Meliosma_idiopoda

Seasonal patterns

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.

Comparison

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.

Summary of the variable values at the collection points.

Niche space with relation to annual precipitation and mean annual temperature.

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.

Spatial clustering

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.

Finding spatial clusters

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

Anosim

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: 0.408 
##       Significance: 0.0099 
## 
## Based on  100  permutations
## 
## Upper quantiles of permutations (null model):
##    90%    95%  97.5%    99% 
## 0.0186 0.0220 0.0244 0.0281 
## 
## Dissimilarity ranks between and within classes:
##          0%  25%   50%   75%  100%     N
## Between 400 5371  8768 12218 15400 10492
## 1       124 1227  3116  7094 15380  2145
## 2       124 8662 10966 13418 13468     6
## 3       124 3494  9402 12640 15389  1431
## 4       124 1265  3331  6378 13978  1326

## Analysis of Variance Table
## 
## Response: vars$mtemp
##                         Df Sum Sq Mean Sq F value  Pr(>F)    
## as.factor(fit$cluster)   3    188    62.8    12.8 1.4e-07 ***
## Residuals              172    844     4.9                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

The collections form 4 spatial clusters

Gam model using simple environmental variables

For comparison we can fit a model using mean temperature, temperature range and annual precipitation.

Bucket model GAM.

Now fit a gam using temperature range, mean temperature and the annual soil moisture dynamic as input.

## Error: A term has fewer unique covariate combinations than specified
## maximum degrees of freedom

Random Forest