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Disturbances fundamentally alter ecosystem functions, yet predicting their impacts remains a key scientific challenge. While the study of disturbances is ubiquitous across many ecological disciplines, there is no agreed-upon, cross-disciplinary foundation for discussing or quantifying the complexity of disturbances, and no consistent terminology or methodologies exist. This inconsistency presents an increasingly urgent challenge due to accelerating global change and the threat of interacting disturbances that can destabilize ecosystem responses. By harvesting the expertise of an interdisciplinary cohort of contributors spanning 42 institutions across 15 countries, we identified an essential limitation in disturbance ecology: the word ‘disturbance’ is used interchangeably to refer to both the events that cause, and the consequences of, ecological change, despite fundamental distinctions between the two meanings. In response, we developed a generalizable framework of ecosystem disturbances, providing a well-defined lexicon for understanding disturbances across perspectives and scales. The framework results from ideas that resonate across multiple scientific disciplines and provides a baseline standard to compare disturbances across fields. This framework can be supplemented by discipline-specific variables to provide maximum benefit to both inter- and intra-disciplinary research. To support future syntheses and meta-analyses of disturbance research, we also encourage researchers to be explicit in how they define disturbance drivers and impacts, and we recommend minimum reporting standards that are applicable regardless of scale. Finally, we discuss the primary factors we considered when developing a baseline framework and propose four future directions to advance our interdisciplinary understanding of disturbances and their social-ecological impacts: integrating across ecological scales, understanding disturbance interactions, establishing baselines and trajectories, and developing process-based models and ecological forecasting initiatives. Our experience through this process motivates us to encourage the wider scientific community to continue to explore new approaches for leveraging Open Science principles in generating creative and multidisciplinary ideas.

The first ever global map of forest management was generated based on remote sensing data. To collect training data, we launched a series of Geo-Wiki (https://www.geo-wiki.org/) campaigns involving forest experts from different world regions, to explore which information related to forest management could be collected by visual interpretation of very high-resolution images from Google Maps and Microsoft Bing, Sentinel time series and normalized difference vegetation index (NDVI) profiles derived from Google Earth Engine. A machine learning technique was then used with the visually interpreted sample (280K locations) as a training dataset to classify PROBA-V satellite imagery. Finally, we obtained a global wall-to-wall map of forest management at a 100m resolution for the year 2015. The map includes classes such as intact forests; forests with signs of management, including logging; planted forests; woody plantations with a rotation period up to 15 years; oil palm plantations; and agroforestry. The map can be used to deliver further information about forest ecosystems, protected and observed forest status changes, biodiversity assessments, and other ecosystem-related aspects.

Since 2013, clearing rates have rapidly increased in the Amazon and Cerrado biomes. This acceleration has raised questions about the efficacy of current regional public and private conservation policies that seek to promote agricultural production while conserving remnants of natural vegetation. In this study, we assessed conservation and agricultural outcomes of four potential policy scenarios that represent perfect adherence to private sector, zero-deforestation commitments (i.e., the Amazon soy moratorium&mdash, ASM and the Amazon cattle agreements&mdash, CA) and to varying levels of implementation of the Brazilian Forest Code (FC). Under a zero-clearing scenario, we find that the extent of croplands as of 2017 within the two biomes (31 MHa) could double without further clearing if agriculture were to expand on all previously cleared land that is suitable for crops. Moreover, at least 47 MHa of land that is already cleared but unsuitable for crops would remain available for pasture. Under scenarios in which only legal clearing under the FC could occur, 51 MHa of additional natural vegetation could be cleared. This includes as many as 1 MHa of nonforest vegetation that could be cleared in the Amazon biome without triggering the ASM and CA monitoring systems. Two-thirds of the total vegetation vulnerable to legal clearing is located within the Cerrado biome, and 19 MHa of this land is suitable for cropland expansion. Legal clearing of all of these areas could reduce biodiversity persistence by 4% within the two biomes, when compared with the zero-clearing scenario, and release up to 9 PgCO2e, with the majority (75%) coming from the Cerrado biome. However, when we considered the potential outcomes of full implementation of the FC, we found that 22% (11 MHa) of the 51 MHa of vegetation subject to legal clearing could be protected through the environmental quotas market, while an additional 1 MHa should be replanted across the two biomes, predominantly in the Amazon biome (73% of the area subject to replanting). Together, quotas and replanting could prevent the release of 2 PgCO2e that would otherwise be emitted if all legal clearing occurred. Based on our results, we conclude that ongoing legal clearing could create additional space for cropland and cattle production beyond the substantial existing stocks of cleared areas but would significantly impair local carbon and biodiversity stocks.