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Using a Tropical Elevation Gradient to Evaluate the Impact of Land‐Use Intensity and Forest Restoration on the Microbial Use of Organic Matter Under Climate Change

Using a Tropical Elevation Gradient to Evaluate the Impact of Land‐Use Intensity and Forest... IntroductionMicrobes are the gate keepers of carbon (C), leaving the soil and entering the atmosphere via respiration (Davidson & Janssens, 2006; Wieder et al., 2015). Simultaneously, microbes regulate the input of C to soil by transforming plant products through biomass synthesis (i.e., microbial growth; Rousk, 2016) into soil C with longer retention times (“the microbial C pump”; Liang et al., 2017). Our ability to forecast the biological feedback to the terrestrial C cycle therefore strictly depends on our understanding of how microbial use of soil organic matter (OM) for respiration and growth is regulated (Bradford et al., 2016). It has long been recognized that the climate factors, temperature and moisture, are dominant environmental controllers of microbial process rates and thus soil C reservoirs (Davidson & Janssens, 2006; Jenny, 1994). The need to sustain the supply of resources including food and energy for growing human populations has led to an accelerating exploitation of land for crop production (FAO, 2011). This has led to the realization that after climate, land‐use factors are the second strongest set of controllers of microbial communities and processes (Jangid et al., 2011; Malik et al., 2018; Newbold et al., 2015; Peters et al., 2019) as land use integrates the influence of plant productivity and community composition, soil edaphic factors, fertilization, along with soil structure and disturbance regimes (Bunemann et al., 2018; Lauber et al., 2008; Postma‐Blaauw et al., 2010; van Groenigen et al., 2010; Weisser et al., 2017). With a rapidly changing climate coinciding with increasing pressures on soil resources exerted as land‐use intensification (IPCC, 2014), there is now an urgent need to elucidate how the interaction between changes in land use and climate will affect the microbial use of OM and the resulting contributions to the soil and atmospheric pools of C (Alexander et al., 2018; Peters et al., 2019). This need is most urgent in tropical and subtropical ecosystems since the rate of land‐use conversion affecting soil OM is fastest there (Le Quere et al., 2009). However, in contrast with the urgent need for information in tropical regions, the study of climate and land‐use intensity on soil microbial communities and processes is mostly restricted to arctic, boreal, temperate, and Mediterranean climates (Crowther et al., 2016; Le Quere et al., 2009; van Gestel et al., 2018) with very scarce observations reported from subtropical and tropical ecosystems (Powers et al., 2011).The historical transition of natural habitat in the Ethiopian highlands provides an interesting and valuable arena to investigate how microbial communities and functions are affected by land‐use intensity and climate change. In the highland plateaus of the Amhara region, comparable study sites along four levels of a steep climate gradient ranging from hot and arid lowland sites to moist and cool highland sites have been established and monitored (Assefa et al., 2017). Each of the sites include the land uses, (a) natural forests, (b) grasslands used for pasture, (c) tilled croplands used for intensive agriculture, and (d) exclosures, where natural forests are in the process of being restored from degraded agricultural soils. These conditions enable a “space‐for‐time substitution” design (Blois et al., 2013; Walker et al., 2010), where the impact of deforestation (forest to grassland) and intensification of agriculture (grassland to cropped agriculture) as well as that of the restoration of forests (cropland to exclosures) along a climate gradient can be used to infer how predicted climate warming, leading to a hotter and drier climate (IPCC, 2014), will interact with land‐use regimes in subtropical ecosystems. As with any large‐scale geographical climate gradient, the climate factor is to some degree confounded with plant community composition, soil formation, including texture and aggregation, and other factors that depend on climate. However, the added difficulty in teasing out causality is compensated by the relevance (Blois et al., 2013; Walker et al., 2010); following climate change, the assemblages of communities would dynamically respond, resulting in a composition shaped by the new climate (Blois et al., 2013), and soil formation factors would also align with those of the new climate with time (Weil & Brady, 2017).In this study, we set out to investigate how land use, climate, and their interaction affected the soil microbial community's use of OM for respiration and growth, together with the responses in nutrient cycling (gross N mineralization and nitrification). In addition to assessing the use of OM by the active microbial decomposer community, we also estimated the total microbial biomass size, the biomass of the major decomposer groups, bacteria and fungi, along with that of arbuscular mycorrhizal fungi (AMF). The composition these broad microbial groups is likely responsive to changes in climate aridity (Augé, 2001; de Vries et al., 2012; Mackie et al., 2019; Remke et al., 2021; Yuste et al., 2011) and land use (Lauber et al., 2008; Valyi et al., 2015) and produce biomass components likely to contribute to soil OM accumulation (Six et al., 2006). Finally, we evaluated the effects on microbial community structure resolved using the composition of microbial phospholipid fatty acids (PLFAs). To specifically assess how the historical variation in climate and land use had shaped microbial communities and process rates, we assessed soils from all sites under standardized conditions in the laboratory, thus circumventing the direct influence of differences due to soil moisture or temperature (Rousk et al., 2013). As such, we assessed the “legacies” of climate and land use on the structure and functioning of the soil microbial community (de Nijs et al., 2019; Göransson et al., 2013; Hawkes et al., 2017; Wallenstein & Hall, 2012).We hypothesized that ecosystem aridity would (H1) decrease microbial growth and biomass (Crowther et al., 2019), (H2) increase the relative importance of fungal decomposers (Yuste et al., 2011), (H3) increase the relative dominance of AMF due to a greater plant need for AMF‐facilitated water acquisition (Augé, 2001; Remke et al., 2021), and (H4) decrease respiration, gross N mineralization and nitrification (Larsen et al., 2011). We also hypothesized that (H5a) deforestation would reduce microbial biomass, growth rates, and the mineralization rates of C and N, while (H5b) reforestation (exclosures) would reverse the process (Holden & Treseder, 2013; Mussa et al., 2017). Further, we predicted that (H6) intensification of agriculture would stimulate gross nitrification rates (Norton & Ouyang, 2019). Most importantly, we hypothesized that (H7) the reduction of microbial community size and functions by land‐use intensification would be accelerated in a warmer and drier compared to a cooler and more humid climate (Smith et al., 2016).Materials and MethodsStudy Sites and Soil SamplingThe studied sites were selected from previously established study sites in the Amhara National Regional State (Assefa et al., 2017), located in north‐western Ethiopia. Although Ethiopia is located in the tropics, the climate of the study areas varies due to differences in altitude (Figure 1), generating a climate gradient from 2,200 mm year−1 mean annual precipitation (MAP) and 16°C mean annual temperature (MAT) at the highest altitude site to 1,050 mm year−1 MAP and 20°C at the lowest altitude site (Table 1). This altitudinal‐based climate gradient is henceforth referred to by the level of aridity or humidity, which integrates both MAP and MAT, ranging from the most humid ecosystem in Katassi (MAP = 2,200 mm year−1, MAT = 16°C), with soils classified as Luvisols, via intermediate altitude sites with progressively warmer and drier climates in Gelawdiwos (MAP = 1,200 mm year−1, MAT = 18°C) and Tara Gedam (MAP = 1,100 mm year−1, MAT = 19°C), both with soils classified as Cambisols, to the most arid conditions in the lowest altitude site Ambo Ber (MAP = 1,050 years−1, MAT = 20°C), with soils classified as a Leptosol. These combinations of rainfall and temperature resulted in potential evapotranspiration (PET) ranging from 2,800 to 3,480 mm year−1. The aridity index (AI), that is, the ratio of MAP to PET, where higher values indicate more humid conditions, ranged from 0.79 in Katasi to 0.30 in Ambo Ber (Table 1), translating to ecosystem aridity (estimated as 1‐AI, Maestre et al., 2015) levels ranging from 0.21 in Katasi to 0.70 in Ambo Ber (Table 1). The area's rainfall distribution is unimodal with most precipitation occurring during June‐September, while the dry months are from October to May, resulting in dry winters and wet summers (Peel et al., 2007). The four selected sites had an average distance of approximately 120 km between them.1FigureThe distribution of study sites along a climate gradient linked to altitude in the Amhara region in Northwest Ethiopia.1TableClimate and Edaphic Characteristics of the Sampled SitesCoordinatesMAPaMATaPETaETaAIbEcosystem aridity metriccStudy siteLand useSoil pHElectrical conductivity (μS cm−1)SOM (%)NH4 (μg NH4‐N g−1)NO3 (μg NO3‐N g−1)MeanSEMeanSEMeanSEMeanSEMeanSE12°31ʹ15″ N, 37°31ʹ53″ E1,050203,4803600.300.70Ambo BerCrop6.60.081968210.11.040.490.063.261.15Exclosure6.70.101886610.90.460.750.095.752.26Forest6.60.083164720.01.050.780.099.684.14Grassland6.50.0946433516.01.020.850.123.603.5312°8ʹ47″ N, 37°44ʹ45″ E1,100192,9303600.380.62Tara GedamCrop6.40.131484610.60.180.720.042.221.28Exclosure6.40.031871007.00.768.302.693.161.14Forest6.20.2166136629.34.971.400.329.850.82Grassland6.30.292397413.20.800.590.276.522.2211°38ʹ25″ N, 37°48ʹ55″ E1,220182,8703800.430.57GelawdiwosCrop6.00.131878511.00.270.660.032.320.65Exclosure5.90.112378613.00.623.092.222.491.04Forest6.20.2541219122.81.420.490.1410.413.62Grassland5.70.1327515615.20.963.201.474.901.7511°0ʹ05″ N, 36°44ʹ8″ E2,200162,8008000.790.21KatasiCrop4.90.1340220812.21.121.340.373.432.08Exclosure5.30.112438414.71.422.841.476.052.57Forest5.80.2438713624.81.720.910.0510.524.03Grassland5.10.1354737015.90.850.970.095.592.23ANOVA resultsLUp = 0.02NSp < 0.0001p = 0.0003NSSitep < 0.0001NSp = 0.0004p = 0.04NSLU × sitep = 0.04NSp < 0.0001p = 0.001NSaMean annual precipitation (MAP) and mean annual temperature (MAT) are 5‐year averages and were taken from closest meteorological stations and were provided by the Ethiopian National Meteorology Agency. Potential Evapotranspiration (PET) and evapotranspiration (ET) are estimates provided by the NASA's Moderate Resolution Imaging Spectroradiometer via the Evapotranspiration Web Viewer, University of Montana, USA.bThe aridity index (AI) is the ratio of MAP to PET, where a value of 1 indicated that MAP matches the moisture demand of the system with lower values indicating increasing aridity.cThe ecosystem aridity metric is (1‐AI), where zero indicates a system where MAP meets the MAP demand and higher values indicate drier ecosystems.At each of the climate sites (Table 1), four randomly assigned plots 20 by 20 m areas were selected with similar topography, edaphic, and climate