Defining pyromes and global syndromes of fire regimes

Abstract

Fire is a ubiquitous component of the Earth system that is poorly understood. To date, a global-scale understanding of fire is largely limited to the annual extent of burning as detected by satellites. This is problematic because fire is multidimensional, and focus on a single metric belies its complexity and importance within the Earth system. To address this, we identified five key characteristics of fire regimes--size, frequency, intensity, season, and extent--and combined new and existing global datasets to represent each. We assessed how these global fire regime characteristics are related to patterns of climate, vegetation (biomes), and human activity. Cross-correlations demonstrate that only certain combinations of fire characteristics are possible, reflecting fundamental constraints in the types of fire regimes that can exist. A Bayesian clustering algorithm identified five global syndromes of fire regimes, or pyromes. Four pyromes represent distinctions between crown, litter, and grass-fueled fires, and the relationship of these to biomes and climate are not deterministic. Pyromes were partially discriminated on the basis of available moisture and rainfall seasonality. Human impacts also affected pyromes and are globally apparent as the driver of a fifth and unique pyrome that represents human-engineered modifications to fire characteristics. Differing biomes and climates may be represented within the same pyrome, implying that pathways of change in future fire regimes in response to changes in climate and human activity may be difficult to predict.

Fig. 1.

Multidimensional fire space represented by selected combinations of fire characteristics. FRI by maximum fire intensity (A), fire season length by maximum fire size (B), maximum fire size by mean burned area (C), and FRI by mean burned area (D). For each combination there are constraints—presumably imposed through vegetation, climate, and people—which mean that not all of the space is occupied. Data are logged and rescaled. Dashed lines represent fifth and 95th piecewise quantile regression fits to the data. Fig. S3 shows graphs of all combinations.

Fig. 2.

Mapping the spatial distribution of pyromes. Produced from the five-cluster solution of a model-based expectation–maximization clustering algorithm. Pyromes represent regions of the globe that have similar fire frequencies, intensities, sizes, burned areas, and fire season lengths. Pixels with greater than 60% probability of being uniquely categorized are plotted (85% of the data).

Fig. 3.

Distinguishing pyromes using frequency distributions of fire characteristics. Data are logged and rescaled. Table 1 shows real values.

Fig. 4.

Plotting pyromes in climate space. A Whittaker plot (MAP–MAT) does not clearly distinguish pyromes as it does with vegetation (A). Meaningful climate indices improve the separation (B), but pyromes are not determined by climate alone. Black points show all vegetated 0.5° grid cells, gray points show all cells that had fire data. Lines show the 95th quantile of the density of points for each pyrome class.

Fig. 5.

Environmental characteristics of the five pyromes. The climate variables were chosen to represent important drivers of fire (see text and SI Materials and Methods). Lines represent the median, boxes the 25th and 75th quantiles, and whiskers the 0.5× interquartile range of the data. Significantly different distributions (two-sided t test) are indicated with letters.