原名：Depth-dependent drivers of soil microbial necromass carbon across Tibetan alpine grasslands

譯名：青藏高原高寒草地土壤微生物殘?bào)w碳的驅(qū)動(dòng)者取決于土壤深度

期刊：Global Change Biology

2020年影響因子:?10.863

在線(xiàn)發(fā)表時(shí)間：2021.11.02

第一作者：Mei He

通訊作者：Yuanhe Yang

第一單位：中國(guó)科學(xué)院植物研究所植被與環(huán)境變化國(guó)家重點(diǎn)實(shí)驗(yàn)室

研究背景

微生物壞死碳(C)被認(rèn)為是持久性土壤碳庫(kù)的重要貢獻(xiàn)者。然而，目前還缺乏對(duì)不同土層特別是高山生態(tài)系統(tǒng)微生物壞死量C的大規(guī)模系統(tǒng)觀測(cè)。此外，植物碳輸入和礦物性質(zhì)等生物和非生物變量在調(diào)節(jié)微生物壞死量C方面的相對(duì)重要性是否會(huì)隨土壤深度而改變尚不清楚。

研究方案

沿著青藏高原約2200公里的高寒草地樣帶進(jìn)行了大規(guī)模采樣，共采集了36個(gè)地點(diǎn)的表土和底土樣品(Figure 1a)，并根據(jù)氨基糖估算了微生物殘?bào)wC的含量。為了探索微生物殘?bào)wC的關(guān)鍵決定因素，檢測(cè)了各種生物和非生物因素，包括植物碳輸入、微生物性質(zhì)(如微生物生物量C (MBC)、總磷脂脂肪酸(PLFAs))、礦物保護(hù)(粘土含量、鐵/鋁氧化物和交換性鈣)和土壤理化性質(zhì)(如:土壤溫度、有機(jī)碳與全氮比)。進(jìn)一步采用方差分解分析(VPA)和結(jié)構(gòu)方程模型(SEM)定量分析了這些因素對(duì)土壤微生物殘?bào)wC空間變化的相對(duì)貢獻(xiàn)。

主要研究結(jié)果

在36個(gè)采樣點(diǎn)，表層和深層土壤的微生物殘?bào)wC分別為0.55 ~ 34.78和0.40 ~ 15.19 mg g-1 dry soil，平均值分別為9.57, 1.72和3.29, 0.57 mg g-1 dry soil.?高寒草原、高寒草甸以及整個(gè)高寒草地的微生物殘?bào)wC均隨土壤深度的增加而顯著降低(Figure 1 c)。與總微生物殘?bào)wC一致，真菌和細(xì)菌殘?bào)wC在表土中顯著高于底土(Figure S1)。而在有機(jī)碳?xì)w一化條件下，兩種草地類(lèi)型的土壤微生物殘?bào)wC含量均無(wú)顯著差異(高寒草原:P = 0.47;高寒草甸:P = 0.40)或整個(gè)高寒草甸(P = 0.28，F(xiàn)igure S2)。有趣的是，高寒草地微生物殘?bào)wC對(duì)土壤有機(jī)碳的貢獻(xiàn)顯著低于全球草地?(表土:45.4% vs 58.1%;底土:41.7% vs. 53.7%; Figure S3)。

微生物殘?bào)wC的主要決定因素與土壤深度有關(guān)。在表土中，微生物殘?bào)wC隨植物C輸入量、MBC、總PLFAs、真菌PLFAs和細(xì)菌PLFAs的增加而顯著增加(Figure 2a-e)。與黏土含量、Caexe、Feo+Alo和Fep+Alp也表現(xiàn)出正相關(guān) (Figure 3a-d)。此外，微生物殘?bào)wC隨土壤理化參數(shù)的變化而變化，與土壤水分和有機(jī)碳/全氮 (Figure 4b-d)，但與土壤pH值呈負(fù)相關(guān)(Figure 4c)，與土壤溫度沒(méi)有顯著關(guān)系 (Figure 4a)。與表土相似，底土微生物殘?bào)wC與植物C輸入量、總PLFAs、細(xì)菌PLFAs (Figure 2f, h, j)，粘土含量，Caexe, Feo+Alo, Fep+Alp (Figure 3e-h) 呈正相關(guān)。與土壤濕度(Figure 4f)，與土壤pH值(Figure 4g)，但不受土壤溫度(Figure 4e)和SOC/TN (Figure 4h)的調(diào)控。

