Designated using a Statutory Instrument in 2008, Lyme Bay marine‐protected area (MPA) is the UK's first and largest example of an ambitious, whole‐site approach to management, to recover and protect reef biodiversity. The whole‐site approach applies consistent management, in this case excluding bottom towed fishing, across the full 206 km² extent of the MPA, thus protecting a mosaic of reef‐associated habitats from regular damage, while still allowing less destructive fishing methods, such as static gear, rod and line, and diving. To assess the effectiveness of this management strategy for mobile taxa and the sustainability for those taxa that continue to be targeted, Exploited and Non‐Exploited species' populations were compared inside the MPA, relative to open control sites spanning 11 of the 12 years of designation. baited remote underwater video systems (BRUVs) were deployed annually to assess mobile benthic and demersal fauna. Overall, the number of taxa significantly increased in the MPA relative to the open controls while total abundance increased in both treatments. Exploited fish showed increases in number of taxa (430%) and total abundance (370%) inside the MPA over 11 years. Likewise, but to a lesser degree in the open controls, number of taxa of commercially Exploited fish increased over time, potentially showing ‘spillover’ effects from the MPA. Non‐Exploited fish did not show such changes. Regardless of constituting the majority of the fishery value, highly valuable Exploited invertebrates showed no significant changes over time. Synthesis and applications. The Lyme Bay marine‐protected area shows importance of protecting a whole site, comprising mosaics of different benthic habitats, through protection of sessile organisms that contribute to essential fish habitats. This Ecosystem Approach to Fisheries Management can benefit and maintain sustainable fisheries and species of conservation importance.

Aim: Ecosystem Approach to Fisheries Management has highlighted the importance of studying ecosystem functions and services, and the biological traits that drive them. Yet, ecosystem services and the associated benefits that they provide are rarely the motive for creating marine protected area (MPA). Therefore, many MPA monitoring projects do not explicitly study these functions and services or the underlying biological traits linked to them.Location: Lyme Bay MPA, located in the SW of England, was established in 2008 to protect the reef biodiversity across a 206‐km² area, which includes rocky reef habitats, pebbly sand and soft muddy sediments. Mobile demersal fishing was excluded across the whole site to allow the recovery of the reef habitats.Methods: Using a combination of towed underwater video and Baited Remote Underwater Video Systems changes in diversity (taxonomic and trait), and traits affected by mobile demersal fishing were assessed in Lyme Bay MPA over 10 years.Results: There was a consistent increase in the number of taxa and the trait diversity they provide within the MPA as well as an increase in functional redundancy, which may increase community resilience to perturbations. Outside of the MPA there was an increase in the abundance of mobile species, while the MPA showed an increase in filter feeders.Main conclusions: The MPA showed a trend towards more diverse and potentially resilient rocky reef habitats. This study constitutes a novel MPA assessment using multiple sampling methods to encompass a wide range of taxa. It also reinforces the importance of effective MPA monitoring, which has demonstrated changes in trait diversity and trait composition driven by changes in taxonomic diversity.

Seagrasses are marine flowering plants that form extensive meadows from the inter-tidal zone up to ~50 m depth. As biological and ecological Essential Biodiversity Variables, seagrass cover and composition provide a wide range of ecosystem services. Inter-tidal seagrass meadows provide services to many ecosystems, so monitoring their occurrence, extent, condition and diversity can be used to indicate the biodiversity and health of local ecosystems. Current global estimates of seagrass extent and recent reviews either do not mention inter-tidal seagrasses and their seasonal variation, or combine them with sub-tidal seagrasses. Here, using high-spatial and high-temporal resolution satellite data (Sentinel-2), we demonstrate a method for consistently mapping inter-tidal seagrass meadows and their phenology at a continental scale. We were able to highlight varying seasonal patterns that are observable across a 23° latitudinal range. Timings of peaks in seagrass extent varied by up to 5 months, rather than the previously assumed marginal to non-existent variation in peak timing. These results will aid management by providing high-resolution spatio-temporal monitoring data to better inform seagrass conservation and restoration. They also highlight the high level of seasonal variability in inter-tidal seagrass, meaning combination with sub-tidal seagrass for global assessments will likely produce misleading or incorrect estimates.