conditions for each of four land uses, including (a) natural forest, (b) grassland used as pasture, (c) cropland, and (d) exclosure (rehabilitation of woodland on former degraded pasture and cropland). The four independent plots of each land use within each climate site were treated as independent replicates. The forests of these areas are dry Afromontane remnant pristine forests composed mostly of a mixture of indigenous tree species, which are protected by local institutions and mostly confined to sacred groves associated with churches and monasteries (Aerts et al., 2016). The exclosures were established on former grazing land and are used to rehabilitate degraded land by protecting the land from further animal grazing and human interference. In addition to grazing prevention, enrichment planting of seedlings of indigenous and some exotic tree species has been carried out. In the exclosures, local people are still allowed to use grass (cut‐and‐carry‐systems) for fodder and to collect fuelwood. Since establishment (1985 for Katassi, Galawdios and Tara Gadam, 2007 for Ambo Ber), natural revegetation has occurred and the land areas are now covered by an extensive woodland dominated by bushes. The pastures are used as a common grazing land to feed herds of cattle and small ruminants including sheep and goats, and equines, such as donkeys. The croplands and pastures were all converted from natural forests within the last 50 years. Croplands are ox‐plowed to a tillage depth of 10–20 cm, and crop rotations including Eragrostis tef, Eleusine coracana, Sorghum bicolor, Zea mays, Triticum aestivum, Guizotia abyssinica, and Vicia faba are grown during the rainy season. Between years, crop rotation is a common practice.Soil samples were collected in the early wet season in June 2017. Within each plot, 9–12 small soil pits (0–10 cm depth) were combined into composite samples maintaining the four independent field replicates (4 land uses each with 4 replicates, in each of 4 climate sites, totaling 64 samples). The replicate plots were >100 m apart to ensure spatial independence. The air‐dry soil samples were stored in double ziploc bags that were shipped express to the Lund University in Sweden for analysis within 14 days of sampling.Soil Physiochemistry, Moisture Adjustment, and PreincubationOn arrival, all soil samples were sieved (<4 mm). Soil subsamples were used to measure gravimetric soil water content (105°C to constant mass) and soil OM content through loss on ignition (600°C for 12 hr). Soil pH and electrical conductivity were measured in a 1:5 (w/v) soil:H2O solution using a pH meter and an electrical conductivity meter, respectively. The maximum water holding capacity (WHC) was measured as previously described (Hicks et al., 2018). All soil samples were then adjusted to optimal moisture for microbial activity at 50% of the maximum WHC, which was confirmed by gravimetric soil moisture determination, before being left to preincubate during 14 days at 18°C in the dark until microbial process rates were assessed (see below).Soil Respiration and SIR‐Microbial BiomassOne gram soil was weighed into 20‐ml glass vials. The head space of glass vials was purged with pressurized air before vials were sealed and incubated at 18°C for 18 hr. The amount of CO2 produced during the incubation was determined using a gas chromatograph equipped with a methanizer and flame ionization detector. Substrate‐induced respiration (SIR) was measured as a proxy for microbial biomass. Briefly, 15 mg of 4:1 glucose:talcum was vigorously mixed into 1.0 g soil (corresponding to 4.8‐mg glucose‐C g−1 soil fwt). After 30 min, vials were purged with pressurized air and incubated at 22°C for 2 hr before the concentration of CO2 was determined. SIR was used to estimate microbial biomass (mg C g−1; Anderson & Domsch, 1978).Gross N Mineralization and Nitrification, and Soil NH4+ and NO3− ConcentrationsGross N mineralization and gross nitrification rates were determined using the 15N pool‐dilution method (Rousk et al., 2016). Briefly, two subsamples of each soil (each 15.0 g fwt) were weighed into 100‐ml plastic pots to which 115 μl of NH4Cl (45 μg N ml−1, enriched to 1 atom% 15N) was administered. The soils were vigorously mixed and then lidded. Soil NH4+ $\mathrm{N}{{\mathrm{H}}_{4}}^{+}$ and NO3− $\mathrm{N}{{\mathrm{O}}_{3}}^{-}$ were extracted using 1M KCl solution and then isolated from the extract by diffusion to acidified glass fiber traps (according to standard procedures; International Atomic Energy Agency [IAEA], 2001). One set of subsamples was extracted approximately 1 hr after 15N addition, while the second set was treated identically after 24 hr incubation at 18°C without light. The amount of NH4+ $\mathrm{N}{{\mathrm{H}}_{4}}^{+}$‐N and NO3− $\mathrm{N}{{\mathrm{O}}_{3}}^{-}$‐N was determined by isotope‐ratio mass spectrometry (IRMS), and the 15N/14N content of the glass fiber traps was measured with a Flash 2000 elemental analyzer coupled to a Delta V plus via the ConFlow interface (Thermo Fisher Scientific, Germany). Gross N mineralization, gross NH4+ $\mathrm{N}{{\mathrm{H}}_{4}}^{+}$ consumption, and gross nitrification rates were estimated according to the equations previously described (Bengtson et al., 2005), assuming that gross N mineralization matched gross immobilization (i.e., concentrations of NH4+ $\mathrm{N}{{\mathrm{H}}_{4}}^{+}$ did not change during the incubation).Bacterial and Fungal GrowthBacterial growth was determined by measuring the rate of 3H‐Leucine (Leu) incorporation into extracted bacteria (Bååth et al., 2001; Rousk et al., 2009). One gram of fresh soil was mixed with 20‐ml demineralized water, vortexed for 3 min, and centrifuged (10 min at 1,000 g). The resulting bacterial suspension was incubated at 18°C for 2 hr with 2 μl 1‐[4,5‐3H]‐Leucine (5.7 TBq mmol−1, Perkin Elmer, USA) and unlabeled Leu with a final concentration of 275 nM Leu in the bacterial suspension. Bacterial growth was terminated after 2 hr by adding 75 μl of 100% trichloroacetic acid, followed by centrifugation and washing (Bååth et al., 2001). Scintillation cocktail (Ultima Gold; PerkinElmer, USA) was added and the radioactivity was measured using a liquid scintillation counter. The amount of leucine incorporated into extracted bacteria (pmol Leu incorporated g−1 h−1) was used as a measure of bacterial growth.Fungal growth was determined using the acetate‐in‐ergosterol (Ac‐in‐erg) incorporation method (Newell & Fallon, 1991) adapted for soil (Bååth, 2001; Rousk et al., 2009), which estimates the rate of ergosterol synthesis as a measure of fungal growth. One gram of soil was mixed with 20 μl of 14C‐acetate solution ([1–14C] acetic acid, sodium salt, 2.07 GBq mmol−1, Perkin Elmer) and unlabeled sodium acetate, resulting in a final acetate concentration of 220 μM in the soil slurry. Samples were incubated at 18°C for 4 hr in the dark before the growth was terminated by the addition of formalin. Ergosterol and incorporated acetate were measured according to Rousk and Baath (2007). The amount of acetate incorporated into ergosterol (pmol g−1 h−1) was used as a measure of fungal growth.Microbial Community PLFA and NLFA Concentrations and StructuresThe PLFA composition from a 1.0‐g fresh soil subsample was determined according to Frostegard et al. (1993) with modifications (Cruz‐Paredes et al., 2017). An internal standard (methyl nonadecanoate fatty acid 19:0) was added before the methylation step for quantification. The total PLFA concentration was used as a measure of total microbial biomass, while bacterial‐ and fungal‐specific PLFA markers were summed to estimate the relative biomass of fungi and bacteria (Frostegård & Bååth, 1996). To estimate the biomass of AMF, we used the NLFA marker 16:1ω5 (Olsson et al., 1995). The relative abundance (mol%) of the 25 identified PLFAs was used to screen for the effects of climate and land use (see below) on the microbial community structure (Frostegard et al., 1993).Statistics and CalculationsThe measured variables were first log‐transformed to meet the assumption of equal variances and then subjected to two‐way analysis of variance (ANOVA), considering the factors, climate‐site (4 levels), land use (4 levels), as well as their interaction. To determine differences among the factor and interaction levels, Tukey's HSD pairwise comparisons (at p < 0.05) were used. To test for interlinkages between measured variables, linear regressions were used. Relationships with soil OM content were also tested using linear regressions, assuming that soil OM was the independent variable.A principal component (PC) analysis was used to screen for differences in the PLFA composition of the soil microbial community, using relative abundances (mol%) after standardizing to unit variance. The scores of the PCs were subjected to two‐way ANOVA (as above) and the variable loadings were used to interpret which PLFA markers drove the separation of the PCs (Frostegard et al., 1993).ResultsSoil PropertiesSoil properties varied both due to differences in climate between sites and due to differences in land use within sites (Table 1). While there was no effect of land use or climate for electrical conductivity, the soil pH differed between sites (p < 0.0001) being most acidic in the cool, humid Katasi site (pH 5.6) and increasing with site aridity to have highest values in the Ambo Ber site (pH 6.6). Land use also mildly influenced soil pH (p = 0.02) with slightly higher pH in forests and exclosures compared with croplands and grassland (Table 1). The climate also interacted with land use (p = 0.04), such that land use resulted in larger pH differences in cool, humid, compared with arid, hot sites (Table 1). Soil OM content was also strongly affected by the climate of the site (p = 0.0004) and by land use (p < 0.0001) with especially pronounced differences among land uses in some sites (p < 0.0001; Table 1). Overall, the soil OM content was higher in humid and lower in more arid sites and differed systematically between land uses being highest in the forests, second highest in grasslands, and similarly low in croplands and exclosures (Table 1). The concentration of NH4+ $\mathrm{N}{{\mathrm{H}}_{4}}^{+}$ varied between land uses (p = 0.0003), being higher in exclosures than other land uses, and was marginally distinguishable between sites (p = 0.04) with a significant interaction term (p = 0.001; Table 1). While NO3− $\mathrm{N}{{\mathrm{O}}_{3}}^{-}$ concentrations were variable, no systematic differences with climate or land use were detected. NH4+ $\mathrm{N}{{\mathrm{H}}_{4}}^{+}$ concentrations were not linked to soil OM content (p > 0.2); however, NO3− $\mathrm{N}{{\mathrm{O}}_{3}}^{-}$ concentrations increased with higher soil OM (p < 0.0001, R2 = 0.27).Microbial Growth Rates and RespirationFungal growth did not vary between sites, but did vary with land use (p = 0.0009), with highest growth rates in forest, intermediate rates in exclosure and grassland, and lowest rates in croplands (Figure 2a). Bacterial growth had highest levels in the dry and lowest values in the wettest site (p < 0.0001). Forests had highest values, grassland intermediate, and the lowest rates of bacterial growth were found in exclosures and croplands (p = 0.0006, Figure 2b). This resulted in a fungal‐to‐bacterial growth ratio that was unaffected by land use, but was lowest in the most arid site and highest in the least arid site (Figure 2c). Respiration