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Biological nitrogen fixation can be an important source of nitrogen in tropical forests that serve as a major CO 2 sink. Extensive deforestation of the Amazon is known to influence microbial communities and the biogeochemical cycles they mediate. However, it is unknown how diazotrophs (nitrogen-fixing microorganisms) respond to deforestation and subsequent ecosystem conversion to agriculture, as well as whether they can recover in secondary forests that are established after agriculture is abandoned. To address these knowledge gaps, we combined a spatially explicit sampling approach with high-throughput sequencing of nifH genes. The main objectives were to assess the functional distance decay relationship of the diazotrophic bacterial community in a tropical forest ecosystem and to quantify the roles of various factors that drive the observed changes in the diazotrophic community structure. We observed an increase in local diazotrophic diversity (α-diversity) with a decrease in community turnover (β-diversity), associated with a shift in diazotrophic community structure as a result of the forest-to-pasture conversion. Both diazotrophic community turnover and structure showed signs of recovery in secondary forests. Changes in the diazotrophic community were primarily driven by the change in land use rather than differences in geochemical characteristics or geographic distances. The diazotroph communities in secondary forests resembled those in primary forests, suggesting that at least partial recovery of diazotrophs is possible following agricultural abandonment. IMPORTANCE The Amazon region is a major tropical forest region that is being deforested at an alarming rate to create space for cattle ranching and agriculture. Diazotrophs (nitrogen-fixing microorganisms) play an important role in supplying soil N for plant growth in tropical forests. It is unknown how diazotrophs respond to deforestation and whether they can recover in secondary forests that establish after agriculture is abandoned. Using high-throughput sequencing of nifH genes, we characterized the response of diazotrophs' β-diversity and identified major drivers of changes in diazotrophs from forest-to-pasture and pasture-to-secondary-forest conversions. Studying the impact of land use change on diazotrophs is important for a better understanding of the impact of deforestation on tropical forest ecosystem functioning, and our results on the potential recovery of diazotrophs in secondary forests imply the possible restoration of ecosystem functions in secondary forests., (Copyright © 2020 American Society for Microbiology.)

Belowground ecosystem processes can be highly variable and difficult to predict using microbial community data. Here, we argue that this stems from at least three issues: (a) complex covariance structure of samples (with environmental conditions or spatial proximity) can make distinguishing biotic drivers a challenge; (b) communities can control ecosystem processes through multiple mechanisms, making the identification of these controls a challenge; and (c) ecosystem function assessments can be broad in physiological scale, encapsulating multiple processes with unique microbially mediated controls. We test these assertions using methane (CH 4 )-cycling processes in soil samples collected along a wetland-to-upland habitat gradient in the Congo Basin. We perform our measurements of function under controlled laboratory conditions and statistically control for environmental covariates to aid in identifying biotic drivers. We divide measurements of microbial communities into four attributes (abundance, activity, composition, and diversity) that represent different forms of community control. Lastly, our process measurements differ in physiological scale, including broader processes (gross methanogenesis and methanotrophy) that involve more mediating groups, to finer processes (hydrogenotrophic methanogenesis and high-affinity CH 4 oxidation) with fewer mediating groups. We observed that finer scale processes can be more readily predicted from microbial community structure than broader scale processes. In addition, the nature of those relationships differed, with broad processes limited by abundance while fine-scale processes were associated with diversity and composition. These findings demonstrate the importance of carefully defining the physiological scale of ecosystem function and performing community measurements that represent the range of possible controls on ecosystem processes., (© 2020 John Wiley & Sons Ltd.)