VPA和SEM結(jié)果共同表明，微生物殘?bào)wC的主導(dǎo)驅(qū)動(dòng)因素在不同土壤深度之間存在差異?(Figure 5-7)。對(duì)于表層土壤，VPA結(jié)果表明植物C輸入和礦物保護(hù)在調(diào)節(jié)整個(gè)研究區(qū)微生物殘?bào)wC的積累中發(fā)揮了重要作用。植物C的輸入和礦物保護(hù)完全解釋了92.6%的微生物殘?bào)wC的空間變異 (Figure 5a)。SEM分析還表明，微生物殘?bào)wC主要受植物C輸入和礦物保護(hù)的直接影響 (Figure 6a)，標(biāo)準(zhǔn)化直接效應(yīng)分別為0.48和0.55?(Figure 7a)。此外，植物C的輸入通過(guò)調(diào)節(jié)土壤pH和礦物保護(hù)間接影響微生物殘?bào)wC (Figure 7a)。VPA結(jié)果表明，與表層土壤相比，在深層土壤中，礦物保護(hù)對(duì)微生物殘?bào)wC變化的解釋比例(30.1%)遠(yuǎn)高于植物C輸入(4.1%)?( Figure 5b)。SEM分析證實(shí)了礦物保護(hù)在調(diào)節(jié)土壤微生物殘?bào)wC中的重要作用 (Figure 6b)。生物和非生物因素共同解釋了62%的微生物殘?bào)wC的空間變異 (Figure 6b)。其中，礦物保護(hù)對(duì)微生物殘?bào)wC的直接影響最大，而植物C的輸入對(duì)最終的SEM沒(méi)有直接影響 (Figure 7b)。

Figure 1?Geographic distributions of sampling sites (a) and frequency distributions of microbial necromass C in the topsoil (b) and subsoil (d) across Tibetan alpine grasslands, and comparison of microbial necromass C between the two soil depths in alpine meadow, alpine steppe and the whole alpine grassland (c). The vegetation map is derived from China's Vegetation Atlas (Editorial Committee for Vegetation Map of China, 2001). The horizontal lines and circles inside each box represent the medians and the mean values, respectively. The ends of the boxes show the 25th and the 75th quartiles, and the whiskers indicate the standard deviation (SD), respectively.

Figure 2?Relationships between microbial necromass C and biotic factors including plant C input (a, f), MBC (b, g), total PLFAs (c, h), fungal PLFAs (d, i) and bacterial PLFAs (e, j). Blue and yellow symbols represent data points in the topsoil and subsoil, respectively. The solid cycles and triangles represent data points from alpine steppe (n = 22) and alpine meadow (n = 14), respectively. The solid lines were fitted by ordinary least-squares regressions, and the shadow areas corresponded to 95% confidence intervals. * and ** represent significant level at P < 0.05 and P < 0.01, respectively. MBC: microbial biomass carbon. PLFAs: phospholipid fatty acids.

Figure 3?Relationships between microbial necromass C and mineral protection including clay content (a, e), Caexe (b, f), Feo+Alo (c, g) and Fep+Alp (d, h), respectively. Blue and yellow symbols represent data points in the topsoil and subsoil, respectively. The solid cycles and triangles represent data points from alpine steppe (n = 22) and alpine meadow (n = 14), respectively. The solid lines were fitted by ordinary least-squares regressions, and the shadow areas corresponded to 95% confidence intervals. * and ** represent significant level at P < 0.05 and P < 0.01, respectively. Caexe: exchangeable Ca2+; Feo+Alo: sum of pyrophosphate-extractable Fe/Al oxides; Fep+Alp: sum of oxalate-extractable Fe/Al oxides.

