A standard Water and Leith climate diagram for the mean values of precipitation and temperature extracted from the species presence points.
The Walter and Leith diagram assumes that the growing season occurs when rainfall is over 100mm. A more refined method is to extract the values from a bucket model that keeps track of input to the soil profile through precipitation and reductions in soil moisture through evaptranspiration over the course of the year. This can be compared to changes in NDVI at the collection points.
In some cases NDVI will remain fairly constant, even when the balance model shows that soil water constant is lowered for part of the year. Providing SWC is above 50% of maximum levels the vegetation would not experience a great deal of hydric stress.
| Min. | 1st Qu. | Median | Mean | 3rd Qu. | Max. | |
|---|---|---|---|---|---|---|
| x | -112.00 | -103.00 | -97.20 | -98.30 | -95.40 | -83.30 |
| y | 8.95 | 16.40 | 18.30 | 18.60 | 19.70 | 30.70 |
| X | -112.00 | -103.00 | -97.20 | -98.30 | -95.40 | -83.30 |
| Y | 8.94 | 16.40 | 18.30 | 18.60 | 19.70 | 30.70 |
| elev | -4.00 | 83.00 | 379.00 | 644.00 | 1080.00 | 3870.00 |
| anprec | 169.00 | 758.00 | 892.00 | 983.00 | 1120.00 | 3690.00 |
| mtemp | 6.56 | 21.90 | 25.00 | 23.80 | 26.20 | 28.70 |
| Trange | 12.10 | 16.40 | 18.50 | 19.60 | 22.10 | 35.60 |
| saet | 168.00 | 759.00 | 858.00 | 854.00 | 961.00 | 1660.00 |
| msoil | 208.00 | 232.00 | 248.00 | 262.00 | 275.00 | 427.00 |
The following diagrams show kernel densities one two synthestic climate axes (total annual rainfall and mean annual temperature). If their are signs of multimodality this may indicate that the species has not fully explored its climate niche, or that there are disjunct populations with differing characteristics. The method will not show clear results for species with few collection points.
## Warning: weights overwritten by binning
The same analysis can be run to look at spatial clustering. The kernel densities are smoothed, so will only suggest multimodality if the points are very highly clustered.
The significance of any spatial clusters can be checked using the silhouette width method. The width is calculated for values of k between 2 and 5. If any are higher than 0.52 the analysis will produce a diagram showing the clusters.
## [1] "The collections form 4 spatial clusters"
The collections form 4 spatial clusters
If there is evidence that the points fall into at least 2 groups, but fewer than 6 we can look at whether there is significant differences in variability between and within groups in the climatic conditions at the sites using Anosim. This is a sensitive test, as would be MANOVA, so there will often be significant differences. They should only be intepreted as important if R is much larger than 0.3.
##
## Call:
## anosim(dat = dis, grouping = fit$cluster, permutations = 100)
## Dissimilarity: euclidean
##
## ANOSIM statistic R: 0.54
## Significance: 0.0099
##
## Based on 100 permutations
##
## Upper quantiles of permutations (null model):
## 90% 95% 97.5% 99%
## 0.00853 0.01776 0.01896 0.02368
##
## Dissimilarity ranks between and within classes:
## 0% 25% 50% 75% 100% N
## Between 8342 127096 208986 281586 348194 240693
## 1 463 36670 77632 160304 347057 43071
## 2 463 77748 158894 229081 327418 4278
## 3 463 21851 78224 169730 338432 52650
## 4 463 29454 147368 215910 345656 7503
## Analysis of Variance Table
##
## Response: vars$mtemp
## Df Sum Sq Mean Sq F value Pr(>F)
## as.factor(fit$cluster) 3 543 181.0 15.9 4.6e-10 ***
## Residuals 831 9454 11.4
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
The collections form 4 spatial clusters
For comparison we can fit a model using mean temperature, temperature range and annual precipitation.
Now fit a gam using temperature range, mean temperature and the annual soil moisture dynamic as input.
