Introduction

During fieldwork you each walked a transect from dune crest down into the slack, recording the species in each quadrat and measuring environmental variables.

Now comes the detective work: how do we make sense of all those numbers? How do we reduce a messy table of species × plots into something we can actually interpret?

This is where ordination comes in. Ordination methods take complex community data and project it into two dimensions so we can see the hidden gradients. In this practical, you’ll use your own data to explore:

What ecological gradients structure the dune vegetation?
Which environmental factors explain these gradients?
Do the patterns in the ordination match what you saw in the field?

The Tools

We will compare three ordination methods. Each reduces multidimensional data into a simpler map, but they differ in assumptions:

PCA (Principal Components Analysis): linear method, best for short gradients.
DCA (Detrended Correspondence Analysis): unimodal method, suited to long gradients with strong turnover.
NMDS (Non-metric Multidimensional Scaling): flexible, rank-based method, fewer distributional assumptions.

As you look at each ordination, ask yourself: which one gives the clearest ecological story for your data?

By the end of this practical you should be able to

Distinguish between PCA, DCA, and NMDS and know when each is appropriate.
Interpret ordination diagrams (sites, species, axes) in ecological terms.
Link vegetation patterns to measured environmental gradients.

Remember that there are different ways of setting up the data table. Recall the difference between long and wide data formats.
- In long format, each row records a single observation (quadrat–species–abundance).
- In wide format, each row is a quadrat and each column is a species, with the entries giving abundances.

Ordination methods in vegan (e.g. PCA, DCA, NMDS) require the wide format: a quadrat × species matrix.
Below is a sample of that matrix from your dataset (showing only a subset of quadrats and species for clarity).

Sample of the species × quadrat matrix (abundances). The total matrix is 45 quadrats (rows) by 46 species (columns).
	Cynaelli	Euclrace	Helicomo	Oleaexas	Resteleo	Crasfili	Metamuri	Zalumari
G01a	1.0	25	2	35	5.0	0	0	0
G01b	0.0	45	0	20	5.0	1	1	0
G01c	0.0	0	0	50	0.0	0	2	1
G02a	0.3	0	1	13	0.5	0	10	0
G02b	0.0	65	0	34	1.0	0	1	0
G02c	0.0	0	0	20	0.0	1	0	1

Generating the ordinations for each method

### PCA
# Hellinger transform is often sensible for community data before PCA
dune_hel <- decostand(vegdat_wide, method = "hellinger")
pca <- rda(dune_hel)

### DCA
dca <- decorana(vegdat_wide)


### NMDS
nmds <- metaMDS(vegdat_wide,trace=0,k=5,try=50,trymax=1000)

Interpreting Ordination Axes

Our sampling design followed a transect from dune crest to slack, capturing the full topographic gradient in vegetation structure. During fieldwork we observed marked shifts in composition: crest plots were distinct from slopes and slacks, with characteristic sets of species at each zone.

The first ordination axis is therefore expected to capture the dominant ecological signal: the transition in community composition from crest → slope → slack.

The second ordination axis reflects the within-zone variation among transects. While the broad crest–slope–slack pattern was consistent, each zone showed slight compositional differences, and these are expressed along the second axis.

In the ordinations below, can you see the pattern described above?

PCA

DCA

NMDS

Reporting the Quality of an Ordination

When you look at the ordination summaries above, you’ll see that each method reports different statistics. These values tell us how well the ordination represents the original data. But the catch is: each ordination type has its own measures of “goodness.”

Task:
- Go and find out what metrics are used for PCA, DCA, and NMDS, and what they mean.
- Write a short statement for each ordination in your report, reporting its quality using the appropriate measure(s).

Hints:

PCA: Look at eigenvalues / proportion of variance explained. Report how much variation in species data is captured by the first few axes.
DCA: Check axis lengths and eigenvalues. Long axis lengths (>4 SD units) imply strong species turnover, while shorter ones suggest weaker gradients.
NMDS: Report the stress value. A stress <0.1 is excellent, <0.2 is usable, >0.3 is poor. Always mention stress in your write-up.

Notice how there isn’t a single universal statistic across ordinations — you must use the metric that belongs to that method.

Below are the outputs from each ordination…

pca

## Call: rda(X = dune_hel)
## 
##               Inertia Rank
## Total          0.6238     
## Unconstrained  0.6238   42
## Inertia is variance 
## 
## Eigenvalues for unconstrained axes:
##     PC1     PC2     PC3     PC4     PC5     PC6     PC7     PC8 
## 0.16909 0.12278 0.09292 0.04894 0.04355 0.02885 0.01771 0.01608 
## (Showing 8 of 42 unconstrained eigenvalues)

dca

## 
## Call:
## decorana(veg = vegdat_wide) 
## 
## Detrended correspondence analysis with 26 segments.
## Rescaling of axes with 4 iterations.
## Total inertia (scaled Chi-square): 4.4091 
## 
##                        DCA1   DCA2   DCA3   DCA4
## Eigenvalues          0.6214 0.4714 0.2937 0.2762
## Additive Eigenvalues 0.6214 0.4649 0.2917 0.2504
## Decorana values      0.6246 0.4134 0.2581 0.1381
## Axis lengths         3.9520 3.0669 2.6355 2.1012

nmds

## 
## Call:
## metaMDS(comm = vegdat_wide, k = 5, try = 50, trymax = 1000, trace = 0) 
## 
## global Multidimensional Scaling using monoMDS
## 
## Data:     wisconsin(sqrt(vegdat_wide)) 
## Distance: bray 
## 
## Dimensions: 5 
## Stress:     0.08691115 
## Stress type 1, weak ties
## Best solution was repeated 12 times in 50 tries
## The best solution was from try 25 (random start)
## Scaling: centring, PC rotation, halfchange scaling 
## Species: expanded scores based on 'wisconsin(sqrt(vegdat_wide))'

As you can see above, each ordination method has its own way of reporting how well it represents the data.
When writing up your results, make sure you report the appropriate statistic(s).

Use the details below to extract the necessary information from the R output above.

PCA (Principal Components Analysis)
- Report the eigenvalues and the percentage of variance explained by the first few axes.
- Example: calculate % variance = eigenvalue / total inertia × 100.
- Always state how much of the total variation is captured by the ordination axes you interpret (e.g. “The first two axes capture 39% of the total variation”.)
DCA (Detrended Correspondence Analysis)
- Report the eigenvalues of the first axes.
- Also report axis lengths (in SD units) — values > 4 indicate complete species turnover, values around 3 indicate substantial turnover, shorter lengths suggest weaker gradients.
- Mention what this means ecologically for your transect.
NMDS (Non-metric Multidimensional Scaling)
- Report the stress value (a measure of fit between the ordination and the distance matrix).
- Rules of thumb: <0.1 = excellent; 0.1–0.2 = usable; >0.3 = poor.
- Always include the number of dimensions used and whether the solution was stable across random starts.

What Else Should Be Reported?

In addition to the formal metrics, think about these:
- Number of dimensions chosen: Did you use 2, 3, or more axes? Why?
- Interpretability: Does the ordination separate crest, slope, and slack in a way that matches your ecological knowledge?
- Biological meaning: Even if an ordination has a “good” metric, it must still make ecological sense.

In other words, don’t just report numbers — explain what they mean for your dune transect dataset.

Adding Group Structure to Ordinations

A raw ordination plot of points is often hard to interpret on its own. To make patterns clearer, ecologists overlay group structures that summarise how plots relate to one another. Common options include:

Convex hulls: draw the smallest polygon around all plots in a group, showing their overall spread.
Ordispiders: connect each plot to its group centroid, highlighting variation within groups.
Ellipses: plot statistical confidence regions around group centroids, showing the “core space” of each group.

