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Summary: Ecologists are being challenged to predict how ecosystems will respond to climate changes. According to the Multi‐Colored World (MCW) hypothesis, climate impacts may not manifest because consumers such as fire and herbivory can override the influence of climate on ecosystem state. One MCW interpretation is that climate determinism fails because alternative ecosystem states (AES) are possible at some locations in climate space. We evaluated theoretical and empirical evidence for the proposition that forest and savanna are AES in Africa. We found that maps which infer where AES zones are located were contradictory. Moreover, data from longitudinal and experimental studies provide inconclusive evidence for AES. That is, although the forest‐savanna AES proposition is theoretically sound, the existing evidence is not yet convincing. We conclude by making the case that the AES proposition has such fundamental consequences for designing management actions to mitigate and adapt to climate change in the savanna‐forest domain that it needs a more robust evidence base before it is used to prescribe management actions. [ABSTRACT FROM AUTHOR]

Dry lightning is a prevalent episodic natural ignition source for wildfires, particularly in remote regions where such fires can escalate into uncontrollable events, burning extensive areas. In this study, we aimed to understand the interplay of environmental, fuel, and geographical factors in evaluating the probability of fire initiation following dry lightning strikes in Tasmania, Australia. We integrated dry lightning, active fire records, and gridded data on fire weather, fuel, and topography into a binary classification framework for both fire‐initiating and non‐fire‐causing lightning strikes. Employing statistical and machine learning techniques, we quantified the likelihood of fire initiation due to dry lightning, with the resampled Random Forest model exhibiting notable performance with an ROC‐AUC value of 0.98. Our findings highlight how fuel characteristics and moisture content associated with particular vegetation types influence fire initiation and provide an objective approach for identifying susceptible regions of dry lightning ignitions, informing associated fire management responses. Plain Language Summary: Lightning strikes occurring with minimal rainfall, known as dry lightning strikes, often ignite wildfires in dry vegetation, especially in remote areas. These fires can remain undetected for hours to days and quickly grow out of control under favorable conditions, causing extensive damage and burning large areas. Our study investigated how environmental factors such as weather, terrain, and fuel variables (fuel load, fuel type, and fuel moisture) affect the probability of a fire starting after a dry lightning strike in Tasmania, Australia. We analyzed data on lightning strikes, active fires, topography, fuel (via vegetation and fire history), and weather conditions, to build advanced machine learning models to predict the chances of a fire started following a dry lightning strike. Our findings indicate a high probability of ignition in scenarios characterized by high fuel loads and low fuel moisture content, particularly in treeless vegetation types such as buttongrass sedgelands. This information can help fire authorities identify areas or times most at risk of dry lightning‐ignited wildfires, allowing for better fire prevention and response strategies. Key Points: Random Forest modeling accurately identified fires ignited by dry lightning strikes in TasmaniaWestern Tasmania exhibits heightened vulnerability to wildfires ignited by lightning strikesTreeless fuel types have higher risk than forests of dry lightning ignitions [ABSTRACT FROM AUTHOR]

Landscape fires are an integral component of the Earth system and a feature of prehistoric, subsistence, and industrial economies. Specific spatiotemporal patterns of landscape fire occur in different locations around the world, shaped by the interactions between environmental and human drivers of fire activity. Seven distinct types of landscape fire emerge from these interactions: remote area fires, wildfire disasters, savanna fires, Indigenous burning, prescribed burning, agricultural burning, and deforestation fires. All can have substantial impacts on human health and well-being directly and indirectly through ( a ) exposure to heat flux (e.g., injuries and destructive impacts), ( b ) emissions (e.g., smoke-related health impacts), and ( c ) altered ecosystem functioning (e.g., biodiversity, amenity, water quality, and climate impacts). Minimizing the adverse effects of landscape fires on population health requires understanding how human and environmental influences on fire impacts can be modified through interventions targeted at individual, community, and regional levels.