Abstract
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 review of predictive Bayesian approaches from ecosystem level, through to fine scale mechanistic understanding of foraging success by individual species, to identify consistent physical (e.g., bottom temperature) and biological (e.g., chlorophyll-a) indicators of habitat and ecosystem change over the last 30 years within the North 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.
Highlights
There is clear evidence of climate change within shallow seas, which are being affected by rising temperatures, for example, the well-studied United Kingdom waters north of Scotland and the North Sea have suffered rapid warming with temperatures increasing by up to 0.24◦C per decade (Tinker et al, 2020)
One of the more likely solutions to combat climate change is the introduction of large-scale offshore renewable energy (ORE) developments of 100 s of gigawatts (GW) (IRENA, 2019)
Physical and Biological Indicators either from wind, wave or tides will have cumulative effects within the world’s shallow seas and will influence whole ecosystems. The size of these developments may end up using more than 30% of coastal sea space (Figure 1), and some of the consequences that will most likely follow include physical habitat change (De Dominicis et al, 2018), displacement of fisheries (Kafas et al, 2017) and possible creation of de facto marine protected areas (MPAs) (Raoux et al, 2019)
Summary
There is clear evidence of climate change within shallow seas, which are being affected by rising temperatures, for example, the well-studied United Kingdom waters north of Scotland and the North Sea have suffered rapid warming with temperatures increasing by up to 0.24◦C per decade (Tinker et al, 2020). An indicator is a physical and/or biological ecosystem component, that could be seen described as an environmental predictor, a response, or a pressure but we choose to use the terminology “indicator” to describe ecosystem components that have been found to help predict (“indicate”) habitat and/or ecosystem change over the last 30 years within the North Sea. The indicators are defined based on the literature reviewed for this study scoping the range of variables found to be significant drivers of variation, or direction of change, in either behaviors, distributions and/or population dynamics of the highly mobile top predator marine species from very fine spatial scales through to large ecosystem scales, delivering an understanding of the indicators of habitat and ecosystem change in the North Sea over the last 30 years. We synthesize what we have learned from marine ecosystem modeling approaches with respect to understanding the impacts of climate change and large-scale ORE developments, with the aim to effectively use and communicate their combined outputs to increase confidence in model projections and obtain more holistic knowledge of complex ecological systems. We discuss the implications from our synthesis and propose possible directions for future research and approaches with the aim to better utilize knowledge from existing data sources, survey techniques and modeling formulations to help guide future multitrophic studies to be performed at the most meaningful ecological scales for the types of investigations being proposed
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