There is about to be an abrupt step-change in the use of our coastal seas, specifically by the addition of large-scale offshore renewable energy developments to combat climate change. Many trade-offs will need to be weighed up for the future sustainable management of marine ecosystems between renewables and other uses (e.g., fisheries, marine protected areas). Therefore, we need a much greater understanding of how different marine habitats and ecosystems are likely to change with both natural and anthropogenic transformations. This work will present a synthesis of predictive non-mechanistic Bayesian approaches from ecosystem level, through to fine scale mechanistic understanding of foraging success by individual species, to identify accurate and consistent physical (e.g., bottom temperature) and biological (e.g., chlorophyll-a) indicators of habitat and ecosystem change over the last 30 years within a well-studied shallow sea. These combined approaches illuminate the feasibility of integrating knowledge across scales to be able to address the spatio-temporal variability of biophysical indicators to ultimately strengthen predictions of population changes at ecosystem scales across broadly different habitat types. Such knowledge will provide an effective baseline for more strategic and integrated approaches to both monitoring studies and assessing anthropogenic impacts to be used within marine spatial planning considerations.