The sequencing data analyzed in this study has been deposited in the SRA
NCBI submission portal (BioProject ID PRJNA807934). The R scripts used for
the analysis of the sequencing data can be found in the GitHub page https://
github. com/ Pedro HLebre/ AfSM_ scripts.
Additional file 1. Figure S1. Distribution of samples across the 9 African
countries according to their land cover (LC) classification. Land cover
codes used were the following: LC_1 - Rainfed croplands; LC_2 - Mosaic
Cropland (50-70%) / Vegetation (grassland, shrubland, forest) (20-50%);
LC_3 - Mosaic Vegetation (grassland, shrubland, forest) (50-70%) /
Cropland (20-50%); LC_4 - Closed to open (>15%) broadleaved evergreen
and/or semi-deciduous forest (>5m); LC_5 - Closed (>40%) broadleaved
deciduous forest (>5m); LC_6 - Open (15-40%) broadleaved deciduous
forest (>5m); LC_10 - Mosaic Forest/Shrubland (50-70%) / Grassland (20-
50%); LC_11 - Mosaic Grassland (50-70%) / Forest/Shrubland (20-50%);
LC_12 - Closed to open (>15%) shrubland (<5m); LC_13 - Closed to open
(>15%) grassland; LC_14 - Sparse (>15%) vegetation (woody vegetation,
shrubs, grassland); LC_17 - Closed to open (>15%) vegetation (grassland,
shrubland, woody vegetation) on regularly flooded or waterlogged soil;
LC_18 - Artificial surfaces and associated areas (urban areas >50%); LC_19
- Bare areas.
Additional file 2. Figure S2. Significant (p-value < 0.01) variation of soil
chemistry and climatic variables across African countries. Significance was
calculated using the Kruskal-Wallis test for non-parametric data distributions,
while pair-wise comparison was calculated using the pairwise
Wilcox test. Significant results are indicated using the following nomenclature:
* - p-value < 0.05; ** - p-value < 0.01; *** - p-value < 0.001.
Additional file 3.Figure S3. Average relative abundance of the top
bacterial (A) and fungal (B) taxa across the sampled sub-Saharan Africa
countries.
Additional file 4.Figure S4. Relative abundance of dominant (201 bacterial,
43 Fungal and 7 archaeal) phylotypes across soil samples.
Additional file 5. Figure S5. Relationship between the relative abundance
of dominant phylotypes across soil samples and their main environmental
predictors, as determined by semipartial correlation analysis.
Phylotypes were grouped into environmental categories based on the
correlation between phylotype and its major environmental predictor:
positive correlation with pH – high pH; negative correlation with pH –
low pH; positive correlation with phosphate – high Phosphate; negative
correlation with phosphate – low Phosphate; negative correlation with
Sodium – low Sodium.
Additional file 6. Figure S6. A-priori ecological model tested using SEM.
MAP and MAP are represented as exogenous variables (black rectangles),
soil chemistry and vegetation index are represented as endogenous
variables (blue rectangles), while the Shannon diversity and abundance
of PGPT are represented as response variables (green rectangles). The
color and direction of the arrows represent the nature and direction of the
causal relationships between variables: red – negative relationship; black –
positive relationship.
Additional file 7. Figure S7. MIROC6 model predictions for mean annual
temperature (oC) (A) and mean annual precipitation (mm) (B) under
too different GH emission scenarios (SSP126 and SSP585), predicted for
2040-2060 and 2080-2100 temporal windows. The predicted datasets are
grouped according to country, as indicated by the vertical dashed lines.
Additional file 8. Figure S8-A. Predicted prokaryotic Shannon biodiversity
index values (expressed as natural log scale) in soils of the 9 sub-
Saharan Africa countries used in this study, for 2040-2060 and 2080-2100
under two distinct GH emission scenarios (SSP126 and SSP585), and
comparison with current predicted Shannon biodiversity as estimated
by SEM. Pairwise significance values of differences in biodiversity means
between the different years and scenarios are represented by the brackets
with the following nomenclature: * - p-value < 0.05; ** - p-value < 0.01;
*** - p-value < 0.001.
Additional file 9. Figure S8-B. Predicted abundance values of PGPB
(expressed as natural log scale) in soils of the 9 sub-Saharan Africa countries
used in this study, for 2040-2060 and 2080-2100 under two distinct
GH emission scenarios (SSP126 and SSP585), and comparison with current predicted Shannon biodiversity as estimated by SEM. Pairwise significance
values of differences in biodiversity means between the different years
and scenarios are represented by the brackets with the following nomenclature:
* - p-value < 0.05; ** - p-value < 0.01; *** - p-value < 0.001.
Additional file 10. Figure S8-C. Predicted fungal Shannon biodiversity
values (expressed as natural log scale) in soils of the 9 sub-Saharan Africa
countries used in this study, for 2040-2060 and 2080-2100 under two
distinct GH emission scenarios (SSP126 and SSP585), and comparison with
current predicted Shannon biodiversity as estimated by SEM. Pairwise significance
values of differences in biodiversity means between the different
years and scenarios are represented by the brackets with the following
nomenclature: * - p-value < 0.05; ** - p-value < 0.01; *** - p-value < 0.001.
Additional file 11. Figure S8-D. Predicted abundance values of PGPF
(expressed as natural log scale) in soils of the 9 sub-Saharan Africa countries
used in this study, for 2040-2060 and 2080-2100 under two distinct
GH emission scenarios (SSP126 and SSP585), and comparison with current
predicted Shannon biodiversity as estimated by SEM. Pairwise significance
values of differences in biodiversity means between the different years
and scenarios are represented by the brackets with the following nomenclature:
* - p-value < 0.05; ** - p-value < 0.01; *** - p-value < 0.001.
Additional file 12. Table S1. Metadata for all the sites used in the study,
which include the latitude and longitude GPS coordinates, physicochemical
properties of the sample soils, macroclimatic variables for each site,
and soil texture and land cover classifications based on the macroclimatic
variables.
Additional file 13. Table S2. Taxonomy of prokaryotic taxa in the dominant
fraction of the microbial community, at the Class taxrank.
Additional file 14. Table S3. Metadata of the dominant phylotypes,
including taxonomy, functional predictions (based on FAPROTAX and
manual curation), and ecological groups based on the main environmental
predictor.
Additional file 15. Table S4. Table with the semi-partial correlation
analysis results, in which the correlation values (r) and associated p-values
of the variable with the highest correlative value are displayed for each
dominant phylotype that was significantly (p-value < 0.05) correlated with
environmental factors.
Additional file 16. Table S5. Taxonomy of the taxa considered as
plant-growth-promoting
Additional file 17. Table S6. Net estimates and corresponding significance
values for the environmental variables associated with soil health in
the SEM model.
Additional file 18. Table S7. Number of samples allocated for each
country, and number of samples collected.
Additional file 19. Table S8. Variable codes, meaning and units for the
environmental variables used in this study.