These tools make ordinations far more useful by helping us see whether groups of plots (e.g. crest, slope, slack) are distinct, overlapping, or highly variable. In other words, ordination visualisation usually requires adding these layers — the raw scatter of points alone rarely tells the full story.

Below are the ordination plots with convex hulls.
A convex hull is the smallest polygon that encloses a set of points, helping us visualise the overall spread of quadrats belonging to the same habitat group.

For this analysis, quadrats were grouped as follows:

Crest: quadrats 1–3
Slope: quadrats 5–8
Slack: quadrats 12–15

Because the transects were not identical, quadrats 4 and 9–11 were excluded.
This provides the clearest representation of the crest, slope, and slack habitats.

PCA

DCA

NMDS

In addition to convex hulls, another useful way to show group structure in ordinations is with ordispiders.
An ordispider draws straight lines from the group centroid (the average position of all plots in that group) to each of the individual plots.

This allows you to see:

how tightly or loosely the plots in a group cluster around their centroid,
whether some plots are outliers compared to the rest of the group, and
differences in spread between groups (e.g., crest vs. slope vs. slack).

In ecological terms, a shorter set of spider lines means the group is relatively homogeneous in species composition, while longer lines suggest more variation within that habitat type.

PCA

DCA

NMDS

Environmental Drivers

Ordinations show patterns — but what drives them? This is where your environmental measurements come in.

Height Above the Slacks: A Key Gradient

One of the simplest but most powerful variables you measured was the height of each quadrat above the dune slacks. This captures the key environmental gradient from the wettest slacks, up the slopes, and onto the dry dune crests — the same gradient you observed in the field.

To illustrate how ordinations link environmental data to species composition, we plot this variable in two complementary ways:

Raw data — Each quadrat is coloured and sized according to its measured height. This shows the actual distribution of values across ordination space.
Arrow from envfit — The vector points in the direction of maximum correlation between height and the ordination axes, with its length reflecting the strength of the relationship.

By combining the visual gradient of the raw data with the summary arrow from the environmental fit, you can see both the underlying measurements and their statistical interpretation. This demonstrates what an environmental arrow means: it is not abstract, but a concise summary of real field data.

PCA

DCA

NMDS

It is important to note that not all environmental variables will align with the ordination axes. Some may show little or no pattern across the plots, meaning they are not strongly related to the gradients captured in the ordination. For example, soil pH in this system ranges only slightly (7.8–8.4) and shows almost no meaningful separation between crest, slope, and slack quadrats.

Soil pH

We first explore soil pH (ranging from 7.8 to 8.4) by overlaying it on the ordination diagrams and by comparing crest, slope, and slack quadrats using box-and-whisker plots.

Both approaches show that pH does not form a strong gradient across the transects. While the slacks appear to be slightly less alkaline than the crest and slope quadrats, this difference is very small and not biologically meaningful.

In other words, soil pH is relatively uniform across the dune system, and is unlikely to be a major driver of the observed vegetation patterns compared to other environmental factors.

DCA

Box-and-whisker plots

Height above slacks vs soil pH

Soil moisture

In contrast to pH, soil moisture percentage shows a much clearer trend across the transects.
When plotted onto the ordination diagrams, soil moisture increases steadily from the drier dune crests, through the intermediate slopes, and into the wetter dune slacks.

This pattern is also visible in the box-and-whisker plots: crest quadrats have the lowest moisture values, slope quadrats are intermediate, and slack quadrats are the wettest.
These differences are ecologically meaningful, as soil water availability is a major factor shaping species distributions and vegetation structure in dune systems.

DCA

Box-and-whisker plots

Ht vs soil moisture

Reading Multiple Environmental Arrows

When many variables are fitted, interpret vectors systematically:

Direction: points toward increasing values of the variable.
Angle: alignment with an axis ≈ stronger correlation with that axis.
Length: proportional to correlation strength (not effect size on its own). Always report r² and permutation p.
Crowding & collinearity: highly parallel arrows often indicate correlated variables; don’t over-interpret both.
Continuous vs categorical:
- Continuous → arrows (vectors).
- Factors → group centroids (and optionally hulls/ellipses).
Significance & multiple testing: filter to meaningful arrows (e.g., p < 0.05, BH-adjusted). Show the rest faintly or hide.
Scaling: arrows are scaled relative to each other; annotate with r² so their meaning is explicit.
Biological sense-check: numbers help, but keep/feature variables that explain the crest → slope → slack story.

Indirect comparison

Remember that there are two approaches to using vegetation and environmental data in ordinations: the indirect comparison where (typically) the ordination is generated from the vegetation data and the environmental variables are correlated with each axis in the ordination OR the direct comparison where the vegetation and environmental variables are both used in concert to generate the ordination. Here I show the first approach.

Each ordination can be correlated with the environmental variables using the envfit() function. Below is the output of the this indirect comparison. To interpret these, focus on three columns:

r²: how strongly the variable is related to the ordination pattern. Larger values = stronger fit.
Pr(>r): the permutation test p-value. Small values (e.g. < 0.05) mean the fit is unlikely to be due to chance.
Direction (PC1/PC2, DCA1/DCA2, NMDS1/NMDS2 values): the vector points in the direction where the variable increases across ordination space (best viewed on the graph).

Task

Identify which variables are most strongly related to vegetation patterns (highest r², significant p).
Identify which variables are weak or not significant (low r², high p).
Comment on whether these patterns are ecologically meaningful for the dune crest → slope → slack gradient.

Use the numbers in the tables below to write short interpretations for each ordination.

PCA

## [1] "Output from the envfit() function"

## 
## ***VECTORS
## 
##                               PC1      PC2     r2 Pr(>r)    
## Soil_pH                   0.92858 -0.37114 0.1464  0.031 *  
## OrgContent%              -0.83942 -0.54348 0.1444  0.025 *  
## SoilMoisture_%           -0.99010 -0.14039 0.3261  0.001 ***
## HeightAboveLowestPoint_m  0.96293  0.26977 0.7635  0.001 ***
## LightInfiltation          0.81025  0.58608 0.0691  0.221    
## CanopyCover               0.07762 -0.99698 0.0298  0.539    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Permutation: free
## Number of permutations: 999

DCA

## 
## ***VECTORS
## 
##                              DCA1     DCA2     r2 Pr(>r)    
## Soil_pH                  -0.94211 -0.33531 0.1083  0.087 .  
## OrgContent%               0.99940 -0.03456 0.1795  0.010 ** 
## SoilMoisture_%            0.82493  0.56524 0.2820  0.001 ***
## HeightAboveLowestPoint_m -0.99762  0.06895 0.6754  0.001 ***
## LightInfiltation         -0.80531 -0.59286 0.0137  0.751    
## CanopyCover              -0.66102  0.75037 0.0361  0.471    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Permutation: free
## Number of permutations: 999

NMDS

## 
## ***VECTORS
## 
##                             NMDS1    NMDS2     r2 Pr(>r)    
## Soil_pH                  -0.93903 -0.34383 0.1428  0.046 *  
## OrgContent%               0.74642 -0.66548 0.1192  0.063 .  
## SoilMoisture_%            0.98857 -0.15079 0.3670  0.001 ***
## HeightAboveLowestPoint_m -0.85292  0.52204 0.7891  0.001 ***
## LightInfiltation         -0.63090  0.77586 0.1017  0.106    
## CanopyCover               0.04796 -0.99885 0.0598  0.279    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Permutation: free
## Number of permutations: 999

Exploring plant species in ordinations

In this section we focus on the species (the “descriptors”) in ordination space—how they are plotted, what their positions mean, and how to interpret them.

