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Canoco

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CANOCO is a multivariate statistical software package designed for the analysis of ecological data. It provides a range of analytical methods, including ordination techniques, regression analysis, and time series analysis, to help researchers explore and interpret complex ecological datasets.

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17 protocols using canoco

1

Comparative Bacterial Community Analysis in Estuaries

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The distribution and abundance matrix of OTUs was randomly resampled using “daisychopper.pl” (Gilbert et al., 2009 (link)) to equalize sampling efforts. After normalizing, 4354 sequences were left for each sample. Alpha diversity measures including richness estimator Chao 1 (Chao and Bunge, 2002 (link)), diversity index Shannon (Magurran, 1988 ) and Good's coverage (Good, 1953 ) were calculated at a 3% dissimilarity level in Mothur. For the beta diversity, a multi-sample similarity dendrogram was constructed according to the OTUs composition and abundance based on Bray-Curtis dissimilarity. Pairwise analyses of similarities (ANOSIM) of bacterioplankton communities were calculated in PRIMER 5 (Plymouth Marine Laboratory, West Hoe, Plymouth, UK). A ternary plot was generated in R software (RDC Team, 2008 ) to compare the community composition of different groups at the genus level. Redundancy analysis (RDA) with Monte Carlo test was performed to calculate the relationship between bacterial clades and water properties at both order and genus levels, following the results of pretested detrended correspondence analysis using Canoco (Version 4.5, Microcomputer Power). A neighbor-joining phylogenetic tree comparing bacterial community compositions in different estuaries was constructed by MEGA 5 (Tamura et al., 2011 (link)).
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2

Analyzing Bacterial Community Diversity

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The statistical significance of the comparisons between multiple groups was carried out by ANOVA, followed by Tukey’s test. A 95% confidence interval was considered significant and was defined as P < 0.05. All values are expressed as the mean ± standard error of the mean (SEM). Each value is the mean of at least three separate experiments. Principal component analysis (PCA) and cluster analysis were used to analyze the terminal restriction fragment (TRF) profiles generated from the T-RFLP experiment, and were combined with the diversity index to study the bacterial communities. PCA plots were generated using the multivariate statistics software Canoco (version 4.5, Microcomputer Power, Ithaca, NY, United States). The biodiversity was measured using the Shannon-Wiener index (H = -∑pi•lnpi); the Simpson Index (D = 1-∑pi^2); and the Evenness Index (E = H/lnS) according to the relative height of each TRF (pi) and sum of the number of TRFs (S) in a sample.
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3

Phytoremediation of Cadmium-Contaminated Soil

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The percentage of the difference of Cd in the soil before and after phytoremediation to the total amount of Cd before phytoremediation was calculated in a decrease ratio of Cd (MDR). The ability of the plants to extract Cd from the soil was indicated by the bioconcentration factor (BCF). The calculation formulas are as follows: MDR=C0CmC0×100%
In which:
C0—the content of total Cd in soil before phytoremediation, mg kg−1Cm—the content of total Cd in soil after phytoremediation, mg kg−1 BCF=CpC0×100%
In which:
Cp—the content of Cd in the plant, mg kg−1Three parallel samples were tested for each indicator, and the final test result was represented by mean ± standard deviation. IBM SPSS Statistics (Version 25.0, Armonk, NY, USA: IBM Corp.) was used for statistical analysis of the experimental data, and the significant differences were analyzed by LSD (least significant difference) method at the level of p < 0.05. RDA analysis was performed by Canoco (Version 5, Microcomputer Power, Ithaca, NY, USA).
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4

Soil Microbiome and Antibiotic Relationships

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Canonical correlation analysis (CCA) and redundancy analysis (RDA) were performed to explore the relationships between basic soil properties and residual antibiotics or microbial communities. Statistical analyses were conducted using SAS for Windows (version 9.2; SAS Institute Inc., Cary, NC, USA). The RDA was conducted using CANOCO (version 5.0; Microcomputer Power, Ithaca, NY, USA), and principal coordinate analysis (PCoA), heatmap, and correlation analyses were performed using R (version 3.4.1, https://cran.r-project.org/bin/windows/base/, accessed on 2 March 2023).
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5

Soil Characteristics Influence Gene Copy

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Relationships between gene copy numbers and soil physicochemical properties (described in Lee et al., 2012 (link)) for 16 samples (four sampling sites in each of the four Dry Valleys) were analyzed using multivariate ordination tools. Redundancy analysis (RDA) was selected as the preferred ordination method (ter Braak and Smilauer, 2002 ) and performed using CANOCO (version 4.5, Microcomputer Power, Ithaca, NY, USA). For RDA, environmental variables (i.e., pH, electrical conductivity, gravimetric water content, C/N, Mg, Cr, Mn, Co, Ni, and Cu) were normalized to a mean of 0 and SD of (1). Monte Carlo permutation test was used to assess the statistical significance of the relationships.
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6

