Overview

Let’s have a look at the Protea Atlas distribution on the Cape Peninsula and within the March 2015 Silvermine fire scar.


## OGR data source with driver: ESRI Shapefile 
## Source: "/Users/jasper/Documents/GIS/CapePeninsula/TMNP/1962-2016 v1.1/TMNP_fires_1962_2016.shp", layer: "TMNP_fires_1962_2016"
## with 694 features
## It has 7 fields


There are 1605 M. fimbriifolius records across 5568 Protea Atlas Plots on the Peninsula, and 420 and 1147 within the March 2015 fire scar.



Environmental correlates?

Let’s look at the spatial data and see what we get if we fit GLMs (logistic regression) at the two scales?


## 
## Iterations = 5001:24991
## Thinning interval = 10 
## Number of chains = 1 
## Sample size per chain = 2000 
## 
## 1. Empirical mean and standard deviation for each variable,
##    plus standard error of the mean:
## 
##                 Mean      SD  Naive SE Time-series SE
## (Intercept) -0.91243 0.03735 0.0008352       0.001386
## janmax      -0.38079 0.18045 0.0040349       0.006472
## julymin      0.30807 0.19940 0.0044587       0.007438
## dem         -0.17444 0.27378 0.0061219       0.010490
## slope       -0.03903 0.21726 0.0048582       0.007644
## TPI          0.05378 0.04918 0.0010998       0.001719
## TRI         -0.46966 0.21320 0.0047672       0.007462
## 
## 2. Quantiles for each variable:
## 
##                 2.5%      25%      50%       75%    97.5%
## (Intercept) -0.98288 -0.93800 -0.91162 -0.886673 -0.84091
## janmax      -0.73203 -0.50357 -0.38036 -0.254118 -0.02827
## julymin     -0.07482  0.17162  0.30378  0.443467  0.70695
## dem         -0.71949 -0.35604 -0.17322  0.008326  0.36502
## slope       -0.45324 -0.19265 -0.04707  0.107651  0.41059
## TPI         -0.04344  0.02259  0.05356  0.086510  0.14735
## TRI         -0.89086 -0.61065 -0.46220 -0.319277 -0.06451


Where the posterior density shows little overlap of 0 indicates that the variable is important. So there’s a significant negative effect of both mean maximum January temperature and the terrain ruggedness index (TRI - the mean of the absolute differences between the value of a cell and the value of its 8 surrounding cells), and a weak positive effect of mean minimum July temperature at the scale of the whole Peninsula. Not too hot, not too cold, not too rugged - just right :)

TPI (Topographic Position Index - which is not significant here) is the difference between the value of a cell and the mean value of its 8 surrounding cells and dem is the digital elevation model (elevation).


## 
## Iterations = 5001:24991
## Thinning interval = 10 
## Number of chains = 1 
## Sample size per chain = 2000 
## 
## 1. Empirical mean and standard deviation for each variable,
##    plus standard error of the mean:
## 
##                 Mean      SD Naive SE Time-series SE
## (Intercept) -0.57071 0.06271 0.001402       0.002242
## janmax      -0.17147 0.24427 0.005462       0.008338
## julymin     -0.09178 0.33159 0.007415       0.011731
## dem         -0.52413 0.34237 0.007656       0.012229
## slope       -0.38391 0.37381 0.008359       0.013412
## TPI          0.01885 0.06725 0.001504       0.002147
## TRI          0.23983 0.33425 0.007474       0.012192
## 
## 2. Quantiles for each variable:
## 
##                2.5%       25%      50%      75%   97.5%
## (Intercept) -0.6930 -0.611532 -0.57191 -0.52876 -0.4480
## janmax      -0.6450 -0.337445 -0.16652 -0.01071  0.3062
## julymin     -0.7598 -0.303750 -0.09974  0.13211  0.5738
## dem         -1.2183 -0.754440 -0.52804 -0.28740  0.1508
## slope       -1.1180 -0.635267 -0.38560 -0.13488  0.3250
## TPI         -0.1138 -0.026503  0.01792  0.06314  0.1535
## TRI         -0.3882  0.007683  0.23781  0.47741  0.9126


At the scale of the 2015 Silvermine fire there is only a very weak negative effect of mean max Jan temperature and slope (niether would be considered significant)…