In the first tab, the ordination is shown with species points added (green dots with names, except where labels would be too crowded). The quadrats are shown as grey dots in the background for context.
In the next tab, I present the results of an envfit() analysis, which tests how strongly each species correlates with the ordination axes.
In the following tab, the ordination is also displayed with species coloured, to aid interpretation, according to whether their correlation is statistically significant (here, p < 0.10) or not.

Task: Find species in the ordination plots and compare their placement with their statistics in the envfit table. Which species positions are strongly supported by the data, and which are not?

PCA 1

PCA Species scores

The table shows the direction cosines of each species vector (PC1 and PC2), the coefficient of determination (r²) indicating the strength of correlation with the ordination, and the permutation-based p-value for significance.
Species	PC1	PC2	r2	p_value	sig_codes
Anthaeth	-0.9214	-0.3886	0.01	0.857
Aspaafri	0.3085	-0.9512	0.04	0.452
Carpdeli	-0.1542	-0.9880	0.02	0.691
Chaecamp	-0.8045	-0.5940	0.06	0.248
Chendiff	0.8019	0.5975	0.07	0.200
Chirbacc	-0.6286	0.7777	0.10	0.070	.
Colepulc	-0.3462	-0.9382	0.46	0.001	***
Colpcomp	0.9786	-0.2059	0.02	0.785
Crasfili	0.6615	0.7500	0.00	0.959
Crasglom	0.1253	-0.9921	0.05	0.371
Cynaelli	0.7923	0.6102	0.11	0.053	.
Ehrherec	0.9499	-0.3127	0.05	0.345
Elegmicr	-0.9384	-0.3455	0.02	0.722
Eleolimo	0.8023	0.5969	0.16	0.015
Euclrace	0.8982	0.4396	0.15	0.025
Feliechi	0.8609	0.5088	0.10	0.102
Felilati	0.9386	-0.3450	0.06	0.225
Ficibulb	0.9743	0.2252	0.04	0.485
Ficicape	-0.9791	-0.2033	0.08	0.208
Ficiramo	-0.5152	-0.8570	0.11	0.079	.
Helicomo	0.8218	0.5698	0.11	0.056	.
Helitere	-0.7859	-0.6184	0.02	0.760
Indiglau	-0.9967	-0.0816	0.03	0.665
Laurtetr	0.5851	-0.8110	0.02	0.625
Meseaito	-0.4665	-0.8845	0.04	0.562
Metamuri	0.2124	-0.9772	0.36	0.001	***
Morequer	0.0300	0.9995	0.17	0.014
Oleaexas	0.9146	0.4044	0.77	0.001	***
Osyrcomp	0.9997	-0.0240	0.02	0.793
Oxaldepr	0.9743	0.2252	0.04	0.485
Oxalpunc	0.9794	0.2021	0.04	0.550
Passcory	-0.9101	0.4145	0.34	0.001	***
Passrigi	-0.7985	0.6020	0.05	0.421
Phyleric	-0.7983	0.6023	0.54	0.001	***
Plecserp	0.9977	0.0673	0.06	0.278
Rapagill	0.8645	-0.5026	0.04	0.470
Resteleo	-0.8623	0.5064	0.15	0.025
Robsmari	0.1600	-0.9871	0.05	0.425
Searcren	-0.2016	-0.9795	0.06	0.259
Searglau	0.7450	0.6671	0.01	0.896
Searlaev	-0.1638	-0.9865	0.04	0.501
Seneangu	-0.6590	0.7521	0.06	0.221
Senepurp	-0.8169	-0.5768	0.05	0.317
Sileundu	0.8023	0.5969	0.16	0.015
Vellvell	0.0110	-0.9999	0.06	0.271
Zalumari	0.8401	0.5424	0.17	0.013

PCA 2

DCA

DCA species scores

The table shows the direction cosines of each species vector (DCA1 and DCA2), the coefficient of determination (r²) indicating the strength of correlation with the ordination, and the permutation-based p-value for significance.
Species	DCA1	DCA2	r2	p_value	sig_codes
Anthaeth	0.5938	-0.8046	0.03	0.604
Aspaafri	-0.3811	0.9245	0.04	0.440
Carpdeli	0.2251	-0.9743	0.08	0.195
Chaecamp	0.9601	0.2798	0.03	0.606
Chendiff	-0.3968	-0.9179	0.06	0.251
Chirbacc	0.9290	0.3702	0.03	0.630
Colepulc	0.5269	-0.8499	0.10	0.093	.
Colpcomp	-0.7471	0.6648	0.04	0.430
Crasfili	0.1759	-0.9844	0.10	0.085	.
Crasglom	-0.2961	0.9552	0.14	0.041
Cynaelli	-0.9985	0.0545	0.05	0.306
Ehrherec	-0.9111	0.4123	0.09	0.129
Elegmicr	0.0845	0.9964	0.16	0.021
Eleolimo	-0.6610	-0.7504	0.10	0.102
Euclrace	-0.9560	0.2934	0.19	0.015
Feliechi	-0.6970	-0.7171	0.07	0.230
Felilati	-0.5900	0.8074	0.05	0.366
Ficibulb	-0.7561	0.6544	0.02	0.743
Ficicape	0.9178	-0.3971	0.07	0.240
Ficiramo	0.9930	-0.1180	0.02	0.723
Helicomo	-0.9954	0.0955	0.05	0.303
Helitere	0.3329	0.9430	0.08	0.162
Indiglau	0.6867	-0.7269	0.04	0.601
Laurtetr	0.0438	-0.9990	0.24	0.005	**
Meseaito	0.8881	-0.4597	0.01	0.874
Metamuri	-0.3212	0.9470	0.69	0.001	***
Morequer	0.2310	-0.9730	0.00	0.991
Oleaexas	-0.7952	-0.6064	0.68	0.001	***
Osyrcomp	-0.2928	-0.9562	0.05	0.347
Oxaldepr	-0.7561	0.6544	0.02	0.743
Oxalpunc	-0.4889	-0.8723	0.06	0.211
Passcory	0.7568	-0.6536	0.36	0.002	**
Passrigi	0.9236	0.3833	0.06	0.299
Phyleric	0.9501	0.3121	0.47	0.001	***
Plecserp	-0.8940	0.4480	0.10	0.081	.
Rapagill	-0.2363	-0.9717	0.07	0.218
Resteleo	0.9995	0.0328	0.12	0.046
Robsmari	-0.3225	0.9466	0.12	0.071	.
Searcren	-0.1536	0.9881	0.03	0.552
Searglau	0.2328	-0.9725	0.01	0.855
Searlaev	-0.2287	0.9735	0.13	0.037
Seneangu	0.6993	0.7148	0.10	0.052	.
Senepurp	0.5789	0.8154	0.03	0.549
Sileundu	-0.6610	-0.7504	0.10	0.102
Vellvell	-0.2618	0.9651	0.13	0.035
Zalumari	-0.6224	-0.7827	0.12	0.068	.

