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Appendix C. STATICO computations.

STATICO computations

Unravelling the environmental correlates influencing the seasonal biodiversity of aquatic Heteropteran assemblages in northern Africa

Heteropteran communities form a key component of aquatic ecosystems but have not been widely studied compared to other freshwater faunal groups. This research examined the environmental parameters influencing the diversity, seasonal distribution and structure of aquatic Heteroptera assemblages in the Mediterranean region of Tunisia, northern Africa. Heteropterans were most abundant during spring and summer, coinciding with the emergence of several species and the most favorable environmental conditions for benthic aquatic fauna. Three-way multivariate analyses (combining community composition data from all sites and seasons) highlighted the longitudinal spatial organization of Heteropteran communities. Headwater regions were dominated by halophobic sensitive taxa, and lowland sites were characterized by high salinity resistant taxa (halophilic taxa). The longitudinal organization was driven by gradients of mineralization (salinity and electrical conductivity) and oxygen (DO, COD and BOD) concentrations. Taxonomic composition differed between river catchments, with significantly higher diversity (taxa richness) in the streams with adjacent riparian forest cover. These sites were characterized by the presence of endemic species, such as Velia africana and Velia eckerleini, and rare species, Notonecta meridionalis, and Aquarius najas. Results recorded highlight the importance of aquatic vegetation and water quality in driving the seasonal and spatial variability of Heteropterans, and provide important information to inform the management and conservation of freshwater biodiversity in Northern Africa

Comparison of the regions considered on the basis of their soil habitats.

<p>A. Soil average dissimilarity of soil habitat for the different regions (number linked to the corresponding region) and position of sites; B. Between group analysis of soil habitats according to the region; C. Correlation circle of the variables defining soil habitat in the between group analysis. The length of the arrow corresponds to the Pearson’s correlation coefficient for quantitative variables and to the correlation ratio for qualitative variables. Symbols: Alt.: Elevation; T°C: Sum of annual temperatures; P_ass: Assimilable P; C:N: Carbon to Nitrogen ratio; C_org: Organic Carbon content.</p

Similar Processes but Different Environmental Filters for Soil Bacterial and Fungal Community Composition Turnover on a Broad Spatial Scale

<div><p>Spatial scaling of microorganisms has been demonstrated over the last decade. However, the processes and environmental filters shaping soil microbial community structure on a broad spatial scale still need to be refined and ranked. Here, we compared bacterial and fungal community composition turnovers through a biogeographical approach on the same soil sampling design at a broad spatial scale (area range: 13300 to 31000 km²): i) to examine their spatial structuring; ii) to investigate the relative importance of environmental selection and spatial autocorrelation in determining their community composition turnover; and iii) to identify and rank the relevant environmental filters and scales involved in their spatial variations. Molecular fingerprinting of soil bacterial and fungal communities was performed on 413 soils from four French regions of contrasting environmental heterogeneity (Landes</p></div

Variance partitioning of bacterial and fungal community composition.

<p>The number indicated in brackets corresponds to the number of samples for the region. Significance levels: ns: not significant; P<0.05: *; P<0.01: **, P<0.001: ***.</p

Regression parameters of the Distance-Decay Relationships for Bacteria and Fungi.

<p>The number of observations per region is provided in brackets beside the name of the region. The community composition turnover rate (z) and the initial similarity are derived from the slope of the regression (−2z) and the mean of similarity at 16 km; respectively. The statistical comparison between region and organism was performed by examining the overlap of the 95% confidence intervals of turnover rates or initial similarities.</p><p>Regression parameters of the Distance-Decay Relationships for Bacteria and Fungi.</p

Distance-Decay Relationships for bacteria and fungi.

<p>Each panel correspond to a region: Brittany (A), Burgundy (B), Landes (C) and South-East (D) Points the average Sørensen’s similarity between sites for each distance class. Lines represent the regression model based on the whole set of paired comparisons; for bacteria (grey) and fungi (black). The equations for the regression models were as follows: (<b>A</b>) Brittany: Bacteria***: log10(Sørensen’s similarity) = −0.014×log10(geographic distance)−0.156; Fungi***: log10(Sørensen’s similarity) = −0.017×log10(geographic distance)−0.350; (<b>B</b>) Burgundy: Bacteria*** log10(Sørensen’s similarity) = −0.018×log10(geographic distance)−0.144; Fungi***: log10(Sørensen’s similarity) = −0.015×log10(geographic distance)−0.316; (<b>C</b>) Landes: Bacteria*: log10(Sørensen’s similarity) = −0.017×log10(geographic distance)−0.198; Fungi ^ns: log10(Sørensen’s similarity) = −0.012×log10(geographic distance)−0.357; (<b>D</b>) South-East: Bacteria***: log10(Sørensen’s similarity) = −0.027×log10(geographic distance)−0.101; Fungi***: log10(Sørensen’s similarity) = −0.019×log10(geographic distance)−0.298. A graph with points representing all paired-comparisions between sites as points can be found in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0111667#pone.0111667.s002" target="_blank">Figure S2</a>. Significance of the model is indicated as an exponent for each organism: ns: not significant; P<0.05: *; P<0.01: **, P<0.001: ***.</p

Variations of microbial communities partitionned according to edaphic variables and space.

<p>For each organism and region, only variables retained in the most parsimonious model are presented and their pure effect is tested by a permutation test. Significance levels are: P<0.05: *; P<0.01: **, P<0.001: ***. Missing values or variables indicate that the variable was not retained in the model. Sand was removed prior to model evaluation since it was represented by the opposite of the sum of silt and clay content. Rainfall: Sum of annual rainfall (mm). Temperature: Sum of annual temperature (°C). Spatial components were summarized according to the scale considered: trend (x, y and z coordinates), coarse, medium or fine. The interval in brackets indicates the numbers of PCNMs retained in the model for each scale. The proportion of variance for each scale was determined as the sum of the pure effects of each PCNM when these were significant. Coarse, medium and fine scales correspond to PCNM with a spatial range of 80 to 120 km, 40 to 65 km and less than 40 km; respectively.</p