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Fire-adaptive traits in plants of tropical and subtropical grassy ecosystems have been the subject of considerable global research, but only recently studied in pyrogenic Florida subtropical grasslands. Plant growth forms, belowground organs, and post-fire recovery strategies were studied for 198 grassland specialists in peninsular Florida. Community types (dry-mesic, mesic, wet-mesic, and wet) were sampled with 1m2 plots along the edaphic-hydrologic gradient and the association between these variables and fire-related plant traits was tested using fourth-corner analysis. Caulescent herbs (43), cespitose graminoids (27), and rosette herbs (31) are the most common growth forms among species sampled. Plants with epigeogenous and hypogeogenous rhizomes dominate the sample plots, including matrix graminoids, shrub geoxyles, and an acaulescent rhizomatous fire-resilient palm (Serenoa repens). Most species (163; 82%) exhibit resprouting, including 30 facultative resprouters and 133 obligate resprouters. All woody rhizomatous species are obligate resprouters, and 35 ephemeral herbaceous species are obligate reseeders. Community type was a better predictor of species abundances than hydrology, however, hydrology was significantly associated with species traits measured, particularly rhizome texture, with woody rhizomes prevalent in all but the wet sites. Belowground organs (xylopodia, geoxylic suffructices) and growth form were associated with frequent fire and phylogeny, suggesting fire regime as a driver of community phylogenetic diversity. Persistence, rapid resilience and co-occurrence of geoxyles align Florida subtropical grasslands with other global geoxyle grasslands. The old-growth, pyrogenic grassy ecosystems of peninsular Florida are the center of geoxyle diversification on the southeastern US coastal plain. [ABSTRACT FROM AUTHOR]

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Theory predicts that wildfires will encounter spatial thresholds where different drivers may become the dominant influence on continued fire spread. Studying these thresholds, however, is limited by a lack of sufficiently detailed data sets. To address this problem, we searched for scale thresholds in data describing wildfire size at the Avon Park Air Force Range, south-central Florida. We used power-law statistics to describe the “heavy-tail” of the fire size distribution, and quantile regression to determine how the edges of data distributions of fire size were related to climate. Power-law statistics revealed a heavy-tail, a pattern consistent with scale threshold theory, which predicts that large fires will be rare because only fires that cross all thresholds will become large. Results from quantile regression suggested that different climate conditions served as critical thresholds, influencing wildfire size at different spatial scales. Modeling at higher quantiles (≥75th) implicated drought as driving the spread of larger fires, whereas modeling at lower quantiles (≤25th) implicated that wind governed the spread of smaller fires. Fires of intermediate size were negatively associated with relative humidity. Our results are consistent with the idea that fire spread involves scale thresholds, with the small-scale drivers allowing fires to spread after ignition, but with further spread only being possible when large-scale drivers are favorable. These results suggest that other data sets that have heavy-tailed distributions may contain patterns generated by scale thresholds, and that these patterns may be revealed using quantile regression. [ABSTRACT FROM AUTHOR]