increased with higher aridity (p < 0.0001) and also decreased from forest via exclosure and grassland to be lowest in croplands (p < 0.0001) with an interaction (p = 0.004) where the land‐use effect was stronger in dry sites (Figure 2d). Differences in both fungal (p = 0.0001, R2 = 0.26) and bacterial growth rates (p < 0.0001; R2 = 0.29) were positively linked to soil OM content, leading to a fungal‐to‐bacterial (F/B) growth ratio unrelated to soil OM (p = 0.73). Respiration was strongly positively linked with soil OM (p < 0.0001; R2 = 0.53).2FigureThe effect of land use (LU) and climate on rates of (a) fungal growth, (b) bacterial growth, (c) fungal‐to‐bacterial growth ratio, and (d) respiration. Results from two‐way analysis of variance (ANOVA), including the factors LU and site along with their interaction, are shown for each microbial variable, and significant factor differences from the ANOVA are accompanied by Tukey HSD pairwise comparisons between sites (capital letters) and between LUs (lowercase letters by the legend). Presented values are the mean ±1 SE.Nitrogen TransformationsGross N mineralization rates were highest in forest, intermediate in croplands and grassland, and lowest in exclosures (p < 0.01) and had lower values in the 1,220 mm site (the second most humid) compared to the three others (p = 0.003; Figure 3a). Gross nitrification was highest in croplands, intermediate in exclosure and grassland, and lowest in forests (p = 0.0002) and decreased with higher aridity of the site (p = 0.002; Figure 3b). This resulted in a ratio of soil C mineralization to gross N mineralization that was higher in forest (c. 12 μg C/μg N), grassland (c. 11 μg C/μg N), and exclosures (c. 10 μg C/μg N) compared to croplands (c. 3 μg C/μg N; p < 0.0001). The ratio of C mineralization to gross N mineralization was also higher in the 1,220 mm (c. 14 μg C/μg N) and 1,100 mm (c. 10 μg C/μg N) sites, intermediate (c. 9 μg C/μg N) in the 1,050 mm site, and lowest (c. 4 μg C/μg N) in the 2,200 mm site (p = 0.003). The variations in gross N mineralization (p = 0.005, R2 = 0.10) and gross nitrification (p = 0.019, R2 = 0.07) were weakly negatively linked to soil OM content, while there was no relationship between the ratio of C mineralization to gross N mineralization and soil OM (p = 0.29).3FigureThe effect of land use (LU) and climate on (a) gross N mineralization and (b) nitrification. Results from two‐way analysis of variance (ANOVA), including the factors LU and site along with their interaction, are shown for each microbial variable, and significant factor differences from the ANOVA are accompanied by Tukey HSD pairwise comparisons between sites (capital letters) and between LUs (lowercase letters by the legend). Presented values are the mean ±1 SE.Microbial Biomass and Community CompositionSoil microbial biomass C measured by SIR was highest in forest and lowest in croplands (p < 0.0001) and had higher levels in the driest site compared to the others (p < 0.0001; Figure 4a). The total PLFA concentration, as well as fungal and bacterial PLFA concentrations, showed similar patterns with highest levels in forests and lowest in cropland, and highest values in humid and lowest in arid sites (all p < 0.0001; Figures 4b–4d). These patterns resulted in a fungal‐to‐bacterial PLFA ratio, which was higher in croplands and exclosures compared with grasslands and forests (p = 0.0002), and higher in dry compared to wet sites (p = 0.0002; Figure 4e). The NLFA marker for AMF had higher values in the driest site (p = 0.0028; Figure 4f), but was unaffected by land use. All estimates of microbial decomposer biomass were positively correlated to soil OM content (all p < 0.0001; R2 = 0.73, 0.68, 0.51, and 0.69 for SIR biomass, total PLFA, fungal PLFA, and bacterial PLFA concentrations, respectively). In contrast, the fungal‐to‐bacterial PLFA concentration was weakly negatively linked to soil OM content (p = 0.014; R2 = 0.08), while the NLFA 16:1w5 showed no relationship with soil OM content (Figures 4e and 4f).4FigureThe effect of land use (LU) and climate on (a) microbial biomass measures as substrate‐induced respiration‐biomass, (b) total phospholipid fatty acid (PLFA) concentration, (c) fungal PLFA concentration, (d) bacterial PLFA concentration, (e) the fungal‐to‐bacterial PLFA ratio, and (f) the arbuscular mycorrhizal fungi NLFA concentration. Results from two‐way analysis of variance (ANOVA), including the factors LU and site along with their interaction, are shown for each microbial variable, and significant factor differences from the ANOVA are accompanied by Tukey HSD pairwise comparisons between sites (capital letters) and between LUs (lowercase letters by the legend). Presented values are the mean ±1 SE.About 45% of the total variation of the microbial PLFA composition could be explained by two PCs (Figure 5). Microbial PLFA composition was strongly influenced by both land use and site with low PC1 scores associated with forests and exclosures and high PC1 scores associated with croplands and grasslands (p < 0.0001), while positive PC2 scores were associated with forests, grassland and exclosures were associated with intermediate scores, and negative PC2 scores were associated with croplands (p < 0.0001). There was a weak positive link between PC2 scores and the aridity of the site (ANOVA p = 0.016, Figure 5a). Soil pH was positively linked to PC1 scores (linear regression, p < 0.0001, R2 = 0.33) with high scores being linked to 16:1ω5, 18:1ω9, and 18:1ω7 and low scores with the 10Me17:0, 10Me18:0, and cy19:0 (Figure 5b). Soil OM content was positively linked to PC2 scores (p < 0.0001, R2 = 0.35) and associated with low concentrations of monounsaturated PLFA markers and the fungal marker 18:2ω ethiopian siteseth 6,9 (Figure 5b).5FigureThe effect of land use (LU) and climate on the microbial phospholipid fatty acid (PLFA) composition shown as (a) the principal component scores showing the level of PLFA composition similarity and (b) variable loadings showing which PLFA markers that drove that variation in the four different LUs (symbols) in the four different climates (colors). Results from two‐way analysis of variances on PC1 and PC2, including the factors LU and site along with their interaction, are shown. Presented values are the mean ±1 SE.DiscussionMicrobial Community Structural and Functional Dependence on ClimateIn a global survey of drylands (i.e., ecosystems with AI < 0.65), the abundance and diversity of soil decomposer bacterial and fungal communities decreased with higher ecosystem aridity (Maestre et al., 2015). We find diverging patterns from these in our survey of high‐altitude subtropical sites, most of which could be categorized as drylands (i.e., AI < 0.65). To specifically resolve the impact of the different legacies of climate in the studied ecosystems on soil microbial community and functions, all assessments of microbial variables were conducted under controlled moisture and temperature (Hicks et al., 2018; Rousk et al., 2013). Our estimates of total microbial biomass—both total PLFA concentrations and SIR‐biomass—increased slightly with higher aridity. The increase in fungal biomass was particularly pronounced (Figure 4c), resulting in an increasing fungal‐to‐bacterial PLFA ratio in more arid systems (Figure 4e). We also resolved differences in microbial growth rates under controlled temperature and moisture conditions, which more closely correspond to microbial contributions to ecosystem functioning than do biomass concentrations (Liang et al., 2017; Rousk, 2016; Zheng et al., 2019). Bacterial growth increased with higher ecosystem aridity, while fungal growth was independent (Figures 2a and 2b), resulting in a substantial decrease in the ratio of fungal‐to‐bacterial growth in more arid climates (Figure 2c). Thus, the pattern for subtropical Ethiopian sites diverged from that reported in a large‐scale survey of drylands (Maestre et al., 2015) with our study showing consistent increases in both microbial biomass and growth rates with higher aridity (H1). This finding is surprising but may be explained by drought legacy effects on the lability and availability of C to microbes (Hicks et al., 2018; Leizeaga et al., 2021). This could be partly driven by the molecular‐scale physiochemical interactions with mineral surfaces and physical protection of OM (Cotrufo et al., 2013; Lehmann & Kleber, 2015; Schmidt et al., 2011), where drier conditions may have preserved OM until the moisture adjustments made in our assessment of legacy effects. We also found evidence for an increased relative abundance of fungal biomass in arid systems, but a reduction in the relative growth rate under moist conditions (H2). This finding is consistent with earlier reports of higher fungal resistance to drought observed in field experiments both in arid (Leizeaga et al., 2021; Yuste et al., 2011) and humid ecosystems (de Vries et al., 2012) and in dry compared to wet seasons in subtropical highlands (Ahmed et al., 2019) as well as in laboratory experiments (Bapiri et al., 2010; Gordon et al., 2008). It also highlights that the size of microbial communities does not unambiguously reflect the active contribution to processes in soils since the status of the resolved biomass is unknown (having a range of possible levels of activity, being dormant, or even dead), thus contrasting with estimates of growth (Blagodatskaya & Kuzyakov, 2013; Rousk, 2016). AMF associations have long been known to benefit plant survival and productivity during drought (Augé, 2001). In support of our hypothesis (H3), the abundance of AMF increased with higher aridity (H3), presumably reflecting an increased reliance by plants on water acquisition via the AMF symbiosis. Taken together, our findings suggest that a legacy of drought increased the size of the soil microbial food web with a higher fraction of fungi, and that this shift coincided with a slower turnover of the microbial community, as indicated by the ratio of the biomass pool to the rate of microbial growth that we measured. It is possible that the maintenance of a large fungal biomass in arid conditions could be linked to the greater capacity of the fungal mycelial growth form to redistribute patchy resources in a dry soil matrix (Augé, 2001; Guhr et al., 2015), physiological differences in abilities to withstand large negative water potentials (Harris, 1981; Schimel, 2018), or a more flexible fungal stoichiometry (Strickland & Rousk, 2010), but more work is needed to resolve the specific causes. A fungal‐dominated food web has been proposed to promote the stabilization of both C and nutrients (Wardle et al., 2004) in agricultural ecosystems (Six et al., 2006) as well as in forests (Clemmensen et al., 2013), consistent with the here reported reduced microbial turnover rate. If our assessments of microbial communities and functions at standardized temperature and moisture can be extrapolated back to capture the biogeochemistry in the ecosystems they were sampled from, the ecosystem legacy of arid conditions should further amplify the C sequestration effects. Running counter to these effects, input of OM by plant productivity was likely negatively dependent on ecosystem aridity (Liu et al., 2018), which may explain why the belowground responses have not yet resulted in detectable increases in OM concentrations.We found systematic differences in microbial PLFA composition associated with