Taking into account trophic relationships in seagrass meadows is crucial to explain and predict seagrass temporal trajectories, as well as for implementing and evaluating seagrass conservation policies. However, this type of interaction has been rarely investigated over the long term and at the scale of the whole seagrass habitat. In this work, reciprocal links between an intertidal seagrass species, Zostera noltei, and a herbivorous bird feeding on this seagrass species, the migratory goose Branta bernicla bernicla, were investigated using an original combination of long‐term Earth Observation (EO) and bird census data. Seagrass Essential Biodiversity Variables (EBVs) such as seagrass abundance and phenology were measured from 1985 to 2020 using high‐resolution satellite remote sensing over Bourgneuf Bay (France), and cross‐analysed with in situ measurements of bird population size during the goose wintering season. Our results showed a mutual relationship between seagrass and Brent geese over the four last decades, suggesting that the relationship between the two species extends beyond a simple grass—herbivore consumptive effect. We provided evidence of two types of interactions: (i) a bottom‐up control where the late‐summer seagrass abundance drives the wintering population of herbivorous geese and (ii) an indirect top‐down effect of Brent goose on seagrass habitat, where seagrass development is positively influenced by the bird population during the previous wintering season. Such a mutualistic relationship has strong implications for biodiversity conservation because protecting one species is beneficial to the other one, as demonstrated here by the positive trajectories observed from 1985 to 2020 in both seagrass and bird populations. Importantly, we also demonstrated here that exploring the synergy between EO and in situ bird data can benefit seagrass ecology and ecosystem management.

Coastal areas support seagrass meadows, which offer crucial ecosystem services, including erosion control and carbon sequestration. However, these areas are increasingly impacted by human activities, leading to habitat fragmentation and seagrass decline. In situ surveys, traditionally performed to monitor these ecosystems, face limitations on temporal and spatial coverage, particularly in intertidal zones, prompting the addition of satellite data within monitoring programs. Yet, satellite remote sensing can be limited by too coarse spatial and/or spectral resolutions, making it difficult to discriminate seagrass from other macrophytes in highly heterogeneous meadows. Drone (unmanned aerial vehicle—UAV) images at a very high spatial resolution offer a promising solution to address challenges related to spatial heterogeneity and the intrapixel mixture. This study focuses on using drone acquisitions with a ten spectral band sensor similar to that onboard Sentinel-2 for mapping intertidal macrophytes at low tide (i.e., during a period of emersion) and effectively discriminating between seagrass and green macroalgae. Nine drone flights were conducted at two different altitudes (12 m and 120 m) across heterogeneous intertidal European habitats in France and Portugal, providing multispectral reflectance observation at very high spatial resolution (8 mm and 80 mm, respectively). Taking advantage of their extremely high spatial resolution, the low altitude flights were used to train a Neural Network classifier to discriminate five taxonomic classes of intertidal vegetation: Magnoliopsida (Seagrass), Chlorophyceae (Green macroalgae), Phaeophyceae (Brown algae), Rhodophyceae (Red macroalgae), and benthic Bacillariophyceae (Benthic diatoms), and validated using concomitant field measurements. Classification of drone imagery resulted in an overall accuracy of 94% across all sites and images, covering a total area of 467,000 m². The model exhibited an accuracy of 96.4% in identifying seagrass. In particular, seagrass and green algae can be discriminated. The very high spatial resolution of the drone data made it possible to assess the influence of spatial resolution on the classification outputs, showing a limited loss in seagrass detection up to about 10 m. Altogether, our findings suggest that the MultiSpectral Instrument (MSI) onboard Sentinel-2 offers a relevant trade-off between its spatial and spectral resolution, thus offering promising perspectives for satellite remote sensing of intertidal biodiversity over larger scales.