Figure 4?Relationships between microbial necromass C and soil physicochemical properties including soil temperature (a, e), soil moisture (b, f), soil pH (c, g) and SOC/TN (d, h), respectively. Blue and yellow symbols represent data points in the topsoil and subsoil, respectively. The solid cycles and triangles represent data points from alpine steppe (n = 22) and alpine meadow (n = 14), respectively. The solid lines were fitted by ordinary least-squares regressions, and the shadow areas correspond to 95% confidence intervals. * and ** represent significant level at P < 0.05 and P < 0.01, respectively. SOC: soil organic carbon; TN: total nitrogen.

Figure 5?Results of variation partitioning analyses illustrating the relative contribution of plant C input and mineral protection to microbial necromass C in the (a) topsoil and (b) subsoil. The retained mineral variables by stepwise regression model were Caexe and Feo+Alo in both the topsoil and subsoil. X1 and X2 indicate the pure effect of each type of variable, and X3 suggests the joint effect of two types of variables. Caexe: exchangeable Ca2+; Feo+Alo: sum of pyrophosphate-extractable Fe/Al oxides; Fep+Alp: sum of oxalate-extractable Fe/Al oxides.

Figure 6?Structural equation models (SEM) revealing the direct and indirect effects of biotic and abiotic factors on microbial necromass C in topsoil (a) and subsoil (b). Black and red solid arrows indicate positive and negative associations, respectively. Dotted lines represent pathways that are not significant. Numbers adjoining the arrows indicate significant standardized path coefficients. The arrow width is proportional to the strength of the association. The multiple-layer rectangles indicate the first component from the PCA of mineral and microbial properties, and the vertical arrows within it represent the positive relationships between adjacent variables and the corresponding PC1. SOC: soil organic carbon; TN: total nitrogen; PLFAs: phospholipid fatty acids; MBC: microbial biomass carbon. Caexe: exchangeable Ca2+; Feo+Alo: sum of pyrophosphate-extractable Fe/Al oxides; Fep+Alp: sum of oxalate-extractable Fe/Al oxides; *, P < 0.05; **, P < 0.01; ***, P < 0.001.

Figure 7?Standardized effects of each variables from the structural eaquation modelling (SEM) analysis. (a) and (b) represent standardized direct effects of plant C input and mineral protection in the top and subsoil, respectively; (c) and (d) correspond to standardized indirect effects of soil moisture, soil pH and and plant C input in the topsoil and subsoil, respectively. The values adjacent to the column represent the standardized coefficients in SEM.

結(jié)論

基于大規(guī)模調(diào)查和室內(nèi)分析相結(jié)合的方法，青藏高原高寒草地表層和深層土壤微生物殘?bào)w C對(duì)有機(jī)碳的貢獻(xiàn)率均高達(dá)40%。微生物殘?bào)wC的主導(dǎo)因素也與土壤深度有關(guān): 植物C輸入的作用隨著土壤深度的增加而減弱，而礦物保護(hù)的作用則隨著土壤深度的增加而增強(qiáng)。因此，本研究強(qiáng)調(diào)土壤深度之間微生物殘?bào)w碳的差異控制應(yīng)納入地球系統(tǒng)模型，以減少土壤碳動(dòng)態(tài)預(yù)測(cè)中的不確定性。本研究還表明，礦物保護(hù)似乎是控制深層土壤微生物殘?bào)wC長(zhǎng)期穩(wěn)定的關(guān)鍵機(jī)制，這可能會(huì)減緩氣候變化下潛在的正C-氣候反饋效應(yīng)。