ecosystem aridity, where more arid systems were orientated toward low PC1 scores and humid systems toward high PC1 scores (Figure 5a). The community differences were pronounced, as indicated by the large fraction of variation in the relative abundance composition of PLFAs explained by PC1, which matched earlier reports from pH and fertility gradients known to be some of the strongest drivers of microbial community composition (Högberg et al., 2007; Nilsson et al., 2007). In line with previous studies, PLFA marker separation was also driven by biomarker lipids shown to be linked with high and low pH, including cy19:0 (low pH) and PLFAs 16:1ω5 and 18:1ω7 (high pH; Rousk et al., 2010). Indeed, the variation in PC1 scores was closely linked with pH (P = 0.0001, R2 = 0.33).The observed differences in microbial communities, growth rates, and structures coincided with higher respiration rates in more arid climates (Figure 2d), while gross N mineralization rates did not change systematically with humidity (H4). This decoupling of C and nutrient cycling resulted in a ratio of C mineralization to gross N mineralization ranging from 10 to 14 μg C/μg N in the more arid sites to be far lower, at about 4 μg C/μg N, in the most humid site. Gross rates of C and N mineralization normalized to the resource availability (i.e., OM content) can be used to infer the nutrient composition of used substrate and therefore the microbial availability of C and nutrients (Craine et al., 2010; Fierer et al., 2005, 2006). As such, the emerging patterns would suggest that the C and N available in the OM used by microorganisms, as proxied under controlled moisture and temperature, shifted from being more nutrient‐rich (mineralization C/N = 4) in humid sites (and therefore more C limited) to being more nutrient‐poor (C/N = 10 – 14) in the more arid sites (and therefore more nutrient limited). A large‐scale survey of soils from an aridity gradient in northern China that targeted microbial enzyme stoichiometry to infer the microbial regulation of C and N cycles found that the ratio of enzymes targeting C compared to those that targeted N decreased with higher aridity (Feng et al., 2019). This also suggests more nutrient limited microbes in arid systems despite smaller OM reservoirs, highlighting a need for more work to elucidate the underlying mechanisms for this counterintuitive finding.Microbial Community Structural and Functional Dependence on Land UseThere are documented links between agricultural intensification and decreasing microbial diversity, and changing community structures (Turley et al., 2020). Similar to the influence of site aridity, we also found that land use systematically affected the microbial PLFA composition with forest and cropland land uses being diametrically most dissimilar in community composition, with lower intensity agriculture in the form of grassland for grazing, and the recovery of croplands within exclosures, falling in between (Figure 5a). The resulting effect of land use on community structure seemed to match that driven by site aridity and was well correlated to soil pH and OM content, consistent with the impacts of land‐use intensity previously observed in both temperate (Turley et al., 2020) and tropical environments (Berkelmann et al., 2020). It has previously been shown that the conversion of natural forests and woodlands into agricultural land is detrimental for soil C, nutrient and microbial biomass stocks, as well as for microbial functions in tropical (Paul et al., 2010) and subtropical soils (Brackin et al., 2013; Tosi et al., 2016). In addition, forest restoration and reforestation practices have been shown to at least partly recover soil C and nutrient stocks together with the microbial community size and functions (Delelegn et al., 2017; Paul et al., 2010). The reversal of the reduction of soil C and nutrient pools by deforestation induced by afforestation strengthens the causal influence by the current land‐use practice on soil resources although the recovery of soil C and nutrient stocks is only partial within years to decades. Our results match these previous reports (H5). The total size of microbial communities was far larger in forest land uses at all climates, while the transition into agricultural use reduced the microbial community size by 4–20 fold (Figure 4). Surprisingly, the relative size of the fungal community compared to the bacterial community was not reduced by conversion to agriculture, but instead increased marginally (Figure 4e). The AMF abundance was also unaffected by land use (Figure 4f). Our results therefore contrast with reports of differences in relative AMF abundances based on spore counts in similar systems that have shown decreases induced by land‐use intensification (Delelegn et al., 2017). Naturally, to some extent, comparisons between data sets risk ambiguity, since the intensity of land use is relative, and it is possible that earlier reports included other intervals of the elusive parameter “land‐use intensity” (see discussion in Fischer et al., 2010). However, the range of land uses we consider here cover the range of current practices in the studied region, providing relevance. Collectively, our findings suggest that the reversal of agricultural conversion consistently resulted in a trend for recovery of microbial community sizes (Figure 4) with slower turnover (Figure 2), which in the long term should lead to replenished stocks of soil C (Liang et al., 2017; Six et al., 2006).Agricultural land conversion from forests decreased rates of fungal and bacterial growth as well as respiration rates (H5; Figure 2) similar to the changes observed for microbial biomass (Figure 4), albeit with slightly smaller effect sizes. These changes suggested a higher level of active biomass (Blagodatskaya & Kuzyakov, 2013), following conversion to agricultural land use, including manure addition practices, and thus faster C and nutrient turnover rates (Wardle et al., 2004). This indication of enhanced soil fertility was also consistent with the smaller reduction of gross N mineralization (Figure 3a) compared with that for respiration (Figure 2d), reducing the ratio of C mineralization to N mineralization from about 12 to 3 and suggesting a shift in microbial resource use from relatively nutrient‐poor to nutrient‐rich OM (Hicks et al., 2021; Murphy et al., 2015; Rousk et al., 2016). These shifts in microbial resource availability also stimulated gross nitrification rates (H6; Figure 3b). Similar to the microbial biomass results, the reversal of the agricultural conversion (i.e., exclosure treatments) consistently resulted in a trend for partial recovery toward the state in the forests.Impact of Land‐Use Intensity and Forest Restoration on Microbial Functions Under Climate ChangeThere is an urgent need to determine how the parallel threats of land‐use intensification and climate change will interact (Alexander et al., 2018; Peters et al., 2019), especially in understudied tropical and subtropical ecosystems, where the rate of land‐use conversion affecting soil OM is fastest (Le Quere et al., 2009). We found no interactions between the legacy of climate and land use for the microbial community structure, and only marginal effects on the microbial community size, lacking a consistent pattern that we could link to ecosystem aridity (Figure 4). We also found similar patterns for the microbial functions assessed under controlled temperature and moisture conditions. Bacterial and fungal growth rates were unaffected by the interaction between drought legacy and land use. However, the influence by land use on respiration increased with higher ecosystem aridity (Figure 2d), suggesting that the rates of OM turnover were more impacted by land use in drier climates (H7). These patterns were consistent both for the reduction in respiration induced by increased intensity of agricultural use (i.e., forest → cropland) as well as the increase in respiration induced by land restoration (i.e., cropland → exclosures), further strengthening the causal link to the land‐use factor.The most likely explanation for the observed interaction between the effects of differences in land use and those of ecosystem aridity on respiration is the concentration of OM, providing the substrate pool that governs the flux of C from soil (Parton et al., 1993; Wardle & Ghani, 1995). The link between respiration and soil OM content was relatively strong (R2 = 0.53) and comparable to global ecosystem surveys, including many sites (e.g., Delgado‐Baquerizo et al., 2013; Maestre et al., 2015), and exceeded the variation explained by bacterial growth, fungal growth, or N turnover processes. However, for the same reason, it is also expected that the size of the microbial biomass is determined by the soil OM pool size (Fierer et al., 2009; Wardle & Ghani, 1995), and while the correlation with soil OM content was even stronger for microbial biomass than that for respiration, there was no systematic correlation between land use and system aridity as shown by the lack of interaction between factors.Our findings suggest that land use will impact the decomposition of soil OM and plant nutrient provisioning more intensively during wet seasons in systems with a legacy of drier conditions, and while part of this variation may be driven by an OM pool size, it also appears to be driven by differences in the quality of OM, which was more impacted by the legacy of land use in drier climates. Surprisingly, the differential responses in the mineralization of OM in soils with different climate legacies did not coincide with shifts in the balance between fungal and bacterial decomposers in terms of growth rates or biomass. Forecasting from these observations, we can expect that the drier subtropical climates expected as a result of climate change (IPCC, 2014) will exacerbate the negative effects of land‐use intensification on soil OM turnover and the provisioning of nutrients for plants during the wet seasons when productivity is highest.AcknowledgmentsThe authors thank the Amhara Regional Agricultural Research Institute (ARARI) for logistical support and local land owners for access to their land, which enabled this study. This study was supported by the “Sustainability and Resilience Program” supported by the Swedish Research Council (Vetenskapsrådet), the Swedish Research Council Formas, and the Swedish International Development Cooperation Agency (VR Grant No. 2016‐06327), and from the Knut and Alice Wallenberg Foundation (Grant No. KAW 2017.0171).Data Availability StatementAll data used in this study are original and are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions linked with local land‐owner agreements.ReferencesAerts, R., Van Overtveld, K., November, E., Wassie, A., Abiyu, A., Demissew, S., et al. (2016). Conservation of the Ethiopian church forests: Threats, opportunities and implications for their management. The Science of the Total Environment, 551, 404–414. https://doi.org/10.1016/j.scitotenv.2016.02.034Ahmed, I. U., Mengistie, H. K., Godbold, D. L., & Sanden, H. (2019). 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Using a Tropical Elevation Gradient to Evaluate the Impact of Land‐Use Intensity and Forest Restoration on the Microbial Use of Organic Matter Under Climate Change

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Wiley
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© 2022. The Authors.
