Hi! My group and I are working on our undergraduate thesis about comparative study of intertidal gastropods in protected and unprotected areas. We measured the physicochemical parameters (temperature, pH, salinity, electrical conductivity, dissolved oxygen, and substrate type) in three replicates per area during sampling. Our MANOVA showed significant difference between the two areas based on the physicochemical parameters, except for temperature.
We collected 782 individual gastropods from the unprotected area and 665 individuals from the protected area. Only a few species were present in both areas.
Our CCA resulted in an Eigenvalue of 0.766 and a p-value of 1, using N = 999.
We also used One-way ANOSIM with a Bray-Curtis matrix: N = 9999; mean rank within = 2; mean rank between = 4.25; R = 0.75; p = 0.3348.
My question is: Is our data still biologically meaningful even if the p-values are > 0.05? Should we still use CCA and ANOSIM, or would you suggest more appropriate statistical analysis tools?
Thank you very much!
Interpretation:
1. No Statistically Significant Group Differences
A p-value greater than 0.05 suggests that you do not have enough evidence to reject the null hypothesis.
In this context, the null hypothesis usually posits that there is no significant difference between the groups or no significant relationship between species and environmental variables.
2. For CCA:
The environmental variables included in your model do not significantly explain the variation in the species composition.
Your ordination may still show some trends, but these are not statistically robust.
3. For ANOSIM:
The R statistic may be close to 0, indicating minimal separation between your predefined groups (e.g., treatment types, sites, time periods).
The observed dissimilarities are not greater than what would be expected by chance.
Possible Reasons for Non-Significant Results:
Low sample size or replication
High variability within groups
Overlapping group structures
Weak relationship between variables (in CCA)
Inappropriate group definitions or too many groups
What You Can Do Next:
Check model assumptions and data quality.
Re-express or transform data (e.g., log-transform species counts).
Explore alternative groupings or subsets.
Try other multivariate methods like PERMANOVA, NMDS, or RDA.
Ensure environmental variables are ecologically meaningful and not collinear.
Hi,
The results of CCA and ANOSIM based on the data and methods used. when Canonical Correspondence Analysis shows p-values > 0.05, this means the environmental variables do not explain a significant portion of the variation in species composition. There is no statistically significant relationship between them (your species data and the environmental variables).
If Analysis of Similaritiesshows p-values > 0.05, this indicates that the groups are not meaningfully different in terms of their species composition.This means there is no statistically significant difference in community composition between your data and the environmental variables.
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