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Microbial diversity declines in warmed tropical soil and respiration rise exceed predictions as communities adapt


Perturbation of soil microbial communities by rising temperatures could have important consequences for biodiversity and future climate, particularly in tropical forests where high biological diversity coincides with a vast store of soil carbon. We carried out a 2-year in situ soil warming experiment in a tropical forest in Panama and found large changes in the soil microbial community and its growth sensitivity, which did not fully explain observed large increases in CO2 emission. Microbial diversity, especially of bacteria, declined markedly with 3 to 8 °C warming, demonstrating a breakdown in the positive temperature-diversity relationship observed elsewhere. The microbial community composition shifted with warming, with many taxa no longer detected and others enriched, including thermophilic taxa. This community shift resulted in community adaptation of growth to warmer temperatures, which we used to predict changes in soil CO2 emissions. However, the in situ CO2 emissions exceeded our model predictions threefold, potentially driven by abiotic acceleration of enzymatic activity. Our results suggest that warming of tropical forests will have rapid, detrimental consequences both for soil microbial biodiversity and future climate.

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Fig. 1: Microbial diversity decline and community change under in situ soil warming in lowland tropical forest.
Fig. 2: Response of microbial growth and enzyme activity to soil warming, and the relationship between this temperature response and microbial community changes.
Fig. 3: The response of soil CO2 efflux to in situ warming is greater than the increase predicted by the temperature response of microbial respiration and growth.

Data availability

Trimmed (primers removed) sequence data generated in this study are deposited in the European Nucleotide Archive (ENA) under Project Accession number PRJEB45074 (ERP129199), sample accession numbers ERS6485270ERS6485284 (16S rRNA) and sample accession numbers ERS6485285ERS6485299 (ITS). Raw fastq files can be accessed through the Smithsonian figshare at (16S rRNA) and (ITS). Related data and data products for individual analysis workflows are available through the Smithsonian figshare under the collection

Code availability

All code, reproducible workflows and further information on data availability can be found on the project website at The code embedded in the website is available on GitHub ( in R Markdown format. The version of code used in this study is archived under SWELTR Workflows v1.0 (, DOI identifier


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This study was supported by three fellowships to A.T.N.: UK NERC grant NE/T012226, European Union Marie-Curie Fellowship FP7-2012-329360 and an STRI Tupper Fellowship. Further support came from UK NERC grant NE/K01627X/1 to P.M., an ANU Biology Innovation grant to P.M. and Simons Foundation grant No. 429440 to W. Wcislo, STRI, and support from the US Department of Agriculture (USDA), Agricultural Research Service to K.B. We thank O. Acevado, D. Agudo, A. Bielnicka, G. Broders, M. Cano, D. Dominguez, M. Garcia, M. Larsen, J. Rodriguez, H. Szczygiel, I. Torres, E. Velasquez, W. Wcislo, K. Winter and J. Wright for support. We thank B. Turner for his contribution to SWELTR, especially during its initial phase of operation. Sequencing analyses were conducted on the Smithsonian High-Performance Cluster (SI/HPC), Smithsonian Institution ( For the purpose of open access, the authors have applied a Creative Commons Attribution (CC BY) licence to any author-accepted manuscript version arising from this submission. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the USDA. The USDA is an equal opportunity provider and employer.

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Authors and Affiliations



A.T.N. conceived the study. A.T.N., J.J.S., M.M.-S., J.P., E.B., K.B. and K.S. performed the study. A.T.N. and J.J.S. analysed the data. A.T.N. wrote the paper with input from J.J.S., E.B., K.S., K.B. and P.M.

Corresponding author

Correspondence to Andrew T. Nottingham.

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The authors declare no competing interests.

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Nature Microbiology thanks Nadia Maaroufi, Ashish Malik and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 One of five warmed plots at SWELTR.

The images show the soil surface temperature shortly after the warming structure was switched on (a and c) and after a period of thermal equilibration‘ (b and d). The circular heating structure was 3.5 m in diameter and extended to 1.2 m depth, which resulted in an effective heated plot of approximately 5 m diameter x > 1.5 m depth (that is to the bedrock, situated at around 1.5–2.0 m across the study site). The experiment consisted of five warmed and control plot-pairs in total. For this study we had three treatment levels, +3 °C warming (within the warmed plots), +8 °C (within a high-temperature buffer zone close [~10 cm] to the heating source for each warmed plot) and ambient temperature controls (within the control plots). Therefore, all analyses are for n = 5 independent sampling locations for each treatment level. Image credit: J. Bujan and E. Velasquez.

Extended Data Fig. 2 Diversity response of soil bacteria (a–c) and fungi (d–f) to two years of warming by +3 °C and +8 °C.

