Soil bacterial diversity mediated by microscale aqueous-phase processes across biomes

Abstract

Soil bacterial diversity varies across biomes with potential impacts on soil ecological functioning. Here, we incorporate key factors that affect soil bacterial abundance and diversity across spatial scales into a mechanistic modeling framework considering soil type, carbon inputs and climate towards predicting soil bacterial diversity. The soil aqueous-phase content and connectivity exert strong influence on bacterial diversity for each soil type and rainfall pattern. Biome-specific carbon inputs deduced from net primary productivity provide constraints on soil bacterial abundance independent from diversity. The proposed heuristic model captures observed global trends of bacterial diversity in good agreement with predictions by an individual-based mechanistic model. Bacterial diversity is highest at intermediate water contents where the aqueous phase forms numerous disconnected habitats and soil carrying capacity determines level of occupancy. The framework delineates global soil bacterial diversity hotspots; located mainly in climatic transition zones that are sensitive to potential climate and land use changes.

Fig. 1. Illustration of aqueous habitat fragmentation and carrying capacity in relation to climatic water contents.

In regions where rainfall is frequent, the soil aqueous phase is largely connected and provides a common habitat for cells of different bacterial species. In soils of drier regions, the aqueous phase is increasingly fragmented and offers a large number of distinct habitats. When the soil becomes sufficiently dry, almost all aqueous habitats are physically isolated and might contain only a few species. Additionally, the total number of cells that can be maintained (potential carrying capacity) is reduced and smaller patches become uninhabited. The specific carrying capacity in a biome is based on carbon input flux and temperature that establish an upper bound on bacterial cell density (rarely realized in any particular location due to other limiting factors). The numbers below each panel indicate the number of cells per number of habitats. Diversity is expected to drop in dry regions with low cell abundance and in wet regions with high habitat connectivity.

Fig. 2. Soil bacterial abundance varies in relation to carbon input, temperature and soil depth.

a Bacterial cell density at soil-carrying capacity as a function of net primary productivity (NPP) with model estimates sensitive to mean annual temperature (MAT) (solid lines). Estimates are compared with measured data of microbial biomass converted to bacterial cell density and are grouped by temperature (MAT ≤ 0 °C, 0 °C > MAT ≤ 15 °C, MAT > 15 °C). Each group’s median is reported in the figure legend in blue, green and orange, respectively. The distributions of cell densities are indicated for each temperature group as the central 50 and 95% range. b Variations of bacterial cell density with soil depth. The lognormal fit provides bounds on cell density (carrying capacity) for intermediate MAT (solid line) and for the central 95% of NPP (shaded area). Observed estimates of cell density are reported for their average sampling depth. Most samples were taken above 10 cm as shown in the boxplot. Source data are provided as a Source Data file.

Fig. 3. Observed and predicted variations in soil bacterial diversity with climatic water content.

a, b Estimates of bacterial richness from two studies are binned by climatic water contents (bin width: 0.05) and the median and interquartile range are reported (circles and bars, respectively). The exact number of samples per group is listed in Supplementary Table 2. Individual data points are shown for bins containing less than ten samples (small circles). The solid black lines correspond to predictions by the fragmentation-based heuristic model (HM) for median carrying capacity specific to each dataset. The square symbols, thin solid line and shading (mean, rolling mean ± SD, n = 12) depict simulated bacterial richness using the spatially explicit individual-based model (SIM) for different water contents. a Bacterial richness from the Earth Microbiome Project (Thompson et al.—EMP) was reported for different soil depths and thus grouped accordingly (<25 and ≥25 cm, top- and subsoil, respectively). The dashed line represents a model scenario with reduced carrying capacity by considering only the subsoil. b Soil bacterial richness from a recent study (Delgado-Baquerizo et al.—DEL). Colors indicate reported soil pH, which has been shown to be affected by climate. For comparison with the DEL dataset, only the top 512 species were considered in the SIM. Source data are provided as a Source Data file.

Fig. 4. Modeled global biogeography of soil bacterial diversity.

Global patterns are modeled based on aqueous microhabitats in the top 25 cm considering climate, NPP and soil type. a Global map of predicted soil bacterial richness. High values correspond to more heterogeneous soil environments, potentially harboring a larger number of habitats. b Global distribution of Shannon index for estimated bacterial diversity. In addition to richness, the Shannon diversity index considers the relative abundance of unique habitats. Higher values of the Shannon index could translate to more even bacterial communities.

Fig. 5. Bacterial community evenness decreases with carrying capacity and climatic water contents.

Evenness from two independent studies is shown together with estimated cell density (carrying capacity). Samples were aggregated by latitude, longitude and soil depth (EMP, n = 484 and DEL, n = 218). The median and interquartile ranges (colored symbols and bars) are displayed for groups of water contents (bin width: 0.05, number of samples, see Supplementary Table 2). Individual data points are shown for bins containing less than ten samples (small circles) and samples with cell density lower than 10¹² m⁻³ were removed. Evenness predicted by the heuristic model (HM) is calculated using paired values of climatic water content and carrying capacity (evaluated for every sample). Using the joint data of water content and cell density as model input, the HM reproduces the observed tendency of evenness. A locally weighted scatterplot smooth (LOWESS) of modeled evenness is shown for the HM predictions (solid line). Source data are provided as a Source Data file.