Here we describe, the longest microbial time-series analyzed to date using high-resolution 16S rRNA tag pyrosequencing of samples taken monthly over 6 years at a temperate marine coastal site off Plymouth, UK. Data treatment effected the estimation of community richness over a 6-year period, whereby 8794 operational taxonomic units (OTUs) were identified using single-linkage preclustering and 21 130 OTUs were identified by denoising the data. The Alphaproteobacteria were the most abundant Class, and the most frequently recorded OTUs were members of the Rickettsiales (SAR 11) and Rhodobacteriales. This near-surface ocean bacterial community showed strong repeatable seasonal patterns, which were defined by winter peaks in diversity across all years. Environmental variables explained far more variation in seasonally predictable bacteria than did data on protists or metazoan biomass. Change in day length alone explains >65% of the variance in community diversity. The results suggested that seasonal changes in environmental variables are more important than trophic interactions. Interestingly, microbial association network analysis showed that correlations in abundance were stronger within bacterial taxa rather than between bacteria and eukaryotes, or between bacteria and environmental variables.

Marine spatial planning that addresses ocean climate-driven change ('climate-smart MSP') is a global aspiration to support economic growth, food security and ecosystem sustainability. Ocean climate change ('CC') modelling may become a key decision-support tool for MSP, but traditional modelling analysis and communication challenges prevent their broad uptake. We employed MSP-specific ocean climate modelling analyses to inform a real-life MSP process; addressing how nature conservation and fisheries could be adapted to CC. We found that the currently planned distribution of these activities may become unsustainable during the policy's implementation due to CC, leading to a shortfall in its sustainability and blue growth targets. Significant, climate-driven ecosystem-level shifts in ocean components underpinning designated sites and fishing activity were estimated, reflecting different magnitudes of shifts in benthic versus pelagic, and inshore versus offshore habitats. Supporting adaptation, we then identified: CC refugia (areas where the ecosystem remains within the boundaries of its present state); CC hotspots (where climate drives the ecosystem towards a new state, inconsistent with each sectors' present use distribution); and for the first time, identified bright spots (areas where oceanographic processes drive range expansion opportunities that may support sustainable growth in the medium term). We thus create the means to: identify where sector-relevant ecosystem change is attributable to CC; incorporate resilient delivery of conservation and sustainable ecosystem management aims into MSP; and to harness opportunities for blue growth where they exist. Capturing CC bright spots alongside refugia within protected areas may present important opportunities to meet sustainability targets while helping support the fishing sector in a changing climate. By capitalizing on the natural distribution of climate resilience within ocean ecosystems, such climate-adaptive spatial management strategies could be seen as nature-based solutions to limit the impact of CC on ocean ecosystems and dependent blue economy sectors, paving the way for climate-smart MSP.