Despite a growing interest in interdisciplinary research, systematic ways of how to integrate data from different disciplines are still scarce. We argue that successful resource management relies on two key data sources: natural science data, which represents ecosystem structure and processes, and social science data, which describes people's perceptions and understanding. Both are vital, mutually complementing information sources that can underpin the development of feasible and effective policies and management interventions. To harvest the added value of combined knowledge, a uniform scaling system is needed. In this paper, we propose a standardized methodology to connect and explore different types of quantitative data from the natural and social sciences reflecting temporal trends in ecosystem quality. We demonstrate this methodology with different types of data such as fisheries stocks and mangrove cover on the one hand and community's perceptions on the other. The example data are collected from three United Nations Educational Scientific and Cultural Organization (UNESCO) Biosphere reserves and one marine park in Southeast Asia. To easily identify patterns of convergence or divergence among the datasets, we propose heat maps using colour codes and icons for language- and education-independent understandability. Finally, we discuss the limitations as well as potential implications for resource management and the accompanying communication strategies. This article is part of the theme issue ‘Nurturing resilient marine ecosystems’.

Rivers are key pathways for transporting microplastics from land to the oceans, yet microplastic flux estimates remain uncertain. Remote sensing allows repeated broad-scale measurements and can be used to extrapolate limited in situ observations. This study investigated the relationship between suspended particulate matter (SPM), a satellite-observable water quality parameter, and microplastic concentration in a partially mixed estuary (Tamar, UK). Microplastic concentrations ranged from 0.04 to 0.99 microplastics/m³, decreasing downstream. A significant correlation was found between SPM and microplastic concentration over two seasons. This relationship was used to compute a multiyear timeseries of proxy microplastic concentration from satellite imagery and produce estimates of annual proxy microplastic flux. This approach could be applied to investigate microplastic flux in other major rivers worldwide where such a relationship between microplastics and SPM exists. To apply this workflow elsewhere, the establishment of local SPM-to-microplastic relationships from in situ observations and local validation of remote sensing SPM algorithms are essential.

Effective management to conserve marine environments requires up-to-date information on the location, distribution, and extent of major benthic habitats. Remote sensing is a key tool for such assessments, enabling consistent, repeated measurements over large areas. There is particular interest in using freely available satellite images such as from the Copernicus Sentinel-2 series for accessible repeat assessments. In this study, an area of 438 km² of the northern Red Sea coastline, adjacent to the NEOM development was mapped using Sentinel-2 imagery. A hierarchical Random Forest classification method was used, where the initial level classified pixels into a geomorphological class, followed by a second level of benthic cover classification. Uncrewed Aerial Vehicle (UAV) surveys were carried out in 12 locations in the NEOM area to collect field data on benthic cover for training and validation. The overall accuracy of the geomorphic and benthic classifications was 84.15% and 72.97%, respectively. Approximately 12% (26.26 km²) of the shallow Red Sea study area was classified as coral or dense algae and 16% (36.12 km²) was classified as rubble. These reef environments offer crucial ecosystem services and are believed to be internationally important as a global warming refugium. Seagrass meadows, covering an estimated 29.17 km² of the study area, play a regionally significant role in carbon sequestration and are estimated to store 200 tonnes of carbon annually, emphasising the importance of their conservation for meeting the environmental goals of the NEOM megaproject. This is the first map of this region generated using Sentinel-2 data and demonstrates the feasibility of using an open source and reproducible methodology for monitoring coastal habitats in the region. The use of training data derived from UAV imagery provides a low-cost and time-efficient alternative to traditional methods of boat or snorkel surveys for covering large areas in remote sites.

Multi-temporal remote sensing imagery can be used to explore how mangrove assemblages are changing over time and facilitate critical interventions for ecological sustainability and effective management. This study aims to explore the spatial dynamics of mangrove extents in Palawan, Philippines, specifically in Puerto Princesa City, Taytay and Aborlan, and facilitate future predictions for Palawan using the Markov Chain model. The multi-date Landsat imageries during the period 1988–2020 were used for this research. The support vector machine algorithm was sufficiently effective for mangrove feature extraction to generate satisfactory accuracy results (>70% kappa coefficient values; 91% average overall accuracies). In Palawan, a 5.2% (2693 ha) decrease was recorded during 1988–1998 and an 8.6% increase in 2013–2020 to 4371 ha. In Puerto Princesa City, a 95.9% (2758 ha) increase was observed during 1988–1998 and 2.0% (136 ha) decrease during 2013–2020. The mangroves in Taytay and Aborlan both gained an additional 2138 ha (55.3%) and 228 ha (16.8%) during 1988–1998 but also decreased from 2013 to 2020 by 3.4% (247 ha) and 0.2% (3 ha), respectively. However, projected results suggest that the mangrove areas in Palawan will likely increase in 2030 (to 64,946 ha) and 2050 (to 66,972 ha). This study demonstrated the capability of the Markov chain model in the context of ecological sustainability involving policy intervention. However, as this research did not capture the environmental factors that may have influenced the changes in mangrove patterns, it is suggested adding cellular automata in future Markovian mangrove modelling.