In previous year’s this section of the assignment has been based on fieldwork conducted at the RPB reserve at Arne. This year we are unfortunately unable to visit the field site. This reduces direct engagagement with t the data and cuts back some of the intended learning objectives.
The instructions are therefore simlpified.
You should write up this section in the style of a simple mamagement report. Screenshots of web maps can be used to illustrate your findings.
Lowland heath in the Soth of England is generally regarded as an anthropenic habitat type. Lowland heathland occurs in area with poor fertility of its soils which are unsuited to most forms of agriculture with the exception of rough grazing. However heathlland has a long history of human management through cutting, burning and rough grazing. Without mamanegemtnatural succession results in heathland eventually developing into birch or pine woodland. There are some potentially competing theories regarding how succession may occur.
In order to produce data that may be used to provide evidence to evaluate the relative contributions of these two potential processes students were asked to design a simple study using circular quadrats and hand held GPS. The numbers of young pines found within each of the circular quadrats were scred as the resonse variable. Inuts to the analsis were then derived from satial data using GIS techniques.
This week we will start looking at the data using simple web maps.
library(aqm)
library(giscourse)
library(raster)
library(ggplot2)
library(sf)
library(tmap)
library(mapview)
library(dplyr)
library(mgcv)
data("arne_pines")
dt(arne_pines)
write.csv(arne_pines, file="Arne_pines.csv", row.names = FALSE)
quadrats<-st_as_sf(arne_pines,coords = c("lon","lat"),crs=4326)
write_sf(quadrats, "qudrats.kml")
## Warning in CPL_write_ogr(obj, dsn, layer, driver,
## as.character(dataset_options), : GDAL Error 4: ERROR parsing kml
## qudrats.kml :no element found on line 2 at offset 0
mapview(quadrats, zcol="Year", burst=TRUE) -> map1
map1 %>% extras()
## Loading required package: leaflet.extras
## Loading required package: leaflet
You should spend some time investigating this map. Go full screen and change some of the options. You can experiment with the measuring tool. For the purposes of this assignment if you want to use this or any leaflet web map as a static figure in a word document you may take a screen shot of the map. There are other more formal ways of making static maps directly in R but these take time and some nowledge of R to get right. A screenshot is good enough for the asssignment.
The restoration site was only measured this year. If we only want to use this year’s data then we will need to apply a filter.
arne_pines %>% filter(Year==2019) -> d
If you look in the Environnment panel in RStudio you will now see an object with only 35 observations. These are the data that you collected.
This is a site specific question. The analysis may be relevant tothe assignment. Finding an answer to this question does not directly answer any broader scientific questions regarding the processes taking place. However it may still be useful for management at Arne and there may be broader implications that are suggested by the answer. If the conversion of former pine forest to heathland is difficult as a result of rapid regeneration this has broader implictaions.
So how do we address this question statistically?
The first step is always to visualise the data. Let’s use ggplots.
See the chapter on making figures.
g0<-ggplot(d,aes(x=site,y=pine_density))
g0 +geom_boxplot()
The quick way to make this is to use the ci function that has been included in the aqm package. Just use the ci funcion on the base plot with the aesthetics set (g0). This adds 95% confidence intervals based on the assumption that the variability around the means follows an approximately gaussian (normal) distribution.
g1<-ci(g0)
g1
You can add labels and customize this plot in other ways. See the handout on ggplots. You should label the axes and caption the figure with an explanation of how confidence intervals have been calculated.
t.test(d$pine_density~d$site)
##
## Welch Two Sample t-test
##
## data: d$pine_density by d$site
## t = -2.6065, df = 15.122, p-value = 0.01974
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1.6627076 -0.1672924
## sample estimates:
## mean in group Heath mean in group Restoration
## 0.5911429 1.5061429