Ordination Study Notes
Awesome ordination site: Michael Palmer from OK State http://ordination.okstate.edu/
This PDF also has a quick summary of interpretation, including R code at the end:
http://www.planta.cn/forum/files_planta/chapter_10_analysis_of_ecological_distance_by_ordination_162.pdf
Chart of Differences and Similarities
Type of
Distribution
Type of
Response
Type of
Predictor
Type of
Gradient
Analysis
Direct
Type of
Scaling
Method
Unconstrained
Type of
Result
Pros
Cons
Verification
Method
PCA
Parametric
Continuous
Continuous
R2 value
Deals with
missing
values
-
Continuous
and
Categorical
Direct
Constrained
Adjusted
R2 value
May be
used for
hypothesis
testing
Continuous
Categorical
Indirect
Constrained
No R2
value
Continuous
Categorical
Indirect
Unconstrained
No R2
value
May be
used for
hypothesis
testing
Doesn’t
assume
normality.
Can deal
with
categorical
data
Distorted
gradient near
edges due to
linear
assumptions
Interactions
are not
automatically
calculated
and must be
added to
environ. vars.
Only strong
with
unimodal
data
Distortion of
results, loss
of
information
due to
ranking
RDA
Parametric
Continuous
CCA
Parametric
NMDS
NonParametric
-
-
MonteCarlo
Permutation
(1) Why do ordinations?
To visualize the relationships within your data. They are especially helpful when dealing with large datasets
including thousands of samples, species, or locations. By focusing on a low-dimensional space, we can easily
reduce noise (Gauch, 1982)
(2) What do ordinations do?
Data are multidimensionally plotted along gradients to reveal interactions and patterns. We are able to
determine the relative importance of gradients. Response variables (eg E. coli abundance) are visually described
against predictor variables (eg seasonal precipitation). Axes (linear combinations of the predictors) are extracted
to sequentially explain the most variation in the responses. Ordinations establish ecological
similarity/dissimilarity. Sites/variables of interest are ordered based on dissimilarity.
(3) What is the difference between a direct and an indirect gradient analysis?
Direct analyses present responses directly against predictors and indirect analyses present responses against
clusters of predictors.
(4) Examples of plots and how to interpret them
- Arrows show the direction and relatedness of predictors.
- Arrows with smaller angles between them are more correlated.
- Sites closer together are more similar.
- Sites towards the point of an arrow are more strongly positively related to that predictor. Sites towards the tail
of an arrow or further in that direction are more strongly negatively related to that predictor.
- Site toward the origin of the plot are more generalist and are not strongly influenced by the predictors.
PCA – Vermaire et al, 2011
RDA – Vermaire et al, 2011
CCA – Nalesso et al, 2005
NMDS – Metz et al, 2011
References
Gauch, H.G. Jr, 1982. Multivariate analysis in community structure. Cambridge University Press, Cambridge.
Metz, M.R., K.M. Frangioso, R.K. Meentemever, D.M. Rizzo. 2011. Interacting disturbances: wildfire severity affected
by stage of forest disease invasion. Ecological Applications 21:313-320.
Nalesso, R.C., J-C. Joyeux, C.O. Quintana, E. Torezani, A C Paz Otegui, 2005. Soft-bottom macrobenthic communities
of Vitoria Bay estuarine system, South-eastern Brazil. Brazil Journal of Oceanography, 53(1/2):23-38.
Vermaire, J.C., Y.T. Prairie, I. Gregory-Eaves, 2011. The influence of submerged macrophytes on sedimentary diatom
assemblages