DCA 2

NMDS

NMDS species scores

The table shows the direction cosines of each species vector (NMDS1 and NMDS22), the coefficient of determination (r²) indicating the strength of correlation with the ordination, and the permutation-based p-value for significance.
Species	NMDS1	NMDS2	r2	p_value	sig_codes
Anthaeth	0.1671	-0.9859	0.03	0.608
Aspaafri	-0.9887	-0.1496	0.00	0.958
Carpdeli	-0.0670	-0.9978	0.07	0.207
Chaecamp	0.7785	-0.6276	0.06	0.237
Chendiff	-0.9686	0.2487	0.09	0.185
Chirbacc	0.5583	0.8297	0.10	0.132
Colepulc	0.1997	-0.9798	0.51	0.001	***
Colpcomp	-0.9975	-0.0706	0.01	0.825
Crasfili	-0.8582	-0.5133	0.02	0.632
Crasglom	-0.1383	-0.9904	0.01	0.789
Cynaelli	-0.4309	0.9024	0.15	0.022
Ehrherec	-0.8566	-0.5160	0.04	0.451
Elegmicr	0.8617	0.5074	0.06	0.292
Eleolimo	-0.7123	0.7018	0.18	0.021
Euclrace	-0.8325	0.5540	0.12	0.066	.
Feliechi	-0.4600	0.8879	0.10	0.098	.
Felilati	-0.4566	0.8897	0.09	0.141
Ficibulb	-0.2708	0.9626	0.13	0.061	.
Ficicape	0.8170	-0.5766	0.11	0.093	.
Ficiramo	0.4406	-0.8977	0.11	0.086	.
Helicomo	-0.3996	0.9167	0.18	0.018
Helitere	0.9182	-0.3960	0.04	0.470
Indiglau	0.9492	-0.3147	0.03	0.553
Laurtetr	-0.4219	-0.9066	0.20	0.012
Meseaito	0.4773	-0.8787	0.03	0.567
Metamuri	-0.2189	-0.9757	0.08	0.144
Morequer	0.1083	0.9941	0.32	0.001	***
Oleaexas	-0.9160	0.4011	0.64	0.001	***
Osyrcomp	-0.9367	-0.3501	0.04	0.412
Oxaldepr	-0.2708	0.9626	0.13	0.061	.
Oxalpunc	-0.8598	-0.5107	0.05	0.352
Passcory	0.9805	0.1965	0.15	0.029
Passrigi	0.8599	0.5105	0.06	0.305
Phyleric	0.8225	0.5688	0.58	0.001	***
Plecserp	-1.0000	0.0015	0.04	0.466
Rapagill	-0.7059	-0.7083	0.10	0.110
Resteleo	0.8439	0.5365	0.10	0.104
Robsmari	0.0530	-0.9986	0.00	0.957
Searcren	0.2719	-0.9623	0.08	0.143
Searglau	-0.9479	-0.3186	0.02	0.679
Searlaev	0.3825	-0.9239	0.01	0.815
Seneangu	0.6838	0.7297	0.10	0.111
Senepurp	0.6122	-0.7907	0.09	0.123
Sileundu	-0.7123	0.7018	0.18	0.021
Vellvell	0.0486	-0.9988	0.02	0.724
Zalumari	-0.9647	0.2632	0.22	0.001	***

NMDS 2

Species positions vs. significance in ordinations

A common misconception is that the location of a species point in the ordination plot automatically indicates its importance.
This is not the case.

Species scores (the positions in the ordination diagram) show the average composition-weighted location of that species relative to the quadrats. They tell you where a species tends to occur, but not how strongly it drives the overall pattern.
Significance and r² values from envfit quantify how well a species’ distribution aligns with the main gradients captured by the ordination axes.
- A species might plot far from the origin simply because it is tied to a few unusual plots, but that does not mean it explains much of the overall variation.
- Conversely, another species might sit near the origin yet still show a strong, statistically significant fit if its distribution matches the major ecological gradients.

In other words:
- Position = where the species occurs.
- Significance (r², p-value) = whether that species explains the main gradients.

Analogy

Think of an ordination like a lecture hall: two students might be sitting right next to each other. One is paying close attention and contributes insightful questions that shape the discussion (high r², significant). The other is catching up on their social media and not paying attention, present but not really influencing the direction of the lecture (low r², not significant).

The same applies to species in ordination space — proximity alone doesn’t tell you their importance. What matters is whether their pattern of occurrence actually aligns with the major ecological gradients captured by the analysis.

Understanding Species Coordinates

To deepen your understanding of species points in ordination space, examine the plots below.

Each panel shows one species.
The green diamond marks the species’ ordination coordinate (its statistical “summary position”).
Quadrats where the species is present are plotted, with point size and colour scaled to percentage cover.
Grey crosses mark quadrats where the species is absent.

Task:
1. Compare the location of the species coordinate (diamond) to the distribution of its quadrats.
- Does the coordinate lie near the centre of where the species is most abundant?
- Can you find examples where two species have nearby coordinates, but very different abundance patterns?

Go back to the envfit results table for species.
- Look at the r² values (strength of relationship with the ordination axes).
- Look at the p-values (significance of the relationship).
Reflect:
- Do species with high r² and low p show clearer, more interpretable patterns in the ordination plots?
- Do species with low r² or high p look more scattered or less aligned with the ordination axes?

This exercise will help you connect the visualisation of species occurrence with the statistical tests that quantify their importance in shaping ordination patterns.
:::

PCA all species

DCA all species

NMDS all species

Writing Up Your Report

Your final task is to write up the Methods, Results, and a short Discussion for this practical.
Use this document as your data source: you may quote numbers from the summaries, copy plots, or crop figures to support your points. You must generate a hand-drawn ordination, with some grouping of the plots (e.g. by zone), include the environmental variables and some of the important species.

What to Include

Methods: Briefly describe how the vegetation and environmental data were collected, how the quadrat × species matrix was set up, and which ordination methods were used. Keep this short and clear.
Results: Present your ordination outputs. You must primarily use your hand-drawn ordination, but you can additionally use plots provided here (whole figures or cropped sections) and report the quality statistics (i.e., variance explained, axis lengths, stress). Highlight the main ecological patterns you see.
Discussion: Reflect on the strengths and weaknesses of PCA, DCA, and NMDS for this dataset. Which method gives the clearest story of crest → slope → slack? Why?
- Point out the most important environmental variables and how they relate to the vegetation patterns.
- Identify some species that strongly match the gradients and others that do not.
- Comment on whether the ordinations confirm what you observed in the field.

Key Reminder

Numbers and plots are essential, but your write-up should explain their meaning in plain language. Focus on telling the ecological story of the dune system rather than just repeating output.

Raw ordination data

Below are the Eigenvalues for each of the ordinations - these can be used to draw your ordination on graph paper. You can find the environmental variables above.

