Scenarios of changes in biodiversity for the year 2100 can now be developed based on scenarios of changes in atmospheric carbon dioxide, climate, vegetation, and land use and the known sensitivity of biodiversity to these changes. This study identified a ranking of the importance of drivers of change, a ranking of the biomes with respect to expected changes, and the major sources of uncertainties. For terrestrial ecosystems, land-use change probably will have the largest effect, followed by climate change, nitrogen deposition, biotic exchange, and elevated carbon dioxide concentration. For freshwater ecosystems, biotic exchange is much more important. Mediterranean climate and grassland ecosystems likely will experience the greatest proportional change in biodiversity because of the substantial influence of all drivers of biodiversity change. Northern temperate ecosystems are estimated to experience the least biodiversity change because major land-use change has already occurred. Plausible changes in biodiversity in other biomes depend on interactions among the causes of biodiversity change. These interactions represent one of the largest uncertainties in projections of future biodiversity change.

The terrestrial carbon sink has been large in recent decades, but its size and location remain uncertain. Using forest inventory data and long-term ecosystem carbon studies, we estimate a total forest sink of 2.4 ± 0.4 petagrams of carbon per year (Pg C year(-1)) globally for 1990 to 2007. We also estimate a source of 1.3 ± 0.7 Pg C year(-1) from tropical land-use change, consisting of a gross tropical deforestation emission of 2.9 ± 0.5 Pg C year(-1) partially compensated by a carbon sink in tropical forest regrowth of 1.6 ± 0.5 Pg C year(-1). Together, the fluxes comprise a net global forest sink of 1.1 ± 0.8 Pg C year(-1), with tropical estimates having the largest uncertainties. Our total forest sink estimate is equivalent in magnitude to the terrestrial sink deduced from fossil fuel emissions and land-use change sources minus ocean and atmospheric sinks.

For centuries, biologists have studied patterns of plant and animal diversity at continental scales. Until recently, similar studies were impossible for microorganisms, arguably the most diverse and abundant group of organisms on Earth. Here, we present a continental-scale description of soil bacterial communities and the environmental factors influencing their biodiversity. We collected 98 soil samples from across North and South America and used a ribosomal DNA-fingerprinting method to compare bacterial community composition and diversity quantitatively across sites. Bacterial diversity was unrelated to site temperature, latitude, and other variables that typically predict plant and animal diversity, and community composition was largely independent of geographic distance. The diversity and richness of soil bacterial communities differed by ecosystem type, and these differences could largely be explained by soil pH (r 2 ‫؍‬ 0.70 and r 2 ‫؍‬ 0.58, respectively; P < 0.0001 in both cases). Bacterial diversity was highest in neutral soils and lower in acidic soils, with soils from the Peruvian Amazon the most acidic and least diverse in our study. Our results suggest that microbial biogeography is controlled primarily by edaphic variables and differs fundamentally from the biogeography of ''macro'' organisms.biodiversity ͉ microbial ecology ͉ soil bacteria ͉ terminal-restriction fragment length polymorphism

Although researchers have begun cataloging the incredible diversity of bacteria found in soil, we are largely unable to interpret this information in an ecological context, including which groups of bacteria are most abundant in different soils and why. With this study, we examined how the abundances of major soil bacterial phyla correspond to the biotic and abiotic characteristics of the soil environment to determine if they can be divided into ecologically meaningful categories. To do this, we collected 71 unique soil samples from a wide range of ecosystems across North America and looked for relationships between soil properties and the relative abundances of six dominant bacterial phyla (Acidobacteria, Bacteroidetes, Firmicutes, Actinobacteria, alpha-Proteobacteria, and the beta-Proteobacteria). Of the soil properties measured, net carbon (C) mineralization rate (an index of C availability) was the best predictor of phylum-level abundances. There was a negative correlation between Acidobacteria abundance and C mineralization rates (r2 = 0.26, P < 0.001), while the abundances of beta-Proteobacteria and Bacteroidetes were positively correlated with C mineralization rates (r2 = 0.35, P < 0.001 and r2 = 0.34, P < 0.001, respectively). These patterns were explored further using both experimental and meta-analytical approaches. We amended soil cores from a specific site with varying levels of sucrose over a 12-month period to maintain a gradient of elevated C availabilities. This experiment confirmed our survey results: there was a negative relationship between C amendment level and the abundance of Acidobacteria (r2 = 0.42, P < 0.01) and a positive relationship for both Bacteroidetes and beta-Proteobacteria (r2 = 0.38 and 0.70, respectively; P < 0.01 for each). Further support for a relationship between the relative abundances of these bacterial phyla and C availability was garnered from an analysis of published bacterial clone libraries from bulk and rhizosphere soils. Together our survey, experimental, and meta-analytical results suggest that certain bacterial phyla can be differentiated into copiotrophic and oligotrophic categories that correspond to the r- and K-selected categories used to describe the ecological attributes of plants and animals. By applying the copiotroph-oligotroph concept to soil microorganisms we can make specific predictions about the ecological attributes of various bacterial taxa and better understand the structure and function of soil bacterial communities.

Understanding and predicting ecosystem functioning (e.g., carbon and water fluxes) and the role of soils in carbon storage requires an accurate assessment of plant rooting distributions. Here, in a comprehensive literature synthesis, we analyze rooting patterns for terrestrial biomes and compare distributions for various plant functional groups. We compiled a database of 250 root studies, subdividing suitable results into 11 biomes, and fitted the depth coefficient β to the data for each biome (Gale and Grigal 1987). β is a simple numerical index of rooting distribution based on the asymptotic equation Y=1-β, where d = depth and Y = the proportion of roots from the surface to depth d. High values of β correspond to a greater proportion of roots with depth. Tundra, boreal forest, and temperate grasslands showed the shallowest rooting profiles (β=0.913, 0.943, and 0.943, respectively), with 80-90% of roots in the top 30 cm of soil; deserts and temperate coniferous forests showed the deepest profiles (β=0.975 and 0.976, respectively) and had only 50% of their roots in the upper 30 cm. Standing root biomass varied by over an order of magnitude across biomes, from approximately 0.2 to 5 kg m. Tropical evergreen forests had the highest root biomass (5 kg m), but other forest biomes and sclerophyllous shrublands were of similar magnitude. Root biomass for croplands, deserts, tundra and grasslands was below 1.5 kg m. Root/shoot (R/S) ratios were highest for tundra, grasslands, and cold deserts (ranging from 4 to 7); forest ecosystems and croplands had the lowest R/S ratios (approximately 0.1 to 0.5). Comparing data across biomes for plant functional groups, grasses had 44% of their roots in the top 10 cm of soil. (β=0.952), while shrubs had only 21% in the same depth increment (β=0.978). The rooting distribution of all temperate and tropical trees was β=0.970 with 26% of roots in the top 10 cm and 60% in the top 30 cm. Overall, the globally averaged root distribution for all ecosystems was β=0.966 (r =0.89) with approximately 30%, 50%, and 75% of roots in the top 10 cm, 20 cm, and 40 cm, respectively. We discuss the merits and possible shortcomings of our analysis in the context of root biomass and root functioning.