Hello,
-So I have a set of bacterial communities extraced from rhizospheric soil in both saline and control environments for two different cultivars of plants, one is tolerant to salinity and the other is susceptible.
-I did ordination (N-MDS)and got the control and treated separated on first coordinate, but cultivars closer together in the second coordinate.
- I got p-value p=0.001, whic is good, indicating diffrence.
-However, i got R^2 values: axis1 = 0.9521, axis2= 0.0005229.
What does R^2 mean? what values indicate that my data is good. Is it a strong test for my data?
The r-square value generally tells you the percent of the variation 'explained' by the axis. So this score tells you that Axis 1 'explains' approximately 95% of the variation in separation of the bacterial communities, whereas Axis 2 explains very little of the variation. Therefore, any environmental variable that aligns strongly with Axis 1 is likely to be a strong environmental influence on the bacterial communities. Conversely, an environmental variable that is strongly aligned with Axis 2 is much less likely to be influencing the communities, although they may be important in the absence of environmental variables that strongly align with Axis 1. For example, if Axis 1 is very strongly aligned with the salinity gradient, then it suggests that salinity is overriding most other environmental variables (and if you did a separate study with no salinity gradient, then other variables may become more important in separating the communities).
The r-square value generally tells you the percent of the variation 'explained' by the axis. So this score tells you that Axis 1 'explains' approximately 95% of the variation in separation of the bacterial communities, whereas Axis 2 explains very little of the variation. Therefore, any environmental variable that aligns strongly with Axis 1 is likely to be a strong environmental influence on the bacterial communities. Conversely, an environmental variable that is strongly aligned with Axis 2 is much less likely to be influencing the communities, although they may be important in the absence of environmental variables that strongly align with Axis 1. For example, if Axis 1 is very strongly aligned with the salinity gradient, then it suggests that salinity is overriding most other environmental variables (and if you did a separate study with no salinity gradient, then other variables may become more important in separating the communities).
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