文獻信息：
Bastida Felipe*, Eldridge David J, García Carlos, Png G. Kenny, Bardgett Richard D, Delgado-Baquerizo Manuel. Soil microbial diversity–biomass relationships are driven by soil carbon content across global biomes. The ISME Journal, 2021. DOI: 10.1038/s41396-021-00906-0
摘 要：生物多樣性和生物量之間的關係一直是生態學中長期爭論的焦點。土壤生物多樣性和生物量是生態系統功能的重要驅動因子。然而，與植物群落不同的是，關於土壤微生物群落的多樣性和生物量在全球分布的生物群落中如何相互聯繫，以及這種關係的變化如何影響生態系統功能，目前了解甚少。為了填補這一知識空白，本研究對全球生物群落進行了實地調查，並對比了植被和氣候類型。結果表明，土壤碳（C）含量與全球生物群落中土壤微生物的多樣性-生物量的關係和比例有關。該比值提供了一個綜合指數，用於識別地球上那些生物多樣性比生物量高的區域，反之亦然。土壤微生物多樣性/生物量比在碳含量低的乾旱環境中達到峰值，而在富碳的寒冷環境中則非常低。本研究進一步表明，與土地利用集約化和氣候變化相關的土壤碳含量降低可能會導致微生物多樣性-生物量比值的急劇變化，並對土壤過程產生潛在影響。
Fig. 1. Microbial biomass (nmol PLFA g−1 dry soil) and richness in soil, and their relationships across globally distributed ecosystems.
Fig. 2. Structural equation models (SEMs) describing the effects of multiple predictors on microbial diversity. A Refers to bacterial communities and B Refers to fungal communities. Numbers adjacent to arrows and in boxes are indicative of the effect size (*p ≤ 0.05; **p ≤ 0.01; ***p ≤ 0.001) of the relationship. R2 denotes the proportion of variance explained. Climate includes mean annual precipitation (MAP) and mean annual temperature (MAT). Soil includes pH and texture. Vegetation includes plant cover (PC), grassland (G), and forest (F). Hexagons represent quadratic variables. The relationship between pH and bacterial richness was quadratic. There was a nonsignificant deviation of the data from the model for bacterial (χ2 = 0.28, df = 1; p = 0.60; RMSEA p = 0.74) and fungal (χ2 = 0.09, df = 1; p = 0.76; RMSEA p = 0.85) diversity. R2 as follows: Bacterial richness = 0.48; Bacterial biomass = 0.67; Fungal richness = 0.45; Fungal biomass = 0.39. Direct effects for bacterial and fungal SEM are provided in Supporting Information (Tables S3 and S4, respectively).
Fig. 3. Structural equation models (SEMs) describing the effects of multiple predictors on microbial richness-to-biomass ratio. A Refers to bacterial communities and B Refers to fungal communities. Numbers adjacent to arrows and in boxes are indicative of the effect size (*p ≤ 0.05; **p ≤ 0.01; ***p ≤ 0.001) of the relationship. R2 denotes the proportion of variance explained. Climate includes mean annual precipitation (MAP) and mean annual temperature (MAT). Soil includes pH and texture. Vegetation includes plant cover (PC), grassland (G), and forest (F). Hexagons represent quadratic variables. The relationship between pH and bacterial richness was quadratic. There was a nonsignificant deviation of the data from the model for bacterial (χ2 = 0.38, df = 1; p = 0.54; RMSEA p = 0.70) and fungal (χ2 = 0.16, df = 1; p = 0.69; RMSEA p = 0.80) ratio. R2 as follows: Bacterial ratio = 0.68; Fungal ratio = 0.52. Direct effects for bacterial and fungal SEM are provided in Supporting Information (Tables S5 and S6, respectively)..
Fig. 4. Relationships between soil carbon content (%), microbial biomass (nmol PLFA g−1 dry soil), microbial richness, and the richness-to-biomass ratio of bacterial and fungal communities (unitless). All variables are normalized (log10 X + 1). N = 435 soil samples from 87 globally distributed locations (Fig. S1). Major biomes are based on field vegetation and climatic information from Kottek et al. [81].
Fig. 5. Predicted global distribution of biomass and standardized richness-to-biomass ratio of soil bacterial and fungal communities (unitless). Microbial biomass units are nmol PLFA g−1 dry soil. All variables are normalized (log10 X + 1). An alternative version of this figure showing qualitative data can be found in Fig S11.
Fig. 6. Relationship between soil respiration and the richness-to-biomass ratio (unitless) of soil bacterial and fungal communities. N = 86. All variables were normalized (log10 X + 1).
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