Shapiro-Wilk Normality and Bartlett tests indicated all alpha diversity estimates (following PERfect filtering) were normally distributed and differences were assessed for (a) bacteria and (d) fungi using analysis of variance (ANOVA) followed by Tukey HSD post-hoc tests. Compositional similarity of microbial communities (beta-diversity) represented as PCoA ordination plots of PERfect filtered data for (b) bacteria—estimated using Unweighted (left) and Weighted Unifrac (right) distance matrices; and (e) fungi estimated—using Jensen–Shannon divergence (left) and Bray-Curtis (right) distance matrices. Within group distances for the (c) bacteria and (f) fungi datasets. The centre line of each box plot represents the median, the lower and upper hinges represent the first and third quartiles and whiskers represent + 1.5 the interquartile range. For panels (a), (c), (d), and (f), only significant differences between treatments are shown.

Extended Data Fig. 3 The response of select soil bacteria taxa to two years of warming by +3 °C and +8 °C.

Differences assessed for multiple-group pair-wise comparisons using ANOVA followed by Tukey HSD post hoc tests. PERfect filtered read count data was log10 transformed and normalized using total sum scaling (TSS). The centre line of each box plot represents the median, the lower and upper hinges represent the first and third quartiles and whiskers represent + 1.5 the interquartile range. Only significant differences between treatments are shown.

Extended Data Fig. 4 The response of select soil fungal taxa to two years of warming by +3 °C and +8 °C.

Differences assessed for multiple-group pair-wise comparisons using ANOVA followed by Tukey HSD post hoc tests. PERfect filtered read count data was log10 transformed and normalized using total sum scaling (TSS). The centre line of each box plot represents the median, the lower and upper hinges represent the first and third quartiles and whiskers represent + 1.5 the interquartile range. Only significant differences between treatments are shown.

Extended Data Fig. 5 Distance-based Redundancy Analysis (db-RDA).

Distance-based Redundancy Analysis (db-RDA) of PIME filtered data based on Bray-Curtis dissimilarity showing the relationships between community composition change for (a) bacteria and (b) fungi versus edaphic properties (left) and microbial functional response (right). All analyses are for soil collected from n = 5 independent sampling locations for each treatment level.

Extended Data Fig. 6 Soil, enzyme, and microbial responses to +3 °C and +8 °C in situ soil warming.

Data are grouped by (a) soil properties, (b) microbial functional responses, and (c) microbial temperature adaptive responses; we used the same grouping to test three hypotheses on how each of these responses were correlated to changes in microbial diversity and community composition (Fig. 2; Extended Data Table 2, Fig. 5). All properties were determined for soil samples collected during the 2018 wet season (June and November); see methods. Units for enzyme Vmax are nmol MU g−1 min−1, except Phenol oxidase in μmol g−1 h−1 and Leucine aminopeptidase in nmol AMC g−1 min−1. The centre line of each box plot represents the median, the lower and upper hinges represent the first and third quartiles and whiskers represent + 1.5 the interquartile range. Significant differences between treatments and controls are highlighted by asterisks (ANOVA; * p ≤ 0.05, ** p ≤ 0.01, *** p ≤ 0.001). All analyses are for soil collected from n = 5 independent sampling locations for each treatment level.

Extended Data Fig. 7 Soil enzyme activities in response to incubation temperature (that is instantaneous temperature response determined in laboratory assays).

Data are maximum potential enzyme activity (Vmax), determined by activity under saturating substrate conditions. Enzymes are: α-glucosidase (AGase), β-glucosidase (BGase), phospho-diesterase (BPase), cellolbiohydrolase (CEase), leucine aminopeptidase (LPase), phosphomonoesterase (Pase), N-acetyl β-glucosaminidase (Nase), phenol oxidase (PXase), sulfatase (Sase) and β-xylanase (XYase). Units for enzyme Vmax are nmol MU g−1 min−1, except Phenol oxidase in μmol g−1 h−1 and Leucine aminopeptidase in nmol AMC g−1 min−1. The error bars represent mean ± one standard error, for n = 5 plots. Fitted lines depict quadratic functions with 95% confidence intervals. All analyses are for soil collected from n = 5 independent sampling locations for each treatment level. Activity was determined during the wet season 2018 for the following sampling periods: controls include 4 sampling periods (June, Sept, Oct, Dec 2018); +3 °C include 3 sampling periods (June, Sept, Dec 2018); +8 °C include 1 sampling period (Sept 2018).

Extended Data Table 1 Relationship of bacterial and fungal richness with (a) environmental drivers, (b) microbial functional responses and (c) microbial temperature adaptive responses
Extended Data Table 2 The relationship between (a) bacterial and (b) fungal beta-diversity and edaphic environment (i), soil process rates (ii) and microbial temperature adaptive responses (iii) following 2 years of soil warming by +3 °C to +8 °C
Extended Data Table 3 The influence of soil abiotic environment on soil CO2 efflux (a), and the effect of in situ warming levels (by +3 °C and +8 °C) on soil CO2 efflux (b) and soil moisture (c)

Supplementary information

Supplementary Information

Supplementary Methods, Results, Figs. 1–14 and Tables 1–15.

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Nottingham, A.T., Scott, J.J., Saltonstall, K. et al. Microbial diversity declines in warmed tropical soil and respiration rise exceed predictions as communities adapt. Nat Microbiol 7, 1650–1660 (2022).

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