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0886-6236
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1944-9224
DOI
10.1029/2021gb007109
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Abstract

IntroductionMicrobes are the gate keepers of carbon (C), leaving the soil and entering the atmosphere via respiration (Davidson & Janssens, 2006; Wieder et al., 2015). Simultaneously, microbes regulate the input of C to soil by transforming plant products through biomass synthesis (i.e., microbial growth; Rousk, 2016) into soil C with longer retention times (“the microbial C pump”; Liang et al., 2017). Our ability to forecast the biological feedback to the terrestrial C cycle therefore strictly depends on our understanding of how microbial use of soil organic matter (OM) for respiration and growth is regulated (Bradford et al., 2016). It has long been recognized that the climate factors, temperature and moisture, are dominant environmental controllers of microbial process rates and thus soil C reservoirs (Davidson & Janssens, 2006; Jenny, 1994). The need to sustain the supply of resources including food and energy for growing human populations has led to an accelerating exploitation of land for crop production (FAO, 2011). This has led to the realization that after climate, land‐use factors are the second strongest set of controllers of microbial communities and processes (Jangid et al., 2011; Malik et al., 2018; Newbold et al., 2015; Peters et al., 2019) as land use integrates the influence of plant productivity and community composition, soil edaphic factors, fertilization, along with soil structure and disturbance regimes (Bunemann et al., 2018; Lauber et al., 2008; Postma‐Blaauw et al., 2010; van Groenigen et al., 2010; Weisser et al., 2017). With a rapidly changing climate coinciding with increasing pressures on soil resources exerted as land‐use intensification (IPCC, 2014), there is now an urgent need to elucidate how the interaction between changes in land use and climate will affect the microbial use of OM and the resulting contributions to the soil and atmospheric pools of C (Alexander et al., 2018; Peters et al., 2019). This need is most urgent in tropical and subtropical ecosystems since the rate of land‐use conversion affecting soil OM is fastest there (Le Quere et al., 2009). However, in contrast with the urgent need for information in tropical regions, the study of climate and land‐use intensity on soil microbial communities and processes is mostly restricted to arctic, boreal, temperate, and Mediterranean climates (Crowther et al., 2016; Le Quere et al., 2009; van Gestel et al., 2018) with very scarce observations reported from subtropical and tropical ecosystems (Powers et al., 2011).The historical transition of natural habitat in the Ethiopian highlands provides an interesting and valuable arena to investigate how microbial communities and functions are affected by land‐use intensity and climate change. In the highland plateaus of the Amhara region, comparable study sites along four levels of a steep climate gradient ranging from hot and arid lowland sites to moist and cool highland sites have been established and monitored (Assefa et al., 2017). Each of the sites include the land uses, (a) natural forests, (b) grasslands used for pasture, (c) tilled croplands used for intensive agriculture, and (d) exclosures, where natural forests are in the process of being restored from degraded agricultural soils. These conditions enable a “space‐for‐time substitution” design (Blois et al., 2013; Walker et al., 2010), where the impact of deforestation (forest to grassland) and intensification of agriculture (grassland to cropped agriculture) as well as that of the restoration of forests (cropland to exclosures) along a climate gradient can be used to infer how predicted climate warming, leading to a hotter and drier climate (IPCC, 2014), will interact with land‐use regimes in subtropical ecosystems. As with any large‐scale geographical climate gradient, the climate factor is to some degree confounded with plant community composition, soil formation, including texture and aggregation, and other factors that depend on climate. However, the added difficulty in teasing out causality is compensated by the relevance (Blois et al., 2013; Walker et al., 2010); following climate change, the assemblages of communities would dynamically respond, resulting in a composition shaped by the new climate (Blois et al., 2013), and soil formation factors would also align with those of the new climate with time (Weil & Brady, 2017).In this study, we set out to investigate how land use, climate, and their interaction affected the soil microbial community's use of OM for respiration and growth, together with the responses in nutrient cycling (gross N mineralization and nitrification). In addition to assessing the use of OM by the active microbial decomposer community, we also estimated the total microbial biomass size, the biomass of the major decomposer groups, bacteria and fungi, along with that of arbuscular mycorrhizal fungi (AMF). The composition these broad microbial groups is likely responsive to changes in climate aridity (Augé, 2001; de Vries et al., 2012; Mackie et al., 2019; Remke et al., 2021; Yuste et al., 2011) and land use (Lauber et al., 2008; Valyi et al., 2015) and produce biomass components likely to contribute to soil OM accumulation (Six et al., 2006). Finally, we evaluated the effects on microbial community structure resolved using the composition of microbial phospholipid fatty acids (PLFAs). To specifically assess how the historical variation in climate and land use had shaped microbial communities and process rates, we assessed soils from all sites under standardized conditions in the laboratory, thus circumventing the direct influence of differences due to soil moisture or temperature (Rousk et al., 2013). As such, we assessed the “legacies” of climate and land use on the structure and functioning of the soil microbial community (de Nijs et al., 2019; Göransson et al., 2013; Hawkes et al., 2017; Wallenstein & Hall, 2012).We hypothesized that ecosystem aridity would (H1) decrease microbial growth and biomass (Crowther et al., 2019), (H2) increase the relative importance of fungal decomposers (Yuste et al., 2011), (H3) increase the relative dominance of AMF due to a greater plant need for AMF‐facilitated water acquisition (Augé, 2001; Remke et al., 2021), and (H4) decrease respiration, gross N mineralization and nitrification (Larsen et al., 2011). We also hypothesized that (H5a) deforestation would reduce microbial biomass, growth rates, and the mineralization rates of C and N, while (H5b) reforestation (exclosures) would reverse the process (Holden & Treseder, 2013; Mussa et al., 2017). Further, we predicted that (H6) intensification of agriculture would stimulate gross nitrification rates (Norton & Ouyang, 2019). Most importantly, we hypothesized that (H7) the reduction of microbial community size and functions by land‐use intensification would be accelerated in a warmer and drier compared to a cooler and more humid climate (Smith et al., 2016).Materials and MethodsStudy Sites and Soil SamplingThe studied sites were selected from previously established study sites in the Amhara National Regional State (Assefa et al., 2017), located in north‐western Ethiopia. Although Ethiopia is located in the tropics, the climate of the study areas varies due to differences in altitude (Figure 1), generating a climate gradient from 2,200 mm year−1 mean annual precipitation (MAP) and 16°C mean annual temperature (MAT) at the highest altitude site to 1,050 mm year−1 MAP and 20°C at the lowest altitude site (Table 1). This altitudinal‐based climate gradient is henceforth referred to by the level of aridity or humidity, which integrates both MAP and MAT, ranging from the most humid ecosystem in Katassi (MAP = 2,200 mm year−1, MAT = 16°C), with soils classified as Luvisols, via intermediate altitude sites with progressively warmer and drier climates in Gelawdiwos (MAP = 1,200 mm year−1, MAT = 18°C) and Tara Gedam (MAP = 1,100 mm year−1, MAT = 19°C), both with soils classified as Cambisols, to the most arid conditions in the lowest altitude site Ambo Ber (MAP = 1,050 years−1, MAT = 20°C), with soils classified as a Leptosol. These combinations of rainfall and temperature resulted in potential evapotranspiration (PET) ranging from 2,800 to 3,480 mm year−1. The aridity index (AI), that is, the ratio of MAP to PET, where higher values indicate more humid conditions, ranged from 0.79 in Katasi to 0.30 in Ambo Ber (Table 1), translating to ecosystem aridity (estimated as 1‐AI, Maestre et al., 2015) levels ranging from 0.21 in Katasi to 0.70 in Ambo Ber (Table 1). The area's rainfall distribution is unimodal with most precipitation occurring during June‐September, while the dry months are from October to May, resulting in dry winters and wet summers (Peel et al., 2007). The four selected sites had an average distance of approximately 120 km between them.1FigureThe distribution of study sites along a climate gradient linked to altitude in the Amhara region in Northwest Ethiopia.1TableClimate and Edaphic Characteristics of the Sampled SitesCoordinatesMAPaMATaPETaETaAIbEcosystem aridity metriccStudy siteLand useSoil pHElectrical conductivity (μS cm−1)SOM (%)NH4 (μg NH4‐N g−1)NO3 (μg NO3‐N g−1)MeanSEMeanSEMeanSEMeanSEMeanSE12°31ʹ15″ N, 37°31ʹ53″ E1,050203,4803600.300.70Ambo BerCrop6.60.081968210.11.040.490.063.261.15Exclosure6.70.101886610.90.460.750.095.752.26Forest6.60.083164720.01.050.780.099.684.14Grassland6.50.0946433516.01.020.850.123.603.5312°8ʹ47″ N, 37°44ʹ45″ E1,100192,9303600.380.62Tara GedamCrop6.40.131484610.60.180.720.042.221.28Exclosure6.40.031871007.00.768.302.693.161.14Forest6.20.2166136629.34.971.400.329.850.82Grassland6.30.292397413.20.800.590.276.522.2211°38ʹ25″ N, 37°48ʹ55″ E1,220182,8703800.430.57GelawdiwosCrop6.00.131878511.00.270.660.032.320.65Exclosure5.90.112378613.00.623.092.222.491.04Forest6.20.2541219122.81.420.490.1410.413.62Grassland5.70.1327515615.20.963.201.474.901.7511°0ʹ05″ N, 36°44ʹ8″ E2,200162,8008000.790.21KatasiCrop4.90.1340220812.21.121.340.373.432.08Exclosure5.30.112438414.71.422.841.476.052.57Forest5.80.2438713624.81.720.910.0510.524.03Grassland5.10.1354737015.90.850.970.095.592.23ANOVA resultsLUp = 0.02NSp < 0.0001p = 0.0003NSSitep < 0.0001NSp = 0.0004p = 0.04NSLU × sitep = 0.04NSp < 0.0001p = 0.001NSaMean annual precipitation (MAP) and mean annual temperature (MAT) are 5‐year averages and were taken from closest meteorological stations and were provided by the Ethiopian National Meteorology Agency. Potential Evapotranspiration (PET) and evapotranspiration (ET) are estimates provided by the NASA's Moderate Resolution Imaging Spectroradiometer via the Evapotranspiration Web Viewer, University of Montana, USA.bThe aridity index (AI) is the ratio of MAP to PET, where a value of 1 indicated that MAP matches the moisture demand of the system with lower values indicating increasing aridity.cThe ecosystem aridity metric is (1‐AI), where zero indicates a system where MAP meets the MAP demand and higher values indicate drier ecosystems.At each of the climate sites (Table 1), four randomly assigned plots 20 by 20 m areas were selected with similar topography, edaphic, and climate conditions for each of four land uses, including (a) natural forest, (b) grassland