Maximum rooting depth of vegetation types at the global scale.

TLDR: Deep root habits are quite common in woody and herbaceous species across most of the terrestrial biomes, far deeper than the traditional view has held up to now, and has important implications for a better understanding of ecosystem function and its application in developing ecosystem models.

Abstract: The depth at which plants are able to grow roots has important implications for the whole ecosystem hydrological balance, as well as for carbon and nutrient cycling. Here we summarize what we know about the maximum rooting depth of species belonging to the major terrestrial biomes. We found 290 observations of maximum rooting depth in the literature which covered 253 woody and herbaceous species. Maximum rooting depth ranged from 0.3 m for some tundra species to 68 m for Boscia albitrunca in the central Kalahari; 194 species had roots at least 2 m deep, 50 species had roots at a depth of 5 m or more, and 22 species had roots as deep as 10 m or more. The average for the globe was 4.6±0.5 m. Maximum rooting depth by biome was 2.0±0.3 m for boreal forest. 2.1±0.2 m for cropland, 9.5±2.4 m for desert, 5.2±0.8 m for sclerophyllous shrubland and forest, 3.9±0.4 m for temperate coniferous forest, 2.9±0.2 m for temperate deciduous forest, 2.6±0.2 m for temperate grassland, 3.7±0.5 m for tropical deciduous forest, 7.3±2.8 m for tropical evergreen forest, 15.0±5.4 m for tropical grassland/savanna, and 0.5±0.1 m for tundra. Grouping all the species across biomes (except croplands) by three basic functional groups: trees, shrubs, and herbaceous plants, the maximum rooting depth was 7.0±1.2 m for trees, 5.1±0.8 m for shrubs, and 2.6±0.1 m for herbaceous plants. These data show that deep root habits are quite common in woody and herbaceous species across most of the terrestrial biomes, far deeper than the traditional view has held up to now. This finding has important implications for a better understanding of ecosystem function and its application in developing ecosystem models.