analysis is notably free from the assumption of stationarity that makes most methods unsuitable for many ecological time series. Wavelet analysis also permits analysis of the relationships between two signals, and it is especially appropriate for following gradual change in forcing by exogenous variables.

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The health of the ocean, central to human well-being, has now reached a critical point. Most fish stocks are overexploited, climate change and increased dissolved carbon dioxide are changing ocean chemistry and disrupting species throughout food webs, and the fundamental capacity of the ocean to regulate the climate has been altered. However, key technical, organizational, and conceptual scientific barriers have prevented the identification of policy levers for sustainability and transformative action. Here, we recommend key strategies to address these challenges, including (1) stronger integration of sciences and (2) ocean-observing systems, (3) improved science-policy interfaces, (4) new partnerships supported by (5) a new ocean-climate finance system, and (6) improved ocean literacy and education to modify social norms and behaviors. Adopting these strategies could help establish ocean science as a key foundation of broader sustainability transformations.