used as pasture, (c) cropland, and (d) exclosure (rehabilitation of woodland on former degraded pasture and cropland). The four independent plots of each land use within each climate site were treated as independent replicates. The forests of these areas are dry Afromontane remnant pristine forests composed mostly of a mixture of indigenous tree species, which are protected by local institutions and mostly confined to sacred groves associated with churches and monasteries (Aerts et al., 2016). The exclosures were established on former grazing land and are used to rehabilitate degraded land by protecting the land from further animal grazing and human interference. In addition to grazing prevention, enrichment planting of seedlings of indigenous and some exotic tree species has been carried out. In the exclosures, local people are still allowed to use grass (cut‐and‐carry‐systems) for fodder and to collect fuelwood. Since establishment (1985 for Katassi, Galawdios and Tara Gadam, 2007 for Ambo Ber), natural revegetation has occurred and the land areas are now covered by an extensive woodland dominated by bushes. The pastures are used as a common grazing land to feed herds of cattle and small ruminants including sheep and goats, and equines, such as donkeys. The croplands and pastures were all converted from natural forests within the last 50 years. Croplands are ox‐plowed to a tillage depth of 10–20 cm, and crop rotations including Eragrostis tef, Eleusine coracana, Sorghum bicolor, Zea mays, Triticum aestivum, Guizotia abyssinica, and Vicia faba are grown during the rainy season. Between years, crop rotation is a common practice.Soil samples were collected in the early wet season in June 2017. Within each plot, 9–12 small soil pits (0–10 cm depth) were combined into composite samples maintaining the four independent field replicates (4 land uses each with 4 replicates, in each of 4 climate sites, totaling 64 samples). The replicate plots were >100 m apart to ensure spatial independence. The air‐dry soil samples were stored in double ziploc bags that were shipped express to the Lund University in Sweden for analysis within 14 days of sampling.Soil Physiochemistry, Moisture Adjustment, and PreincubationOn arrival, all soil samples were sieved (<4 mm). Soil subsamples were used to measure gravimetric soil water content (105°C to constant mass) and soil OM content through loss on ignition (600°C for 12 hr). Soil pH and electrical conductivity were measured in a 1:5 (w/v) soil:H2O solution using a pH meter and an electrical conductivity meter, respectively. The maximum water holding capacity (WHC) was measured as previously described (Hicks et al., 2018). All soil samples were then adjusted to optimal moisture for microbial activity at 50% of the maximum WHC, which was confirmed by gravimetric soil moisture determination, before being left to preincubate during 14 days at 18°C in the dark until microbial process rates were assessed (see below).Soil Respiration and SIR‐Microbial BiomassOne gram soil was weighed into 20‐ml glass vials. The head space of glass vials was purged with pressurized air before vials were sealed and incubated at 18°C for 18 hr. The amount of CO2 produced during the incubation was determined using a gas chromatograph equipped with a methanizer and flame ionization detector. Substrate‐induced respiration (SIR) was measured as a proxy for microbial biomass. Briefly, 15 mg of 4:1 glucose:talcum was vigorously mixed into 1.0 g soil (corresponding to 4.8‐mg glucose‐C g−1 soil fwt). After 30 min, vials were purged with pressurized air and incubated at 22°C for 2 hr before the concentration of CO2 was determined. SIR was used to estimate microbial biomass (mg C g−1; Anderson & Domsch, 1978).Gross N Mineralization and Nitrification, and Soil NH4+ and NO3− ConcentrationsGross N mineralization and gross nitrification rates were determined using the 15N pool‐dilution method (Rousk et al., 2016). Briefly, two subsamples of each soil (each 15.0 g fwt) were weighed into 100‐ml plastic pots to which 115 μl of NH4Cl (45 μg N ml−1, enriched to 1 atom% 15N) was administered. The soils were vigorously mixed and then lidded. Soil NH4+ $\mathrm{N}{{\mathrm{H}}_{4}}^{+}$ and NO3− $\mathrm{N}{{\mathrm{O}}_{3}}^{-}$ were extracted using 1M KCl solution and then isolated from the extract by diffusion to acidified glass fiber traps (according to standard procedures; International Atomic Energy Agency [IAEA], 2001). One set of subsamples was extracted approximately 1 hr after 15N addition, while the second set was treated identically after 24 hr incubation at 18°C without light. The amount of NH4+ $\mathrm{N}{{\mathrm{H}}_{4}}^{+}$‐N and NO3− $\mathrm{N}{{\mathrm{O}}_{3}}^{-}$‐N was determined by isotope‐ratio mass spectrometry (IRMS), and the 15N/14N content of the glass fiber traps was measured with a Flash 2000 elemental analyzer coupled to a Delta V plus via the ConFlow interface (Thermo Fisher Scientific, Germany). Gross N mineralization, gross NH4+ $\mathrm{N}{{\mathrm{H}}_{4}}^{+}$ consumption, and gross nitrification rates were estimated according to the equations previously described (Bengtson et al., 2005), assuming that gross N mineralization matched gross immobilization (i.e., concentrations of NH4+ $\mathrm{N}{{\mathrm{H}}_{4}}^{+}$ did not change during the incubation).Bacterial and Fungal GrowthBacterial growth was determined by measuring the rate of 3H‐Leucine (Leu) incorporation into extracted bacteria (Bååth et al., 2001; Rousk et al., 2009). One gram of fresh soil was mixed with 20‐ml demineralized water, vortexed for 3 min, and centrifuged (10 min at 1,000 g). The resulting bacterial suspension was incubated at 18°C for 2 hr with 2 μl 1‐[4,5‐3H]‐Leucine (5.7 TBq mmol−1, Perkin Elmer, USA) and unlabeled Leu with a final concentration of 275 nM Leu in the bacterial suspension. Bacterial growth was terminated after 2 hr by adding 75 μl of 100% trichloroacetic acid, followed by centrifugation and washing (Bååth et al., 2001). Scintillation cocktail (Ultima Gold; PerkinElmer, USA) was added and the radioactivity was measured using a liquid scintillation counter. The amount of leucine incorporated into extracted bacteria (pmol Leu incorporated g−1 h−1) was used as a measure of bacterial growth.Fungal growth was determined using the acetate‐in‐ergosterol (Ac‐in‐erg) incorporation method (Newell & Fallon, 1991) adapted for soil (Bååth, 2001; Rousk et al., 2009), which estimates the rate of ergosterol synthesis as a measure of fungal growth. One gram of soil was mixed with 20 μl of 14C‐acetate solution ([1–14C] acetic acid, sodium salt, 2.07 GBq mmol−1, Perkin Elmer) and unlabeled sodium acetate, resulting in a final acetate concentration of 220 μM in the soil slurry. Samples were incubated at 18°C for 4 hr in the dark before the growth was terminated by the addition of formalin. Ergosterol and incorporated acetate were measured according to Rousk and Baath (2007). The amount of acetate incorporated into ergosterol (pmol g−1 h−1) was used as a measure of fungal growth.Microbial Community PLFA and NLFA Concentrations and StructuresThe PLFA composition from a 1.0‐g fresh soil subsample was determined according to Frostegard et al. (1993) with modifications (Cruz‐Paredes et al., 2017). An internal standard (methyl nonadecanoate fatty acid 19:0) was added before the methylation step for quantification. The total PLFA concentration was used as a measure of total microbial biomass, while bacterial‐ and fungal‐specific PLFA markers were summed to estimate the relative biomass of fungi and bacteria (Frostegård & Bååth, 1996). To estimate the biomass of AMF, we used the NLFA marker 16:1ω5 (Olsson et al., 1995). The relative abundance (mol%) of the 25 identified PLFAs was used to screen for the effects of climate and land use (see below) on the microbial community structure (Frostegard et al., 1993).Statistics and CalculationsThe measured variables were first log‐transformed to meet the assumption of equal variances and then subjected to two‐way analysis of variance (ANOVA), considering the factors, climate‐site (4 levels), land use (4 levels), as well as their interaction. To determine differences among the factor and interaction levels, Tukey's HSD pairwise comparisons (at p < 0.05) were used. To test for interlinkages between measured variables, linear regressions were used. Relationships with soil OM content were also tested using linear regressions, assuming that soil OM was the independent variable.A principal component (PC) analysis was used to screen for differences in the PLFA composition of the soil microbial community, using relative abundances (mol%) after standardizing to unit variance. The scores of the PCs were subjected to two‐way ANOVA (as above) and the variable loadings were used to interpret which PLFA markers drove the separation of the PCs (Frostegard et al., 1993).ResultsSoil PropertiesSoil properties varied both due to differences in climate between sites and due to differences in land use within sites (Table 1). While there was no effect of land use or climate for electrical conductivity, the soil pH differed between sites (p < 0.0001) being most acidic in the cool, humid Katasi site (pH 5.6) and increasing with site aridity to have highest values in the Ambo Ber site (pH 6.6). Land use also mildly influenced soil pH (p = 0.02) with slightly higher pH in forests and exclosures compared with croplands and grassland (Table 1). The climate also interacted with land use (p = 0.04), such that land use resulted in larger pH differences in cool, humid, compared with arid, hot sites (Table 1). Soil OM content was also strongly affected by the climate of the site (p = 0.0004) and by land use (p < 0.0001) with especially pronounced differences among land uses in some sites (p < 0.0001; Table 1). Overall, the soil OM content was higher in humid and lower in more arid sites and differed systematically between land uses being highest in the forests, second highest in grasslands, and similarly low in croplands and exclosures (Table 1). The concentration of NH4+ $\mathrm{N}{{\mathrm{H}}_{4}}^{+}$ varied between land uses (p = 0.0003), being higher in exclosures than other land uses, and was marginally distinguishable between sites (p = 0.04) with a significant interaction term (p = 0.001; Table 1). While NO3− $\mathrm{N}{{\mathrm{O}}_{3}}^{-}$ concentrations were variable, no systematic differences with climate or land use were detected. NH4+ $\mathrm{N}{{\mathrm{H}}_{4}}^{+}$ concentrations were not linked to soil OM content (p > 0.2); however, NO3− $\mathrm{N}{{\mathrm{O}}_{3}}^{-}$ concentrations increased with higher soil OM (p < 0.0001, R2 = 0.27).Microbial Growth Rates and RespirationFungal growth did not vary between sites, but did vary with land use (p = 0.0009), with highest growth rates in forest, intermediate rates in exclosure and grassland, and lowest rates in croplands (Figure 2a). Bacterial growth had highest levels in the dry and lowest values in the wettest site (p < 0.0001). Forests had highest values, grassland intermediate, and the lowest rates of bacterial growth were found in exclosures and croplands (p = 0.0006, Figure 2b). This resulted in a fungal‐to‐bacterial growth ratio that was unaffected by land use, but was lowest in the most arid site and highest in the least arid site (Figure 2c). Respiration increased with higher aridity (p < 