A key stage underpinning marine spatial planning (MSP) involves mapping the spatial distribution of ecological processes and biological features as well the social and economic interests of different user groups. One sector, merchant shipping (vessels that transport cargo or passengers), however, is often poorly represented in MSP due to a perceived lack of fine‐scale spatially explicit data to support decision‐making processes. Here, using the Republic of Congo as an example, we show how publicly accessible satellite‐derived automatic identification system (S‐AIS) data can address gaps in ocean observation data for shipping at a national scale. We also demonstrate how fine‐scale (0.05 km2 resolution) spatial data layers derived from S‐AIS (intensity, occupancy) can be used to generate maps of vessel pressure to provide an indication of patterns of impact on the marine environment and potential for conflict with other ocean user‐groups. We reveal that passenger vessels, offshore service vessels, bulk carrier and cargo vessels and tankers account for 93.7% of all vessels and vessel traffic annually, and that these sectors operate in a combined area equivalent to 92% of Congo's exclusive economic zone—far exceeding the areas allocated for other user groups (conservation, fisheries and petrochemicals). We also show that the shallow coastal waters and habitats of the continental shelf are subject to more persistent pressure associated with shipping, and that the potential for conflict among user groups is likely to be greater with fisheries, whose zones are subject to the highest vessel pressure scores than with conservation or petrochemical sectors. Synthesis and applications. Shipping dominates ocean use, and so excluding this sector from decision‐making could lead to increased conflict among user groups, poor compliance and negative environmental impacts. This study demonstrates how satellite‐derived Automatic Identification System data can provide a comprehensive mechanism to fill gaps in ocean observation data and visualise patterns of vessel behaviour and potential threats to better support marine spatial planning at national scales.

Globally, marine turtles are considered threatened throughout their range, and therefore conservation practitioners are increasingly investing resources in marine protected areas to protect key life history stages and critical habitats, including foraging grounds, nesting beaches and inter-nesting areas. Empirical data on the distribution of these habitats and/or the spatial ecology and behaviour of individuals of many marine turtle populations are often lacking, undermining conservation efforts, particularly along the Atlantic coast of Africa. Here we contribute to the knowledge base in this region by describing patterns of habitat use for nine green turtles Chelonia mydas tagged with satellite platform transmitter terminals at a foraging ground in Loango Bay, Republic of the Congo, one of only a few documented mainland foraging grounds for marine turtles in Central Africa. Analyses of these data revealed that core areas of habitat use and occupancy for a wide range of size/age classes were restricted to shallow waters adjacent to Pointe Indienne in Loango Bay, with most individuals showing periods of high fidelity to this area. These data are timely given the Congolese government recently announced its intention to create a marine conservation zone to protect marine turtles in Loango Bay. Despite the small sample size of this study, these data exemplify the need for comprehensive strategies that span national jurisdictions, as we provide the first documented evidence of linkages between green turtle foraging sites in Central Africa (Loango Bay, Republic of the Congo) and Southern Africa (Mussulo Bay, Angola).

Impact statementIUU fishing threatens efforts to manage fisheries and conserve marine biodiversity and hinders progress toward sustainable development goals.

Monitoring how populations respond to sustained conservation measures is essential to detect changes in their population status and determine the effectiveness of any interventions. In the case of sea turtles, their populations are difficult to assess because of their complicated life histories. Ground-derived clutch counts are most often used as an index of population size for sea turtles; however, data are often incomplete with varying sampling intensity within and among sites and seasons. To address these issues, we: (1) develop a Bayesian statistical modelling framework that can be used to account for sampling uncertainties in a robust probabilistic manner within a given site and season; and (2) apply this to a previously unpublished long-term sea turtle dataset (n = 17 years) collated for the Republic of the Congo, which hosts two sympatrically nesting species of sea turtle (leatherback turtle [Dermochelys coriacea] and olive ridley turtle [Lepidochelys olivacea]). The results of this analysis suggest that leatherback turtle nesting levels dropped initially and then settled into quasi-cyclical levels of interannual variability, with an average of 573 (mean, 95% prediction interval: 554–626) clutches laid annually between 2012 and 2017. In contrast, nesting abundance for olive ridley turtles has increased more recently, with an average of 1,087 (mean, 95% prediction interval: 1,057–1,153) clutches laid annually between 2012 and 2017. These findings highlight the regional and global importance of this rookery with the Republic of the Congo, hosting the second largest documented populations of olive ridley and the third largest for leatherback turtles in Central Africa; and the fourth largest non-arribada olive ridley rookery globally. Furthermore, whilst the results show that Congo’s single marine and coastal national park provides protection for over half of sea turtle clutches laid in the country, there is scope for further protection along the coast. Although large parts of the African coastline remain to be adequately monitored, the modelling approach used here will be invaluable to inform future status assessments for sea turtles given that most datasets are temporally and spatially fragmented.