Co-occurrence networks allow for the identification of potential associations among species, which may be important for understanding community assembly and ecosystem functions. We employed this strategy to examine prokaryotic co-occurrence patterns in the Amazon soils and the response of these patterns to land use change to pasture, with the hypothesis that altered microbial composition due to deforestation will mirror the co-occurrence patterns across prokaryotic taxa. In this study, we calculated Spearman correlations between operational taxonomic units (OTUs) as determined by 16S rRNA gene sequencing, and only robust correlations were considered for network construction (-0.80 ≥ P ≥ 0.80, adjusted P < 0.01). The constructed network represents distinct forest and pasture components, with altered compositional and topological features. A comparative analysis between two representative modules of these contrasting ecosystems revealed novel information regarding changes to metabolic pathways related to nitrogen cycling. Our results showed that soil physicochemical properties such as temperature, C/N and H++Al3+ had a significant impact on prokaryotic communities, with alterations to network topologies. Taken together, changes in co-occurrence patterns and physicochemical properties may contribute to ecosystem processes including nitrification and denitrification, two important biogeochemical processes occurring in tropical forest systems.

Several studies have shown that rainfall seasonality, soil heterogeneity, and increased nitrogen (N) deposition may have important effects on tropical forest function. However, the effects of these environmental controls on soil microbial communities in seasonally dry tropical forests are poorly understood. In a seasonally dry tropical forest in the Yucatan Peninsula (Mexico), we investigated the influence of soil heterogeneity (which results in two different soil types, black and red soils), rainfall seasonality (in two successive seasons, wet and dry), and 3 years of repeated N enrichment on soil chemical and microbiological properties, including bacterial gene content and community structure. The soil properties varied with the soil type and the sampling season but did not respond to N enrichment. Greater organic matter content in the black soils was associated with higher microbial biomass, enzyme activities, and abundances of genes related to nitrification ( amoA ) and denitrification ( nirK and nirS ) than were observed in the red soils. Rainfall seasonality was also associated with changes in soil microbial biomass and activity levels and N gene abundances. Actinobacteria , Proteobacteria , Firmicutes , and Acidobacteria were the most abundant phyla. Differences in bacterial community composition were associated with soil type and season and were primarily detected at higher taxonomic resolution, where specific taxa drive the separation of communities between soils. We observed that soil heterogeneity and rainfall seasonality were the main correlates of soil bacterial community structure and function in this tropical forest, likely acting through their effects on soil attributes, especially those related to soil organic matter and moisture content. IMPORTANCE Understanding the response of soil microbial communities to environmental factors is important for predicting the contribution of forest ecosystems to global environmental change. Seasonally dry tropical forests are characterized by receiving less than 1,800 mm of rain per year in alternating wet and dry seasons and by high heterogeneity in plant diversity and soil chemistry. For these reasons, N deposition may affect their soils differently than those in humid tropical forests. This study documents the influence of rainfall seasonality, soil heterogeneity, and N deposition on soil chemical and microbiological properties in a seasonally dry tropical forest. Our findings suggest that soil heterogeneity and rainfall seasonality are likely the main factors controlling soil bacterial community structure and function in this tropical forest. Nitrogen enrichment was likely too low to induce significant short-term effects on soil properties, because this tropical forest is not N limited., (Copyright © 2018 American Society for Microbiology.)

Analysis of patterns in the distribution of taxa can provide important insights into ecological and evolutionary processes. Microbial biogeographic patterns almost always appear to be weaker than those reported for plant and animal taxa. It is as yet unclear why this is the case. Some argue that microbial diversity scales differently over space because microbial taxa are fundamentally different in their abundance, longevity and dispersal abilities. Others have argued that differences in scaling are an artifact of how we assess microbial biogeography, driven, for example, by differences in taxonomic resolution, spatial scale, sampling effort or community activity/dormancy. We tested these alternative explanations by comparing bacterial biogeographic patterns in soil to those of trees found in a forest in Gabon. Altering taxonomic resolution, excluding inactive individuals, or adjusting for differences in spatial scale were insufficient to change the rate of microbial taxonomic turnover. In contrast, we account for the differences in spatial turnover between these groups by equalizing sampling extent. Our results suggest that spatial scaling differences between microbial and plant diversity are likely not due to fundamental differences in biology, and that sampling extent should be taken into account when comparing the biogeographic patterns of microorganisms and larger organisms.