Background: ‘Megafire’ is an emerging concept commonly used to describe fires that are extreme in terms of size, behaviour, and/or impacts, but the term’s meaning remains ambiguous.Approach: We sought to resolve ambiguity surrounding the meaning of ‘megafire’ by conducting a structured review of the use and definition of the term in several languages in the peer‐reviewed scientific literature. We collated definitions and descriptions of megafire and identified criteria frequently invoked to define megafire. We recorded the size and location of megafires and mapped them to reveal global variation in the size of fires described as megafires.Results: We identified 109 studies that define the term ‘megafire’ or identify a megafire, with the term first appearing in the peer‐reviewed literature in 2005. Seventy‐one (~65%) of these studies attempted to describe or define the term. There was considerable variability in the criteria used to define megafire, although definitions of megafire based on fire size were most common. Megafire size thresholds varied geographically from > 100–100,000 ha, with fires > 10,000 ha the most common size threshold (41%, 18/44 studies). Definitions of megafire were most common from studies led by authors from North America (52%, 37/71). We recorded 137 instances from 84 studies where fires were reported as megafires, the vast majority (94%, 129/137) of which exceed 10,000 ha in size. Megafires occurred in a range of biomes, but were most frequently described in forested biomes (112/137, 82%), and usually described single ignition fires (59% 81/137).Conclusion: As Earth’s climate and ecosystems change, it is important that scientists can communicate trends in the occurrence of larger and more extreme fires with clarity. To overcome ambiguity, we suggest a definition of megafire as fires > 10,000 ha arising from single or multiple related ignition events. We introduce two additional terms – gigafire (> 100,000 ha) and terafire (> 1,000,000 ha) – for fires of an even larger scale than megafires.

Anthropogenic and natural disturbances can cause degradation of ecosystems, reducing their capacity to sustain biodiversity and provide ecosystem services. Understanding the extent of ecosystem degradation is critical for estimating risks to ecosystems, yet there are few existing methods to map degradation at the ecosystem scale and none using freely available satellite data for mangrove ecosystems. In this study, we developed a quantitative classification model of mangrove ecosystem degradation using freely available earth observation data. Crucially, a conceptual model of mangrove ecosystem degradation was established to identify suitable remote sensing variables that support the quantitative classification model, bridging the gap between satellite-derived variables and ecosystem degradation with explicit ecological links. We applied our degradation model to two case-studies, the mangroves of Rakhine State, Myanmar, which are severely threatened by anthropogenic disturbances, and Shark River within the Everglades National Park, USA, which is periodically disturbed by severe tropical storms. Our model suggested that 40% (597 km²) of the extent of mangroves in Rakhine showed evidence of degradation. In the Everglades, the model suggested that the extent of degraded mangrove forest increased from 5.1% to 97.4% following the Category 4 Hurricane Irma in 2017. Quantitative accuracy assessments indicated the model achieved overall accuracies of 77.6% and 79.1% for the Rakhine and the Everglades, respectively. We highlight that using an ecological conceptual model as the basis for building quantitative classification models to estimate the extent of ecosystem degradation ensures the ecological relevance of the classification models. Our developed method enables researchers to move beyond only mapping ecosystem distribution to condition and degradation as well. These results can help support ecosystem risk assessments, natural capital accounting, and restoration planning and provide quantitative estimates of ecosystem degradation for new global biodiversity targets.

Climate change poses a growing risk to global biodiversity. To prioritize conservation efforts, identification of the species and ecosystems most at risk from further changes in climatic conditions is critically needed. Although frameworks are available to assess species vulnerability to climate change, we still lack an easily implementable, ecosystem‐level perspective to inform landscape management. Here, we introduce a novel, spatially explicit vulnerability framework able to generate assessments at the ecosystem scale and apply it to Mozambican forest mangroves, which are under growing pressures from climate change. Results show that most of these ecosystems are currently highly vulnerable to sea level rise, while mangroves in the Zambezia and Nampula districts are highly vulnerable to both sea level rise and tropical storms. Altogether, we believe the introduced assessment framework has clear potential to inform conservation planning and management at various spatial scales, and help achieve adaptive management in the face of climatic uncertainties.