Human beings are an active component of every terrestrial ecosystem on Earth. Although our local impact on the evolution of these ecosystems has been undeniable and extensively documented, it remains unclear precisely how our activities are altering them, in part because ecosystems are dynamic systems structured by complex, non-linear feedback processes and cascading effects. We argue that it is only by studying human–environment interactions over timescales that greatly exceed the lifespan of any individual human (i.e., the deep past or longue durée), we can hope to fully understand such processes and their implications. In this article, we identify some of the key challenges faced in integrating long-term datasets with those of other areas of sustainability science, and suggest some useful ways forward. Specifically, we (a) highlight the potential of the historical sciences for sustainability science, (b) stress the need to integrate theoretical frameworks wherein humans are seen as inherently entangled with the environment, and (c) propose formal computational modelling as the ideal platform to overcome the challenges of transdisciplinary work across large, and multiple, geographical and temporal scales. Our goal is to provide a manifesto for an integrated scientific approach to the study of socio-ecological systems over the long term.

Accidental oil spills were assessed in the north-eastern Ecuadorian Amazon, a rich biodiversity and cultural heritage area. Institutional reports were used to estimate oil spill volumes over the period 2001–2011. However, we had to make with heterogeneous and incomplete data. After statistically discriminating well- and poorly-documented oil blocks, some spill factors were derived from the former to spatially allocate oil spills where fragmentary data were available. Spatial prediction accuracy was assessed using similarity metrics in a cross-validation approach. Results showed 464 spill events (42.2/year), accounting for 10,000.2 t of crude oil, equivalent to annual discharges of 909.1 (±SD = 1219.5) t. Total spill volumes increased by 54.8% when spill factors were used to perform allocation to poorly-documented blocks. Resulting maps displayed pollution ‘hotspots’ in Dayuma and Joya de Los Sachas, with the highest inputs averaging 13.8 t km⁻² year⁻¹. The accuracy of spatial prediction ranged from 32 to 97%, depending on the metric and the weight given to double-zeros. Simulated situations showed that estimation accuracy depends on variabilities in incident occurrences and in spill volumes per incident. Our method is suitable for mapping hazards and risks in sensitive ecosystems, particularly in areas where incomplete data hinder this process.

The Ituri-Epulu-Aru landscape (IEAL) is experiencing deforestation and forest degradation. This deforestation is at the root of many environmental disturbances in a region characterized by endemism in biodiversity. The importance of this article is to provide useful information for those who wish to discuss a model that can be replicated for other territories affected by deforestation and changes in natural and anthropogenic forest structure. This article focuses on the triangulation of spatialized prospective scenarios in order to identify future trajectories based on the knowledge of historical dynamics through the diachronic analysis of three satellite images (2003–2010–2014–2016). The scenarios were designed in a supervised model implemented in the DINAMICA EGO platform. The three scenarios: business as-usual (BAU), rapid economic growth (REG) and sustainable management of the environment (SME), extrapolating current trends, show that by 2061 this landscape will always be dominated forests (+84%). Old-growth forests occupy 74.2% of the landscape area in the BAU scenario, 81.4% in the SEM scenario and 61.2% in the REG scenario. The SEM scenario gives hope that restoration and preservation of biodiversity priority habitats is still possible if policy makers become aware of it.