PCA

##                    PC1           PC2   score    label
## Cynaelli  0.0200322640  0.0122255059 species Cynaelli
## Euclrace  0.2214692439  0.0518628110 species Euclrace
## Helicomo  0.0302083372  0.0163376706 species Helicomo
## Oleaexas  0.8234266769  0.3251035354 species Oleaexas
## Resteleo -0.3058052153  0.1784442829 species Resteleo
## Crasfili  0.0448449167  0.0304677715 species Crasfili
## Metamuri  0.1868898851 -0.5119830179 species Metamuri
## Zalumari  0.0313759066  0.0205310633 species Zalumari
## Felilati  0.0246883213 -0.0094328664 species Felilati
## Ficibulb  0.0052885325  0.0012223475 species Ficibulb
## Morequer -0.0145450018  0.1325952203 species Morequer
## Oxaldepr  0.0052885325  0.0012223475 species Oxaldepr
## Ehrherec  0.0367913009 -0.0140477094 species Ehrherec
## Ficiramo -0.0361232994 -0.0683996254 species Ficiramo
## Plecserp  0.0464475019  0.0034840430 species Plecserp
## Chendiff  0.0219776439  0.0163749982 species Chendiff
## Eleolimo  0.0283551430  0.0210990792 species Eleolimo
## Laurtetr  0.0670510657 -0.0367086204 species Laurtetr
## Sileundu  0.0283551430  0.0210990792 species Sileundu
## Aspaafri  0.0034940787 -0.0085016975 species Aspaafri
## Colepulc -0.2461267023 -0.6283552039 species Colepulc
## Rapagill  0.0437236612 -0.0458659614 species Rapagill
## Searcren -0.0177049324 -0.0729980134 species Searcren
## Colpcomp  0.0135963319 -0.0028614242 species Colpcomp
## Feliechi  0.0264999360  0.0160987312 species Feliechi
## Elegmicr -0.0199625477 -0.0007890677 species Elegmicr
## Ficicape -0.0910279031 -0.0454549070 species Ficicape
## Helitere -0.0581186271 -0.0175583598 species Helitere
## Searglau  0.0282964544  0.0067413183 species Searglau
## Chaecamp -0.0259141522 -0.0204555231 species Chaecamp
## Osyrcomp  0.0197683570 -0.0004744252 species Osyrcomp
## Carpdeli -0.0289373029 -0.0724801241 species Carpdeli
## Robsmari  0.0019102352 -0.0117833939 species Robsmari
## Seneangu -0.0104283446  0.0078214335 species Seneangu
## Senepurp -0.0365269002 -0.0222726705 species Senepurp
## Oxalpunc  0.0095534882  0.0019714997 species Oxalpunc
## Crasglom  0.0006087406 -0.0048211382 species Crasglom
## Searlaev -0.0239687751 -0.0199714610 species Searlaev
## Vellvell -0.0009286608 -0.0094459690 species Vellvell
## Phyleric -0.4804815594  0.3458765138 species Phyleric
## Anthaeth -0.0255676902 -0.0038787775 species Anthaeth
## Meseaito -0.0144654157 -0.0105133927 species Meseaito
## Passcory -0.4839108956  0.2630133164 species Passcory
## Passrigi -0.0249040261  0.0187754158 species Passrigi
## Chirbacc -0.0218476410  0.0270313597 species Chirbacc
## Indiglau -0.0194725855 -0.0015950736 species Indiglau
## G01a      0.4697184910  0.4367145908   sites     G01a
## G01b      0.4035742413  0.3254415506   sites     G01b
## G01c      0.6039896807  0.3414528703   sites     G01c
## G02a      0.4636365686  0.1071611045   sites     G02a
## G02b      0.4525489506  0.3019703050   sites     G02b
## G02c      0.4885183437  0.3639829196   sites     G02c
## G03a      0.2527547145 -0.3479181029   sites     G03a
## G03b      0.3169697693 -0.0667080629   sites     G03b
## G03c      0.5412841236  0.4021300787   sites     G03c
## G04a      0.1195586018 -0.4118369154   sites     G04a
## G04b      0.2712905154 -0.1405739495   sites     G04b
## G04c      0.5354629470  0.4618607459   sites     G04c
## G05a      0.1781429635 -0.4566380845   sites     G05a
## G05b      0.2831660583 -0.2608682622   sites     G05b
## G05c      0.3180218898 -0.0076322781   sites     G05c
## G06a      0.0805690601 -0.4969948090   sites     G06a
## G06b     -0.1642277904 -0.3744798263   sites     G06b
## G06c      0.4334670553  0.0894521593   sites     G06c
## G07a      0.0637860534 -0.5051764155   sites     G07a
## G07b     -0.0662493765 -0.3861568762   sites     G07b
## G07c      0.3082875201 -0.0760172665   sites     G07c
## G08a     -0.2880189636 -0.2776499599   sites     G08a
## G08b     -0.1441289873 -0.4897775845   sites     G08b
## G08c      0.2669151592 -0.0900979455   sites     G08c
## G09a     -0.1086132652 -0.5563491284   sites     G09a
## G09b     -0.1879487403 -0.5057249227   sites     G09b
## G09c     -0.1036270421 -0.3687830817   sites     G09c
## G10a     -0.1735605996 -0.1753958298   sites     G10a
## G10b     -0.1709487010 -0.4109229241   sites     G10b
## G10c     -0.1325335912 -0.1099672789   sites     G10c
## G11a     -0.3826114521  0.4898217174   sites     G11a
## G11b     -0.3512887236 -0.0705603870   sites     G11b
## G11c     -0.1723944397  0.0320600771   sites     G11c
## G12a     -0.3914301805  0.2951034647   sites     G12a
## G12b     -0.4161344658  0.2402391531   sites     G12b
## G12c     -0.1761969454  0.0573237377   sites     G12c
## G13a     -0.4643168980  0.5655487541   sites     G13a
## G13b     -0.4584497120  0.2669218004   sites     G13b
## G13c      0.0003849801  0.2194540063   sites     G13c
## G14a     -0.4506443095  0.5575672169   sites     G14a
## G14b     -0.3940998518  0.0411603840   sites     G14b
## G14c     -0.3951233904 -0.0323660610   sites     G14c
## G15a     -0.4634985824  0.5776327168   sites     G15a
## G15b     -0.5074999753  0.2454738217   sites     G15b
## G15c     -0.2885017037  0.2001227777   sites     G15c