0.0001) and also decreased from forest via exclosure and grassland to be lowest in croplands (p < 0.0001) with an interaction (p = 0.004) where the land‐use effect was stronger in dry sites (Figure 2d). Differences in both fungal (p = 0.0001, R2 = 0.26) and bacterial growth rates (p < 0.0001; R2 = 0.29) were positively linked to soil OM content, leading to a fungal‐to‐bacterial (F/B) growth ratio unrelated to soil OM (p = 0.73). Respiration was strongly positively linked with soil OM (p < 0.0001; R2 = 0.53).2FigureThe effect of land use (LU) and climate on rates of (a) fungal growth, (b) bacterial growth, (c) fungal‐to‐bacterial growth ratio, and (d) respiration. Results from two‐way analysis of variance (ANOVA), including the factors LU and site along with their interaction, are shown for each microbial variable, and significant factor differences from the ANOVA are accompanied by Tukey HSD pairwise comparisons between sites (capital letters) and between LUs (lowercase letters by the legend). Presented values are the mean ±1 SE.Nitrogen TransformationsGross N mineralization rates were highest in forest, intermediate in croplands and grassland, and lowest in exclosures (p < 0.01) and had lower values in the 1,220 mm site (the second most humid) compared to the three others (p = 0.003; Figure 3a). Gross nitrification was highest in croplands, intermediate in exclosure and grassland, and lowest in forests (p = 0.0002) and decreased with higher aridity of the site (p = 0.002; Figure 3b). This resulted in a ratio of soil C mineralization to gross N mineralization that was higher in forest (c. 12 μg C/μg N), grassland (c. 11 μg C/μg N), and exclosures (c. 10 μg C/μg N) compared to croplands (c. 3 μg C/μg N; p < 0.0001). The ratio of C mineralization to gross N mineralization was also higher in the 1,220 mm (c. 14 μg C/μg N) and 1,100 mm (c. 10 μg C/μg N) sites, intermediate (c. 9 μg C/μg N) in the 1,050 mm site, and lowest (c. 4 μg C/μg N) in the 2,200 mm site (p = 0.003). The variations in gross N mineralization (p = 0.005, R2 = 0.10) and gross nitrification (p = 0.019, R2 = 0.07) were weakly negatively linked to soil OM content, while there was no relationship between the ratio of C mineralization to gross N mineralization and soil OM (p = 0.29).3FigureThe effect of land use (LU) and climate on (a) gross N mineralization and (b) nitrification. Results from two‐way analysis of variance (ANOVA), including the factors LU and site along with their interaction, are shown for each microbial variable, and significant factor differences from the ANOVA are accompanied by Tukey HSD pairwise comparisons between sites (capital letters) and between LUs (lowercase letters by the legend). Presented values are the mean ±1 SE.Microbial Biomass and Community CompositionSoil microbial biomass C measured by SIR was highest in forest and lowest in croplands (p < 0.0001) and had higher levels in the driest site compared to the others (p < 0.0001; Figure 4a). The total PLFA concentration, as well as fungal and bacterial PLFA concentrations, showed similar patterns with highest levels in forests and lowest in cropland, and highest values in humid and lowest in arid sites (all p < 0.0001; Figures 4b–4d). These patterns resulted in a fungal‐to‐bacterial PLFA ratio, which was higher in croplands and exclosures compared with grasslands and forests (p = 0.0002), and higher in dry compared to wet sites (p = 0.0002; Figure 4e). The NLFA marker for AMF had higher values in the driest site (p = 0.0028; Figure 4f), but was unaffected by land use. All estimates of microbial decomposer biomass were positively correlated to soil OM content (all p < 0.0001; R2 = 0.73, 0.68, 0.51, and 0.69 for SIR biomass, total PLFA, fungal PLFA, and bacterial PLFA concentrations, respectively). In contrast, the fungal‐to‐bacterial PLFA concentration was weakly negatively linked to soil OM content (p = 0.014; R2 = 0.08), while the NLFA 16:1w5 showed no relationship with soil OM content (Figures 4e and 4f).4FigureThe effect of land use (LU) and climate on (a) microbial biomass measures as substrate‐induced respiration‐biomass, (b) total phospholipid fatty acid (PLFA) concentration, (c) fungal PLFA concentration, (d) bacterial PLFA concentration, (e) the fungal‐to‐bacterial PLFA ratio, and (f) the arbuscular mycorrhizal fungi NLFA concentration. Results from two‐way analysis of variance (ANOVA), including the factors LU and site along with their interaction, are shown for each microbial variable, and significant factor differences from the ANOVA are accompanied by Tukey HSD pairwise comparisons between sites (capital letters) and between LUs (lowercase letters by the legend). Presented values are the mean ±1 SE.About 45% of the total variation of the microbial PLFA composition could be explained by two PCs (Figure 5). Microbial PLFA composition was strongly influenced by both land use and site with low PC1 scores associated with forests and exclosures and high PC1 scores associated with croplands and grasslands (p < 0.0001), while positive PC2 scores were associated with forests, grassland and exclosures were associated with intermediate scores, and negative PC2 scores were associated with croplands (p < 0.0001). There was a weak positive link between PC2 scores and the aridity of the site (ANOVA p = 0.016, Figure 5a). Soil pH was positively linked to PC1 scores (linear regression, p < 0.0001, R2 = 0.33) with high scores being linked to 16:1ω5, 18:1ω9, and 18:1ω7 and low scores with the 10Me17:0, 10Me18:0, and cy19:0 (Figure 5b). Soil OM content was positively linked to PC2 scores (p < 0.0001, R2 = 0.35) and associated with low concentrations of monounsaturated PLFA markers and the fungal marker 18:2ω ethiopian siteseth 6,9 (Figure 5b).5FigureThe effect of land use (LU) and climate on the microbial phospholipid fatty acid (PLFA) composition shown as (a) the principal component scores showing the level of PLFA composition similarity and (b) variable loadings showing which PLFA markers that drove that variation in the four different LUs (symbols) in the four different climates (colors). Results from two‐way analysis of variances on PC1 and PC2, including the factors LU and site along with their interaction, are shown. Presented values are the mean ±1 SE.DiscussionMicrobial Community Structural and Functional Dependence on ClimateIn a global survey of drylands (i.e., ecosystems with AI < 0.65), the abundance and diversity of soil decomposer bacterial and fungal communities decreased with higher ecosystem aridity (Maestre et al., 2015). We find diverging patterns from these in our survey of high‐altitude subtropical sites, most of which could be categorized as drylands (i.e., AI < 0.65). To specifically resolve the impact of the different legacies of climate in the studied ecosystems on soil microbial community and functions, all assessments of microbial variables were conducted under controlled moisture and temperature (Hicks et al., 2018; Rousk et al., 2013). Our estimates of total microbial biomass—both total PLFA concentrations and SIR‐biomass—increased slightly with higher aridity. The increase in fungal biomass was particularly pronounced (Figure 4c), resulting in an increasing fungal‐to‐bacterial PLFA ratio in more arid systems (Figure 4e). We also resolved differences in microbial growth rates under controlled temperature and moisture conditions, which more closely correspond to microbial contributions to ecosystem functioning than do biomass concentrations (Liang et al., 2017; Rousk, 2016; Zheng et al., 2019). Bacterial growth increased with higher ecosystem aridity, while fungal growth was independent (Figures 2a and 2b), resulting in a substantial decrease in the ratio of fungal‐to‐bacterial growth in more arid climates (Figure 2c). Thus, the pattern for subtropical Ethiopian sites diverged from that reported in a large‐scale survey of drylands (Maestre et al., 2015) with our study showing consistent increases in both microbial biomass and growth rates with higher aridity (H1). This finding is surprising but may be explained by drought legacy effects on the lability and availability of C to microbes (Hicks et al., 2018; Leizeaga et al., 2021). This could be partly driven by the molecular‐scale physiochemical interactions with mineral surfaces and physical protection of OM (Cotrufo et al., 2013; Lehmann & Kleber, 2015; Schmidt et al., 2011), where drier conditions may have preserved OM until the moisture adjustments made in our assessment of legacy effects. We also found evidence for an increased relative abundance of fungal biomass in arid systems, but a reduction in the relative growth rate under moist conditions (H2). This finding is consistent with earlier reports of higher fungal resistance to drought observed in field experiments both in arid (Leizeaga et al., 2021; Yuste et al., 2011) and humid ecosystems (de Vries et al., 2012) and in dry compared to wet seasons in subtropical highlands (Ahmed et al., 2019) as well as in laboratory experiments (Bapiri et al., 2010; Gordon et al., 2008). It also highlights that the size of microbial communities does not unambiguously reflect the active contribution to processes in soils since the status of the resolved biomass is unknown (having a range of possible levels of activity, being dormant, or even dead), thus contrasting with estimates of growth (Blagodatskaya & Kuzyakov, 2013; Rousk, 2016). AMF associations have long been known to benefit plant survival and productivity during drought (Augé, 2001). In support of our hypothesis (H3), the abundance of AMF increased with higher aridity (H3), presumably reflecting an increased reliance by plants on water acquisition via the AMF symbiosis. Taken together, our findings suggest that a legacy of drought increased the size of the soil microbial food web with a higher fraction of fungi, and that this shift coincided with a slower turnover of the microbial community, as indicated by the ratio of the biomass pool to the rate of microbial growth that we measured. It is possible that the maintenance of a large fungal biomass in arid conditions could be linked to the greater capacity of the fungal mycelial growth form to redistribute patchy resources in a dry soil matrix (Augé, 2001; Guhr et al., 2015), physiological differences in abilities to withstand large negative water potentials (Harris, 1981; Schimel, 2018), or a more flexible fungal stoichiometry (Strickland & Rousk, 2010), but more work is needed to resolve the specific causes. A fungal‐dominated food web has been proposed to promote the stabilization of both C and nutrients (Wardle et al., 2004) in agricultural ecosystems (Six et al., 2006) as well as in forests (Clemmensen et al., 2013), consistent with the here reported reduced microbial turnover rate. If our assessments of microbial communities and functions at standardized temperature and moisture can be extrapolated back to capture the biogeochemistry in the ecosystems they were sampled from, the ecosystem legacy of arid conditions should further amplify the C sequestration effects. Running counter to these effects, input of OM by plant productivity was likely negatively dependent on ecosystem aridity (Liu et al., 2018), which may explain why the belowground responses have not yet resulted in detectable increases in OM concentrations.We found systematic differences in microbial PLFA composition associated with ecosystem