Intro

(without broader/theoretical context…)

Mimetes fimbriifolius is a large charismatic shrub endemic to the Cape Peninsula. The species has thick protective bark and large individuals generally survive fire events, while seeds are dispersed by ants and persist in soil-stored seedbanks. Many individuals that survived the March 2015 Silvermine fire have since died and we would like to know if this mortality is related to microclimate as determined by topography (e.g. aspect, slope, elevation). We have detailed 30m gridded data layers for the area for topography, vegetation (from pre-fire satellite NDVI), and microclimate, based on spatial interpolation from 5 years of data from 100 stations. We propose sampling plots that span the range of topographic and microclimatic variation within Silvermine, recording the presence of M. fimbriifolius and scoring individuals as “Alive”, “Killed in the fire or died shortly after” (as evidenced by charring or brittle grey leaves), or “Died since the fire” (as evidenced by tan, flexible leaves).

Our questions (and proposed analyses):

  1. What is the species’ preferred habitat? - GLM with binomial distribution (i.e. logistic regression) of presence/absence data by plot/grid cell with GIS layers as predictors (topographic etc vars) - GLM with poisson regression of count data by plot/grid cell with GIS layers as predictors (topographic etc vars)

  2. Can we predict fire-induced mortality? - GLMM with binomial distribution for the survival/mortality of individuals (died post fire lumped into “survived”), with plot as a random effect, and with GIS layers and individual level variables (e.g. stem diameter, distance to nearest conspecific) as fixed effects

  3. Can we predict post-fire mortality and is it higher in “hotter/drier” topoclimates? - GLMM with binomial distribution for the survival/mortality of individuals (died in fire excluded), with plot as a random effect, and with GIS layers and individual level variables (e.g. stem diameter, distance to nearest conspecific) as fixed effects

  4. Is post-fire mortality within the 2015 burn scar higher than in unburnt areas? - This requires sampling unburnt areas. Data can be included in above analyses with burnt/unburnt as an additional factor in the model

  5. Are the constraints on the species’ occurrence the same as those that determine mortality during or after the fire? - Compare the outcomes of the models

We will need test the accuracy of using leaf conditions as an indicator of our mortality classes using long term data from Mimetes Valley. There are many simple ways of testing this.


Overview

First, let’s have a look at extent of the March 2015 Silvermine fire scar and the underlying variation in our spatial data within the scar so that we can stratify sampling.


## 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


Now let’s see how the covariates relate to each other.



Looking at the correlations it’s probably worth dropping elevation, TRI, January radiation and NDVI from 2013 before sampling sites.



What coverage of the covariates does that give us?


## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.