To prevent further global declines in biodiversity, identifying and understanding key habitats is crucial for successful conservation strategies. For example, globally, seabird populations are under threat and animal movement data can identify key at‐sea areas and provide valuable information on the state of marine ecosystems. To date, in order to locate these areas, studies have used global positioning system (GPS) to record position and are sometimes combined with time–depth recorder (TDR) devices to identify diving activity associated with foraging, a crucial aspect of at‐sea behaviour. However, the use of additional devices such as TDRs can be expensive, logistically difficult and may adversely affect the animal. Alternatively, behaviours may be resolved from measurements derived from the movement data alone. However, this behavioural analysis frequently lacks validation data for locations predicted as foraging (or other behaviours). Here, we address these issues using a combined GPS and TDR dataset from 108 individuals by training deep learning models to predict diving in European shags, common guillemots and razorbills. We validate our predictions using withheld data, producing quantitative assessment of predictive accuracy. The variables used to train these models are those recorded solely by the GPS device: variation in longitude and latitude, altitude and coverage ratio (proportion of possible fixes acquired within a set window of time). Different combinations of these variables were used to explore the qualities of different models, with the optimum models for all species predicting non‐diving and diving behaviour correctly over 94% and 80% of the time, respectively. We also demonstrate the superior predictive ability of these supervised deep learning models over other commonly used behavioural prediction methods such as hidden Markov models. Mapping these predictions provides useful insights into the foraging activity of a range of seabird species, highlighting important at sea locations. These models have the potential to be used to analyse historic GPS datasets and further our understanding of how environmental changes have affected these seabirds over time.

Large, remote marine protected areas (MPAs) containing both reef and pelagic habitats, have been shown to offer considerable refuge to populations of reef‐associated sharks. Many large MPAs are, however, impacted by illegal fishing activity conducted by unlicensed vessels. While enforcement of these reserves is often expensive, it would likely benefit from the integration of ecological data on the mobile animals they are designed to protect. Consequently, shark populations in some protected areas continue to decline, as they remain a prime target for illegal fishers. To understand shark movements and their vulnerability to illegal fishing, 3 years of acoustic tracking data, from 101 reef‐associated sharks, were analysed as movement networks to explore the predictability of movement patterns and identify key movement corridors within the British Indian Ocean Territory (BIOT) MPA. We examined how space use and connectivity overlap with spatially explicit risk of illegal fishing, through data obtained from the management consultancy enforcing the MPA. Using individual‐based models, the movement networks of two sympatric shark species were efficiently predicted with distance‐decay functions (>95% movements accurately predicted). Model outliers were used to highlight the locations with unexpectedly high movement rates where MPA enforcement patrols might most efficiently mitigate predator removal. Activity space estimates and network metrics illustrate that silvertip sharks were more dynamic, less resident and link larger components of the MPA than grey reef sharks. However, we show that this behaviour potentially enhances their exposure to illegal fishing activity. Synthesis and applications. Marine protected area (MPA) enforcement strategies are often limited by resources. The British Indian Ocean Territory MPA, one of the world's largest ‘no take’ MPAs, has a single patrol vessel to enforce 640,000 km² of open ocean, atoll and reef ecosystems. We argue that to optimize the patrol vessel search strategy and thus enhance their protective capacity, ecological data on the space use and movements of desirable species, such as large‐bodied reef predators, must be incorporated into management plans. Here, we use electronic tracking data to evaluate how shark movement dynamics influence species mortality trajectories in exploited reef ecosystems. In doing so we discuss how network analyses of such data might be applied for protected area enforcement.

Global wind patterns affect flight strategies in many birds, including pelagic seabirds, many of which use wind-powered soaring to reduce energy costs during at-sea foraging trips and migration. Such long-distance movement patterns are underpinned by local interactions between wind conditions and flight behaviour, but these fine-scale relationships are far less well understood. Here we show that remotely sensed ocean wind speed and direction are highly significant predictors of soaring behaviour in a migratory pelagic seabird, the Manx shearwater ( Puffinus puffinus ). We used high-frequency GPS tracking data (10 Hz) and statistical behaviour state classification to identify two energetic modes in at-sea flight, corresponding to flap-like and soar-like flight. We show that soaring is significantly more likely to occur in tailwinds and crosswinds above a wind speed threshold of around 8 m s ⁻¹ , suggesting that these conditions enable birds to reduce metabolic costs by preferentially soaring over flapping. Our results suggest a behavioural mechanism by which wind conditions may shape foraging and migration ecology in pelagic seabirds, and thus indicate that shifts in wind patterns driven by climate change could impact this and other species. They also emphasize the emerging potential of high-frequency GPS biologgers to provide detailed quantitative insights into fine-scale flight behaviour in free-living animals.

The UN Sustainable Development Goals (SDGs) represent a universal agenda that nations have committed to achieving by 2030. The challenge is substantial, with no country excelling across all SDGs. Using global UN data, we assess patterns of positive and negative correlations between indicators of SDG status and progress. For nearly 70% of SDG indicators, status is positively associated with GDP/capita. Progress on SDG indicators, however, occurs in both poorer and wealthier countries. When GDP/capita is controlled for, positive associations remain between health, environment and energy usage indicators. Economic growth is negatively associated with changes in some health and environment indicators. For SDGs targets to be achieved, major opportunities and conflicts will need to be identified, prioritized and acted upon.

Given the current biodiversity crisis, pragmatic approaches to detect global conservation trends across a broad range of taxa are critical. A sampled approach to the Red List Index (RLI) was proposed, as many groups are highly speciose. However, a decade after its conception, the recommended 900 species sample has only been implemented in six groups and trend data are available for none, potentially because this sample is unfeasibly high. Using a broader set of all available data we show that when re‐assessments are conducted every 10 years, 200 species (400 in some cases) should be sufficient to detect a RLI trend. Correctly detecting changes in slope still requires samples of 900 species (11,000 in some cases). Sampled assessments can accelerate biodiversity monitoring and complement current metrics, but the time‐period between assessments and the approaches’ purpose should be carefully considered, as there is a trade‐off between sample size and the resulting indices.