aridity, where more arid systems were orientated toward low PC1 scores and humid systems toward high PC1 scores (Figure 5a). The community differences were pronounced, as indicated by the large fraction of variation in the relative abundance composition of PLFAs explained by PC1, which matched earlier reports from pH and fertility gradients known to be some of the strongest drivers of microbial community composition (Högberg et al., 2007; Nilsson et al., 2007). In line with previous studies, PLFA marker separation was also driven by biomarker lipids shown to be linked with high and low pH, including cy19:0 (low pH) and PLFAs 16:1ω5 and 18:1ω7 (high pH; Rousk et al., 2010). Indeed, the variation in PC1 scores was closely linked with pH (P = 0.0001, R2 = 0.33).The observed differences in microbial communities, growth rates, and structures coincided with higher respiration rates in more arid climates (Figure 2d), while gross N mineralization rates did not change systematically with humidity (H4). This decoupling of C and nutrient cycling resulted in a ratio of C mineralization to gross N mineralization ranging from 10 to 14 μg C/μg N in the more arid sites to be far lower, at about 4 μg C/μg N, in the most humid site. Gross rates of C and N mineralization normalized to the resource availability (i.e., OM content) can be used to infer the nutrient composition of used substrate and therefore the microbial availability of C and nutrients (Craine et al., 2010; Fierer et al., 2005, 2006). As such, the emerging patterns would suggest that the C and N available in the OM used by microorganisms, as proxied under controlled moisture and temperature, shifted from being more nutrient‐rich (mineralization C/N = 4) in humid sites (and therefore more C limited) to being more nutrient‐poor (C/N = 10 – 14) in the more arid sites (and therefore more nutrient limited). A large‐scale survey of soils from an aridity gradient in northern China that targeted microbial enzyme stoichiometry to infer the microbial regulation of C and N cycles found that the ratio of enzymes targeting C compared to those that targeted N decreased with higher aridity (Feng et al., 2019). This also suggests more nutrient limited microbes in arid systems despite smaller OM reservoirs, highlighting a need for more work to elucidate the underlying mechanisms for this counterintuitive finding.Microbial Community Structural and Functional Dependence on Land UseThere are documented links between agricultural intensification and decreasing microbial diversity, and changing community structures (Turley et al., 2020). Similar to the influence of site aridity, we also found that land use systematically affected the microbial PLFA composition with forest and cropland land uses being diametrically most dissimilar in community composition, with lower intensity agriculture in the form of grassland for grazing, and the recovery of croplands within exclosures, falling in between (Figure 5a). The resulting effect of land use on community structure seemed to match that driven by site aridity and was well correlated to soil pH and OM content, consistent with the impacts of land‐use intensity previously observed in both temperate (Turley et al., 2020) and tropical environments (Berkelmann et al., 2020). It has previously been shown that the conversion of natural forests and woodlands into agricultural land is detrimental for soil C, nutrient and microbial biomass stocks, as well as for microbial functions in tropical (Paul et al., 2010) and subtropical soils (Brackin et al., 2013; Tosi et al., 2016). In addition, forest restoration and reforestation practices have been shown to at least partly recover soil C and nutrient stocks together with the microbial community size and functions (Delelegn et al., 2017; Paul et al., 2010). The reversal of the reduction of soil C and nutrient pools by deforestation induced by afforestation strengthens the causal influence by the current land‐use practice on soil resources although the recovery of soil C and nutrient stocks is only partial within years to decades. Our results match these previous reports (H5). The total size of microbial communities was far larger in forest land uses at all climates, while the transition into agricultural use reduced the microbial community size by 4–20 fold (Figure 4). Surprisingly, the relative size of the fungal community compared to the bacterial community was not reduced by conversion to agriculture, but instead increased marginally (Figure 4e). The AMF abundance was also unaffected by land use (Figure 4f). Our results therefore contrast with reports of differences in relative AMF abundances based on spore counts in similar systems that have shown decreases induced by land‐use intensification (Delelegn et al., 2017). Naturally, to some extent, comparisons between data sets risk ambiguity, since the intensity of land use is relative, and it is possible that earlier reports included other intervals of the elusive parameter “land‐use intensity” (see discussion in Fischer et al., 2010). However, the range of land uses we consider here cover the range of current practices in the studied region, providing relevance. Collectively, our findings suggest that the reversal of agricultural conversion consistently resulted in a trend for recovery of microbial community sizes (Figure 4) with slower turnover (Figure 2), which in the long term should lead to replenished stocks of soil C (Liang et al., 2017; Six et al., 2006).Agricultural land conversion from forests decreased rates of fungal and bacterial growth as well as respiration rates (H5; Figure 2) similar to the changes observed for microbial biomass (Figure 4), albeit with slightly smaller effect sizes. These changes suggested a higher level of active biomass (Blagodatskaya & Kuzyakov, 2013), following conversion to agricultural land use, including manure addition practices, and thus faster C and nutrient turnover rates (Wardle et al., 2004). This indication of enhanced soil fertility was also consistent with the smaller reduction of gross N mineralization (Figure 3a) compared with that for respiration (Figure 2d), reducing the ratio of C mineralization to N mineralization from about 12 to 3 and suggesting a shift in microbial resource use from relatively nutrient‐poor to nutrient‐rich OM (Hicks et al., 2021; Murphy et al., 2015; Rousk et al., 2016). These shifts in microbial resource availability also stimulated gross nitrification rates (H6; Figure 3b). Similar to the microbial biomass results, the reversal of the agricultural conversion (i.e., exclosure treatments) consistently resulted in a trend for partial recovery toward the state in the forests.Impact of Land‐Use Intensity and Forest Restoration on Microbial Functions Under Climate ChangeThere is an urgent need to determine how the parallel threats of land‐use intensification and climate change will interact (Alexander et al., 2018; Peters et al., 2019), especially in understudied tropical and subtropical ecosystems, where the rate of land‐use conversion affecting soil OM is fastest (Le Quere et al., 2009). We found no interactions between the legacy of climate and land use for the microbial community structure, and only marginal effects on the microbial community size, lacking a consistent pattern that we could link to ecosystem aridity (Figure 4). We also found similar patterns for the microbial functions assessed under controlled temperature and moisture conditions. Bacterial and fungal growth rates were unaffected by the interaction between drought legacy and land use. However, the influence by land use on respiration increased with higher ecosystem aridity (Figure 2d), suggesting that the rates of OM turnover were more impacted by land use in drier climates (H7). These patterns were consistent both for the reduction in respiration induced by increased intensity of agricultural use (i.e., forest → cropland) as well as the increase in respiration induced by land restoration (i.e., cropland → exclosures), further strengthening the causal link to the land‐use factor.The most likely explanation for the observed interaction between the effects of differences in land use and those of ecosystem aridity on respiration is the concentration of OM, providing the substrate pool that governs the flux of C from soil (Parton et al., 1993; Wardle & Ghani, 1995). The link between respiration and soil OM content was relatively strong (R2 = 0.53) and comparable to global ecosystem surveys, including many sites (e.g., Delgado‐Baquerizo et al., 2013; Maestre et al., 2015), and exceeded the variation explained by bacterial growth, fungal growth, or N turnover processes. However, for the same reason, it is also expected that the size of the microbial biomass is determined by the soil OM pool size (Fierer et al., 2009; Wardle & Ghani, 1995), and while the correlation with soil OM content was even stronger for microbial biomass than that for respiration, there was no systematic correlation between land use and system aridity as shown by the lack of interaction between factors.Our findings suggest that land use will impact the decomposition of soil OM and plant nutrient provisioning more intensively during wet seasons in systems with a legacy of drier conditions, and while part of this variation may be driven by an OM pool size, it also appears to be driven by differences in the quality of OM, which was more impacted by the legacy of land use in drier climates. Surprisingly, the differential responses in the mineralization of OM in soils with different climate legacies did not coincide with shifts in the balance between fungal and bacterial decomposers in terms of growth rates or biomass. Forecasting from these observations, we can expect that the drier subtropical climates expected as a result of climate change (IPCC, 2014) will exacerbate the negative effects of land‐use intensification on soil OM turnover and the provisioning of nutrients for plants during the wet seasons when productivity is highest.AcknowledgmentsThe authors thank the Amhara Regional Agricultural Research Institute (ARARI) for logistical support and local land owners for access to their land, which enabled this study. This study was supported by the “Sustainability and Resilience Program” supported by the Swedish Research Council (Vetenskapsrådet), the Swedish Research Council Formas, and the Swedish International Development Cooperation Agency (VR Grant No. 2016‐06327), and from the Knut and Alice Wallenberg Foundation (Grant No. KAW 2017.0171).Data Availability StatementAll data used in this study are original and are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions linked with local land‐owner agreements.ReferencesAerts, R., Van Overtveld, K., November, E., Wassie, A., Abiyu, A., Demissew, S., et al. (2016). Conservation of the Ethiopian church forests: Threats, opportunities and implications for their management. The Science of the Total Environment, 551, 404–414. https://doi.org/10.1016/j.scitotenv.2016.02.034Ahmed, I. U., Mengistie, H. K., Godbold, D. L., & Sanden, H. (2019). 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Journal

Global Biogeochemical Cycles – Wiley

Published: Apr 1, 2022

Keywords: land‐use intensification; agriculture; deforestation and afforestation; microbial growth; soil carbon sequestration; space‐for‐time substitution

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