DCA

##                   DCA1        DCA2         DCA3        DCA4   score    label
## G01a     -1.5098872403 -0.27562632  0.201713578  0.23541784   sites     G01a
## G01b     -1.3653063894 -0.01571410 -0.293880299  0.34274015   sites     G01b
## G01c     -1.8395404718 -0.86671055  0.658229769  0.04927905   sites     G01c
## G02a     -1.2531141519  0.37581582  0.569474311  0.53572801   sites     G02a
## G02b     -1.5325795348 -0.03464988 -0.380681706  0.17480409   sites     G02b
## G02c     -1.5872550716 -1.48342783  0.543812156 -0.47697913   sites     G02c
## G03a     -0.6613312715  0.56927088  0.154325974  0.31016624   sites     G03a
## G03b     -1.0050610207  0.31669292  0.019586147  0.24024074   sites     G03b
## G03c     -1.5119357084 -0.62030406  0.721639809  0.16242073   sites     G03c
## G04a     -0.4251184640  0.74065469 -0.096589296  0.23638967   sites     G04a
## G04b     -0.5814135994 -0.38792774 -0.249325532 -0.48139588   sites     G04b
## G04c     -1.7016693228 -0.86065662  0.627718036  0.00580519   sites     G04c
## G05a     -0.7012111914  1.52991788  0.142933203  0.76865928   sites     G05a
## G05b     -0.6589033770 -0.04824716 -0.194567080 -0.18574235   sites     G05b
## G05c     -0.8980735411 -1.02569070 -0.667561235  0.34446841   sites     G05c
## G06a     -0.6142964439  1.48792344  0.146011535  0.70734313   sites     G06a
## G06b      0.2502151643 -0.09623212 -0.206387242  0.08212431   sites     G06b
## G06c     -1.2195857994 -0.97319238  0.543684169 -0.38253903   sites     G06c
## G07a     -0.5345722727  1.58345656 -0.098162272  0.83859859   sites     G07a
## G07b      0.0113586875  0.84270513  0.124298685  0.87871269   sites     G07b
## G07c     -0.6859305695 -0.77678540 -0.139195227 -0.30031786   sites     G07c
## G08a      0.7149681227 -0.08852899 -0.497697192 -0.62110307   sites     G08a
## G08b      0.1700888595  0.39795431  0.003218646 -0.23988895   sites     G08b
## G08c     -0.6203743408 -0.89438144  0.590930807 -0.62825605   sites     G08c
## G09a      0.0486752169  0.46074242 -0.106808333 -0.06132585   sites     G09a
## G09b      0.3954979377 -0.19230714 -0.433720509 -0.70826575   sites     G09b
## G09c      0.0747694549 -1.09761823  0.056617696 -1.22244804   sites     G09c
## G10a      0.9331291227  1.34600308 -0.308775955 -0.62685149   sites     G10a
## G10b      0.4342912231 -0.08253589 -0.662902594 -1.15943048   sites     G10b
## G10c      0.1910561963 -0.73819259  0.113601814 -0.58398111   sites     G10c
## G11a      2.1124387192  0.90341150  0.221680839 -0.10698590   sites     G11a
## G11b      0.6832223785  0.15199827 -0.169573116 -0.22108854   sites     G11b
## G11c      0.5188504891 -0.90202840 -0.838785594  0.46986585   sites     G11c
## G12a      2.0755668174  0.70890502  0.101977533 -0.11647158   sites     G12a
## G12b      0.9660624212  0.22872093 -0.085334594  0.28984904   sites     G12b
## G12c      0.5474768448 -0.40273322  0.187504978 -0.01470568   sites     G12c
## G13a      1.5008042755  0.15805473 -0.075920154  0.74952020   sites     G13a
## G13b      1.2917370816  0.15874096 -0.192399933 -0.06809592   sites     G13b
## G13c      0.4475709285 -0.01837255  1.796679806 -0.08003764   sites     G13c
## G14a      1.5267146103  0.51128482  0.830640381  0.36374343   sites     G14a
## G14b      0.8575842435 -0.25007260 -0.398333083  0.18635361   sites     G14b
## G14c      0.9873953515 -0.41931298 -0.115045479  0.08974350   sites     G14c
## G15a      1.8410580488  0.67985058  0.298749412  0.26457043   sites     G15a
## G15b      1.4792839554 -0.11632154 -0.381523853  0.21913860   sites     G15b
## G15c      1.0466569168 -0.34521000 -0.015073610  0.53793207   sites     G15c
## Cynaelli -2.2206334738  1.12058518  1.243575755  0.66465038 species Cynaelli
## Euclrace -1.4428784146  0.33866226 -0.898321605  0.41577312 species Euclrace
## Helicomo -2.0892784225  1.23207746  1.172982422  0.86319689 species Helicomo
## Oleaexas -1.8679226939 -0.97682186  0.683299856  0.02514799 species Oleaexas
## Resteleo  1.0352225251  0.67897580  1.733905583  0.46857231 species Resteleo
## Crasfili -0.4315420137 -2.01461616 -3.218178792  2.18986980 species Crasfili
## Metamuri -0.7586481173  1.78495733  0.147744502  0.93180917 species Metamuri
## Zalumari -2.5822140805 -0.66448104  0.425695970 -0.50922843 species Zalumari
## Felilati -1.4486581429  1.52935252  0.961423403  1.39933596 species Felilati
## Ficibulb -1.9007110314  1.73426678  1.251359106  1.73340899 species Ficibulb
## Morequer  0.4244687452  0.79400355  0.980117201  1.15623632 species Morequer
## Oxaldepr -1.9007110314  1.73426678  1.251359106  1.73340899 species Oxaldepr
## Ehrherec -1.3194695938  0.20139113 -0.862334623 -1.19959572 species Ehrherec
## Ficiramo  0.5908340412  0.52423663 -1.138034312 -1.89319047 species Ficiramo
## Plecserp -1.7244999684  0.66459683 -0.781916895 -0.36228770 species Plecserp
## Chendiff -2.3752081816 -1.82516136  0.446088972 -1.46078473 species Chendiff
## Eleolimo -2.3779716529 -0.99883415  1.001107415 -0.38347492 species Eleolimo
## Laurtetr -0.7262612937 -2.55398764  0.580933054 -1.56663308 species Laurtetr
## Sileundu -2.3779716529 -0.99883415  1.001107415 -0.38347492 species Sileundu
## Aspaafri -0.8277322864  1.04806546 -0.358621418  0.22342550 species Aspaafri
## Colepulc  0.3593785052 -0.35038418 -0.522236707 -0.85160861 species Colepulc
## Rapagill -0.8640191271 -1.58868353  1.825473027 -1.24350428 species Rapagill
## Searcren  0.0109475559  1.15427276  0.232206329  2.58754765 species Searcren
## Colpcomp -1.5990823303  1.19669995  0.013155188  0.40696686 species Colpcomp
## Feliechi -1.8048911921  0.39700217  0.395231728 -1.21363751 species Feliechi
## Elegmicr  1.2818371664  1.43932239  0.040972446  0.19076698 species Elegmicr
## Ficicape  0.9888626757 -0.41693140 -0.791115088 -1.09411917 species Ficicape
## Helitere  1.2124875232  1.90405790 -0.523596187 -1.66462801 species Helitere
## Searglau -0.0102759804 -1.02694540  2.980366622 -0.72229747 species Searglau
## Chaecamp  0.8020985228  0.74894714 -1.085421367 -1.50467346 species Chaecamp
## Osyrcomp -1.4992269135 -3.69531015 -1.876084449  2.73629585 species Osyrcomp
## Carpdeli  0.2594409132 -1.35136713 -1.877510586  0.87809388 species Carpdeli
## Robsmari -0.8827172343  2.11274206  0.219946884  0.96299880 species Robsmari
## Seneangu  2.9213417270  1.17085703  0.371905018 -0.46657601 species Seneangu
## Senepurp  0.8032883565  0.82925643  0.073812810  2.25780776 species Senepurp
## Oxalpunc -1.9453804141 -1.68676002  1.665249325 -2.38422239 species Oxalpunc
## Crasglom -0.8232219912  2.20901574 -0.953990081  1.72676361 species Crasglom
## Searlaev -0.0009799873  1.91654720 -1.039707196  1.52532964 species Searlaev
## Vellvell -0.5810418293  2.02304861 -1.147738973  1.47853214 species Vellvell
## Phyleric  2.4357263318  0.91270737 -0.131549254 -0.15960345 species Phyleric
## Anthaeth  0.7332731075 -1.16116451  2.343623702 -2.83503790 species Anthaeth
## Meseaito  0.8040899185  0.57714210 -2.134429276 -4.02861245 species Meseaito
## Passcory  1.5224428188 -0.63224621 -0.487210098  1.06696423 species Passcory
## Passrigi  3.2976174781  0.40573421 -0.126848880 -0.58334720 species Passrigi
## Chirbacc  2.4131591859  0.83309359  1.770258142 -0.37634642 species Chirbacc
## Indiglau  1.4495572786 -1.75186403  0.863187476 -0.88312579 species Indiglau
##          weight
## G01a      68.00
## G01b      72.00
## G01c      53.00
## G02a      28.00
## G02b     112.00
## G02c      36.00
## G03a      39.85
## G03b      99.00
## G03c      65.00
## G04a      81.70
## G04b     100.00
## G04c      81.00
## G05a      96.30
## G05b     108.00
## G05c      66.00
## G06a      71.29
## G06b      88.00
## G06c      75.00
## G07a      80.00
## G07b      96.00
## G07c     100.00
## G08a      84.50
## G08b     112.00
## G08c      90.00
## G09a      79.70
## G09b      88.00
## G09c      51.00
## G10a      68.00
## G10b     120.00
## G10c      90.00
## G11a      61.50
## G11b      82.00
## G11c     108.00
## G12a      45.00
## G12b      90.00
## G12c      54.00
## G13a      40.00
## G13b      89.00
## G13c     103.00
## G14a      46.50
## G14b     102.00
## G14c      75.00
## G15a      60.00
## G15b      77.00
## G15c     105.00
## Cynaelli   1.30
## Euclrace 253.50
## Helicomo   3.10
## Oleaexas 552.00
## Resteleo 286.60
## Crasfili  26.00
## Metamuri 408.00
## Zalumari   2.00
## Felilati   2.00
## Ficibulb   0.10
## Morequer  25.00
## Oxaldepr   0.10
## Ehrherec  18.00
## Ficiramo  33.00
## Plecserp  18.00
## Chendiff   2.00
## Eleolimo   2.00
## Laurtetr  72.00
## Sileundu   2.00
## Aspaafri   0.55
## Colepulc 927.00
## Rapagill  29.50
## Searcren  54.80
## Colpcomp   5.00
## Feliechi   4.00
## Elegmicr   6.00
## Ficicape  51.00
## Helitere  43.60
## Searglau  35.50
## Chaecamp  10.00
## Osyrcomp   7.00
## Carpdeli  31.90
## Robsmari   1.10
## Seneangu   1.69
## Senepurp  16.00
## Oxalpunc   1.00
## Crasglom   0.20
## Searlaev  15.10
## Vellvell   0.60
## Phyleric 234.10
## Anthaeth  12.50
## Meseaito   7.50
## Passcory 322.00
## Passrigi   5.00
## Chirbacc   3.00
## Indiglau   5.00