Soil Phosphorus Dynamics in Forest Stands

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Data (i.e., P concentrations and physico-chemical properties) were tested for normality using IBM SPSS Statistics 17 (IBM Corp., Armonk, NY, United States). If data were not satisfied normality and homoscedasticity tests, and log-transformed were performed. Differences in P concentrations and physico-chemical properties in each stand age for the same season were tested using one-way ANOVA and Duncan’s multiple range post hoc tests at p < 0.05. Two-way ANOVA was used to test the effects of stand age, sampling season, and their interactions on soil P fractions. The non-parametric multivariate statistical test of dissimilarity (MRPP) was used to evaluate variations in the composition of phoD-harboring microbial communities among treatments using the vegan package in R version 3.5.1. Furthermore, principal component analysis (PCA) was performed to detect dissimilarity in phoD-harboring microorganisms using the vegan package in R version 3.5.1. Redundancy analysis (RDA) was performed to identify the major factors influencing the composition of phoD-harboring microbial communities using CANOCO (version 5.0, Microcomputer Power, Inc., Ithaca, NY, United States). Environmental factors, including SOC, pH, TN, TP, MBP, ALP activity, CaCl2-P, citrate-P, enzyme-P, HCl-P, and exchangeable Mg and Ca, were used in RDA.
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7

Spatial Variability of Polycyclic Aromatic Hydrocarbons

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Non-metric multidimensional scaling (NMDS) and analysis of similarity (ANOSIM; permutations = 999) were executed with the vegan package (Version 2.0-2, Free Software Foundation, Inc., Boston, USA) of R v.2.8.1 project. The differences of the levels of PAHs among the three land use types were investigated using ANOSIM with R software (2.15.2, R Core Team, Vienna, Austria). We used Sigmaplot software to show the results of the analysis. To identify the relationship among PAHs and selected environmental factors, we performed a multivariate redundancy analysis (RDA) based on a Monte Carlo permutation to explore the correlations of the environmental properties with the spatial variability of PAHs. The software CANOCO (version 4.5, Microcomputer Power, NY, USA) was used to perform the RDA.
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8

Analysis of Fungal Community Diversity

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Cluster analysis of the DGGE profiles was performed with QuantityOne software (version 4.2; BioRad), as described previously.26 (link) After background subtraction, the lanes were normalized to compensate for differences in the migration distance of the DNA in each lane. The bands in each lane were detected automatically to create matching profiles. Moreover, the matching profiles were then used to construct a dendrogram using unweighted pair group method with arithmetic average. Dendrograms generated using this method was applied for the analysis of clustering patterns between different lanes. The intensity of fragments was expressed as a proportion (%) of the sum of all fragments in the same lane of the gel.27 (link) Species richness and diversity of fungal community were calculated as number of bands and as weighted diversity scores according to Shannon and Weaver, respectively.28,29 Principal component analysis (PCA) plots were generated on the basis of the relative abundance of DGGE bands using the multivariate statistics software, Canoco (version 4.5; Microcomputer Power, Ithaca, NY).
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9

Microbial Community Analysis in Sediments

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The alpha-diversity indices and Bray-Curtis similarities between samples were computed using Primer 6 (Plymouth Marine Laboratory, UK). Jaccard similarity index, bacterial community compositions and relative abundance comparisons were analyzed by R software. One-way analysis of community similarity (ANOSIM) of different layers and non-metric multidimensional scaling (NMDS) were performed in Primer 6. A standard Mantel test at each sampling layer was run in R to evaluate the correlation between horizontal geographic distance and bacterial community compositions (based on the abundances of the top 50 OTUs). Redundancy analysis (RDA) was implemented with Monte Carlo permutation tests to analyze variations in the bacterial assemblages under the constraint of environmental factors by Canoco (Version 5, Microcomputer Power). Significant environmental parameters without multicolinearity effects (variance inflation factor < 20) were obtained to explain community variability31 (link). BIOENV and BVSTEP analyses were then performed in order to statistically determine correlations between community compositions and environmental variables.
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10

ITSF/ITSReub and ITS1F/ITS4 RISA Protocol

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RISA was performed as described by Saito et al. (48 ) using two primer sets: the bacterial primer for ITSF/ITSReub and the fungal primer for ITS1F/ITS4. ITSF and ITS1F were labeled by 6-carboxyfluorescein-aminohexyl amidite. After electrophoresis, digital fingerprinting images were obtained with a fluorescent scanner (FLA-2000; Fujifilm, Tokyo, Japan). Band patterns were analyzed using FPQuest Software (Bio-Rad, Hercules, CA, USA). A principal-component analysis (PCA) was performed using CANOCO (version 4.5 for Windows; Microcomputer Power, Ithaca, NY, USA) with default parameters (except that intersample scaling was used) to generate ordination plots based on the scores of the first two principal components.
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