NMDS

##                NMDS1         NMDS2       NMDS3         NMDS4       NMDS5
## G01a     -0.63828306  0.7747399975 -0.10180419  0.5442873937  0.28583237
## G01b     -0.61573603  0.5757122899 -0.07978475  0.4145391403 -0.15290562
## G01c     -1.33073815  0.2585479163  0.37710974 -0.1318644116 -0.42738226
## G02a     -0.44175644  1.0641233374  0.55446599 -0.1095893804  0.51770715
## G02b     -0.66674962  0.2226028267  0.31902630  0.8031664917 -0.21064202
## G02c     -1.14140210  0.1985999448 -0.49545150 -0.7418367537 -0.25697463
## G03a     -0.37641684 -0.0708178703  0.47385147 -0.0088535312  0.58488694
## G03b     -0.39106865 -0.0187602771  0.50662551  0.6750322619  0.17265812
## G03c     -0.60471924  0.9674309424 -0.12131881 -0.4734639428 -0.50449653
## G04a     -0.09496923 -0.0065189585  0.73092553  0.0165951832  0.35442338
## G04b     -0.34562008 -0.3055942790  0.21288820  0.6592906382 -0.23430419
## G04c     -0.64646402  0.6097118134 -0.12807405  0.0437185317 -0.77902078
## G05a     -0.39124711 -0.4921058509  0.43211054 -0.1349302545  0.57922165
## G05b     -0.35593246 -0.3008644736  0.05436558  0.3122016365 -0.07339274
## G05c     -0.73257860 -0.1855507786 -0.51095289  0.2973121430 -0.19934192
## G06a      0.01412605 -0.1804255345  0.82137255 -0.7485876638  0.45371393
## G06b      0.19859418 -0.8893007082 -0.19331303  0.2639532483  0.02512411
## G06c     -0.79018768 -0.3181175087 -0.32130203 -0.1295921287  0.39859680
## G07a     -0.07530398 -0.3653625319  0.90343365 -0.8084464988 -0.41269091
## G07b      0.34152790 -0.3813184947  0.42220983  0.6092183555  0.25244111
## G07c     -0.48619296 -0.4437720923 -0.34376401 -0.1639780121 -0.24070174
## G08a      0.52417278 -0.2312151705 -0.03683277 -0.6798437380 -0.55532765
## G08b      0.19603075 -0.4737865533  0.40615545  0.3859460831 -0.15416414
## G08c     -0.61479138 -0.4002754781 -0.42900377 -0.2499896710  0.34867145
## G09a      0.21533447 -0.4869911774  0.18577674 -0.4615495364  0.08646842
## G09b      0.37201736 -0.6087461672  0.15495020  0.0824671279 -0.33050500
## G09c      0.05561369 -0.9238362078 -0.53479634 -0.5235915133 -0.02398909
## G10a      0.53478139 -0.1712530551  0.56250383 -0.4982220029 -0.40975511
## G10b      0.28567057 -0.4903570357  0.11090121  0.5585231446 -0.68557742
## G10c     -0.01449926 -0.2661787128 -0.50340724 -0.1691090191  0.36003391
## G11a      0.93476542  0.7081834181  0.57953953 -0.1910021374 -0.12446135
## G11b      0.51735471 -0.0564940971  0.16242923  0.5595728449  0.02117919
## G11c     -0.04519143 -0.2349841594 -0.58659324 -0.4160713252 -0.11309984
## G12a      0.85043716  0.3422081052  0.40464371 -0.5497838000  0.15282901
## G12b      0.68364134  0.1336975378 -0.08486704  0.4627610741  0.26982459
## G12c     -0.00511144 -0.0277876397 -0.61234052 -0.0211890936  0.17668082
## G13a      0.80649200  0.7090608146 -0.38037332 -0.2389714549 -0.21081730
## G13b      0.70087663  0.0727552505 -0.02311024  0.3787491773 -0.21926674
## G13c     -0.15869697  0.1406384629 -0.31917642 -0.2713578126  0.88339687
## G14a      0.77981421  0.7854271821 -0.44002611 -0.0330725969  0.31573659
## G14b      0.47481129 -0.0873909018 -0.28673147  0.4135313227 -0.09458331
## G14c      0.62259341 -0.1398965550 -0.73623491  0.0671436956  0.21205895
## G15a      0.80886328  0.7960989551 -0.26563235 -0.0752895701 -0.01252998
## G15b      0.89045870  0.0676724076 -0.17790306  0.1663316325 -0.18667153
## G15c      0.15567951  0.1304910668 -0.66249073  0.1158447226  0.16111645
## Cynaelli -0.75845015  1.2486497790  0.10079367  0.5295112879  0.53614116
## Euclrace -0.41010055  0.1534373371  0.09172838  0.5722814158  0.00869774
## Helicomo -0.52410125  1.0205907485  0.11863092  0.2563984372  0.33511481
## Oleaexas -0.77854752  0.2317847716 -0.10376632  0.0973529748 -0.14243704
## Resteleo  0.32129833  0.1837125203 -0.17972514  0.1128828641  0.16822627
## Crasfili -0.67247859  0.1213896729 -0.57187312  0.0005982109 -0.39671929
## Metamuri -0.15577226 -0.3327564295  0.66401230 -0.1662602630  0.21555876
## Zalumari -1.65605578  0.3566750055  0.21600699 -0.4125526802 -0.59260251
## Felilati -0.44504408  0.4509797858  0.79082336 -0.0518168524  0.80420275
## Ficibulb -0.57091286  1.5623720488  0.79963885 -0.1554817346  0.80106080
## Morequer  0.54546308  1.2107841592 -0.33644095 -0.2809260456 -0.11277098
## Oxaldepr -0.57091286  1.5623720488  0.79963885 -0.1554817346  0.80106080
## Ehrherec -0.39083062 -0.2105548780  0.50318893  0.8949775433 -0.20611857
## Ficiramo  0.23488537 -0.4405953555  0.18321682  0.6016209706 -0.30677380
## Plecserp -0.62673295  0.0007459417  0.51924867  1.0215654784 -0.12010936
## Chendiff -1.47511405  0.2915893222 -0.71452944 -1.0524930862 -0.39762306
## Eleolimo -1.10635003  0.8917496195 -0.42765720 -0.8500544959 -0.60125169
## Laurtetr -0.36002134 -0.6030845175 -0.62635221 -0.2954879639  0.10379500
## Sileundu -1.10635003  0.8917496195 -0.42765720 -0.8500544959 -0.60125169
## Aspaafri -0.22843389 -0.0370047429  0.94638882  0.0130526226  0.65203367
## Colepulc  0.11936174 -0.4277708343 -0.11591508 -0.0068474105 -0.01286420
## Rapagill -0.40694322 -0.3872466834 -0.14916629 -0.3661282318  0.65406229
## Searcren  0.25072420 -0.4271731071  0.22045514  0.4312135404  0.27037474
## Colpcomp -0.50540547 -0.0275443002  0.73064435  0.9577131156  0.26715808
## Feliechi -0.57969768  0.7483869182 -0.11339914 -0.0344615334 -1.03740798
## Elegmicr  0.59206026  0.3107312555  0.89475820 -0.5945499442  0.33039024
## Ficicape  0.53484326 -0.3766111336  0.11045266  0.5089258928 -0.29134176
## Helitere  0.50001232 -0.0539305071  0.38600076 -0.2007530733 -0.54138441
## Searglau -0.45256840 -0.2579604052 -0.39896392 -0.2636926835  0.81327535
## Chaecamp  0.41329005 -0.2886011587  0.16127066  0.7074232576 -0.08906536
## Osyrcomp -0.94676275 -0.2724302154 -0.73688520  0.4218164891 -0.30844658
## Carpdeli  0.15373056 -0.5750964951 -0.06499991 -0.5119373112 -0.24366993
## Robsmari  0.01825609 -0.2649052061  1.18456572 -1.0620710510  0.70204255
## Seneangu  0.83555815  0.6102283492  0.95728587 -0.5198361056 -0.12027404
## Senepurp  0.54664452 -0.4803735023 -0.02597680  0.6181596332  0.21073583
## Oxalpunc -1.02121501 -0.4670679481 -0.46337484 -0.1838609624  0.61675847
## Crasglom -0.09732062 -0.5364342529  1.30291247 -1.1469967568 -0.63856664
## Searlaev  0.48227358  0.0308691391  0.33514108 -0.8345407842 -0.59594374
## Vellvell  0.21560107 -0.4568822690  0.75520799 -1.0733018313 -0.72771002
## Phyleric  0.96126400  0.4847055357  0.04415574 -0.1494317226 -0.16054392
## Anthaeth  0.18887745 -0.3981798169 -0.51127542 -0.3229734397  0.45793598
## Meseaito  0.61880754 -0.1963279044  0.36104906  0.4759869175 -0.80245943
## Passcory  0.59775841  0.1875188264 -0.60644802  0.1136400082  0.06594057
## Passrigi  1.09907963  0.5024383542  0.58356840 -0.7800148019  0.23647602
## Chirbacc  1.00780863  1.1531835009 -0.63459614 -0.0469222904  0.48854687
## Indiglau  0.80462116 -0.2053995619 -1.06178206  0.0952612216  0.32812395
##            score    label
## G01a       sites     G01a
## G01b       sites     G01b
## G01c       sites     G01c
## G02a       sites     G02a
## G02b       sites     G02b
## G02c       sites     G02c
## G03a       sites     G03a
## G03b       sites     G03b
## G03c       sites     G03c
## G04a       sites     G04a
## G04b       sites     G04b
## G04c       sites     G04c
## G05a       sites     G05a
## G05b       sites     G05b
## G05c       sites     G05c
## G06a       sites     G06a
## G06b       sites     G06b
## G06c       sites     G06c
## G07a       sites     G07a
## G07b       sites     G07b
## G07c       sites     G07c
## G08a       sites     G08a
## G08b       sites     G08b
## G08c       sites     G08c
## G09a       sites     G09a
## G09b       sites     G09b
## G09c       sites     G09c
## G10a       sites     G10a
## G10b       sites     G10b
## G10c       sites     G10c
## G11a       sites     G11a
## G11b       sites     G11b
## G11c       sites     G11c
## G12a       sites     G12a
## G12b       sites     G12b
## G12c       sites     G12c
## G13a       sites     G13a
## G13b       sites     G13b
## G13c       sites     G13c
## G14a       sites     G14a
## G14b       sites     G14b
## G14c       sites     G14c
## G15a       sites     G15a
## G15b       sites     G15b
## G15c       sites     G15c
## Cynaelli species Cynaelli
## Euclrace species Euclrace
## Helicomo species Helicomo
## Oleaexas species Oleaexas
## Resteleo species Resteleo
## Crasfili species Crasfili
## Metamuri species Metamuri
## Zalumari species Zalumari
## Felilati species Felilati
## Ficibulb species Ficibulb
## Morequer species Morequer
## Oxaldepr species Oxaldepr
## Ehrherec species Ehrherec
## Ficiramo species Ficiramo
## Plecserp species Plecserp
## Chendiff species Chendiff
## Eleolimo species Eleolimo
## Laurtetr species Laurtetr
## Sileundu species Sileundu
## Aspaafri species Aspaafri
## Colepulc species Colepulc
## Rapagill species Rapagill
## Searcren species Searcren
## Colpcomp species Colpcomp
## Feliechi species Feliechi
## Elegmicr species Elegmicr
## Ficicape species Ficicape
## Helitere species Helitere
## Searglau species Searglau
## Chaecamp species Chaecamp
## Osyrcomp species Osyrcomp
## Carpdeli species Carpdeli
## Robsmari species Robsmari
## Seneangu species Seneangu
## Senepurp species Senepurp
## Oxalpunc species Oxalpunc
## Crasglom species Crasglom
## Searlaev species Searlaev
## Vellvell species Vellvell
## Phyleric species Phyleric
## Anthaeth species Anthaeth
## Meseaito species Meseaito
## Passcory species Passcory
## Passrigi species Passrigi
## Chirbacc species Chirbacc
## Indiglau species Indiglau

Dune transects and ordinations

Prof. Alastair Potts

2025-10-12

Introduction

The Tools

By the end of this practical you should be able to

Generating the ordinations for each method

Interpreting Ordination Axes

PCA

DCA

NMDS

Reporting the Quality of an Ordination

Hints:

What Else Should Be Reported?

Adding Group Structure to Ordinations

PCA

DCA

NMDS

PCA

DCA

NMDS

Environmental Drivers

Height Above the Slacks: A Key Gradient

PCA

DCA

NMDS

Soil pH

DCA

Box-and-whisker plots

Height above slacks vs soil pH

Soil moisture

DCA

Box-and-whisker plots

Ht vs soil moisture

Reading Multiple Environmental Arrows

Indirect comparison

Task

PCA

DCA

NMDS

Exploring plant species in ordinations

PCA 1

PCA Species scores

PCA 2

DCA

DCA species scores

DCA 2

NMDS

NMDS species scores

NMDS 2

Species positions vs. significance in ordinations

Analogy

Understanding Species Coordinates

PCA all species

DCA all species

NMDS all species

Writing Up Your Report

What to Include

Key Reminder

Raw ordination data

PCA

DCA

NMDS