2. patch analysis
Johannes De Groeve
1/27/2021
packages
library(reshape)
library(ggplot2)
library(dplyr)
library(yarrr)
source('./fncs/21_patch_statistics.R')data
Dataset including the patch size for every location of roe and red deer location dataset.
- aniyr: unique identifier for animal-year
- study_areas_id: unique identifier study area as defined in the eurodeer database
- sex: biological sex of the animal
- animals_id: unique identifier of animal as defined in the eurodeer database
- acquisition_time: UTC date and time corresponding to a location
- x: x-coordinate of the location using Spatial Reference Identifier SRID 3035
- y: y-coordinate of the location using Spatial Reference Identifier SRID 3035
- forest_density: forest density percentage of the location as defined by the Tree Cover Density Dataset (TCD)
- corine_land_cover_2012_vector_code: land cover classification code of the location as defined by Corine Land Cover - Vector (CLC).
- timex: time in utc
- datex: date
- yearx: year
- mid_or_noon: midnight or noon
- doyx: day of the year
- min: minimum doy in dataset
- max: maximum doy in dataset
- diff_doy: total number of days as defined by the interval (minimum doy to maximum doy)
- timex: time in utc
- prop: overall proportion of locations in the forest as defined by TCD
- prop_night: proportion of locations in the forest during the night as defined by TCD
- prop_day: proportion of locations in the forest during the day as defined by TCD
- count: overall number of locations on which calculations are based
- count_night: number of locations during the night on which calculations are based
- count_day: number of locations during the day on which calculations are based
- TCD_forest_patch_size: patch size in squared meter of forest patch of a location as defined by TCD
- CLC_forest_patch_size: patch size in squared meter of forest patch of a location as defined by CLC
- TCD_open_patch_size: patch size in squared meter of open patch of a location as defined by TCD
- CLC_open_patch_size: patch size in squared meter of open patch of a location as defined by CLC
ce <- read.csv2('../data/2_patchsize/red.csv',dec = ',')
ca <- read.csv2('../data/2_patchsize/roe.csv',dec = ',')
head(ce)## X aniyr study_areas_id sex animals_id acquisition_time
## 1 1 113_2011 3 m 113 2011-05-01 01:00:41+00
## 2 2 113_2011 3 m 113 2011-05-01 12:00:53+00
## 3 3 113_2011 3 m 113 2011-05-02 00:00:24+00
## 4 4 113_2011 3 m 113 2011-05-02 12:00:47+00
## 5 5 113_2011 3 m 113 2011-05-02 23:00:42+00
## 6 6 113_2011 3 m 113 2011-05-03 11:02:01+00
## forest_density corine_land_cover_2012_vector_code timex datex yearx
## 1 0 231 01:00:41 2011-05-01 2011
## 2 97 312 12:00:53 2011-05-01 2011
## 3 93 231 00:00:24 2011-05-02 2011
## 4 98 312 12:00:47 2011-05-02 2011
## 5 0 211 23:00:42 2011-05-03 2011
## 6 98 312 11:02:01 2011-05-03 2011
## mid_or_noon doyx min max diff_doy prop count cnt_night prop_night
## 1 midnight 121 121 304 183 0.9076087 334 168 0.9130435
## 2 noon 121 121 304 183 0.9076087 334 168 0.9130435
## 3 midnight 122 121 304 183 0.9076087 334 168 0.9130435
## 4 noon 122 121 304 183 0.9076087 334 168 0.9130435
## 5 midnight 122 121 304 183 0.9076087 334 168 0.9130435
## 6 noon 123 121 304 183 0.9076087 334 168 0.9130435
## cnt_day prop_day TCD_forest_patch_size CLC_forest_patch_size
## 1 166 0.9021739 NA NA
## 2 166 0.9021739 13147200 6053200
## 3 166 0.9021739 13147200 NA
## 4 166 0.9021739 13147200 6053200
## 5 166 0.9021739 NA NA
## 6 166 0.9021739 13147200 13534000
## TCD_open_patch_size CLC_open_patch_size x y optional
## 1 632800 142250800 4341133 2596948 TRUE
## 2 NA NA 4341369 2597390 TRUE
## 3 NA 142250800 4341057 2596897 TRUE
## 4 NA NA 4340694 2596719 TRUE
## 5 526800 142250800 4341963 2597197 TRUE
## 6 NA NA 4342160 2597473 TRUEhead(ca)## X aniyr study_areas_id sex animals_id acquisition_time
## 1 103311 769_2005 1 m 769 2005-05-01 00:02:54+00
## 2 103312 769_2005 1 m 769 2005-05-01 12:01:59+00
## 3 103313 769_2005 1 m 769 2005-05-02 00:03:00+00
## 4 103314 769_2005 1 m 769 2005-05-07 12:03:05+00
## 5 103315 769_2005 1 m 769 2005-05-08 00:02:12+00
## 6 103316 769_2005 1 m 769 2005-05-08 12:02:22+00
## forest_density corine_land_cover_2012_vector_code timex datex yearx
## 1 99 313 00:02:54 2005-05-01 2005
## 2 86 311 12:01:59 2005-05-01 2005
## 3 97 311 00:03:00 2005-05-02 2005
## 4 89 311 12:03:05 2005-05-07 2005
## 5 94 311 00:02:12 2005-05-08 2005
## 6 98 311 12:02:22 2005-05-08 2005
## mid_or_noon doyx min max diff_doy prop count cnt_night prop_night
## 1 midnight 121 121 304 183 0.6467391 238 122 0.6630435
## 2 noon 121 121 304 183 0.6467391 238 122 0.6630435
## 3 midnight 122 121 304 183 0.6467391 238 122 0.6630435
## 4 noon 127 121 304 183 0.6467391 238 122 0.6630435
## 5 midnight 128 121 304 183 0.6467391 238 122 0.6630435
## 6 noon 128 121 304 183 0.6467391 238 122 0.6630435
## cnt_day prop_day TCD_forest_patch_size CLC_forest_patch_size
## 1 116 0.6304348 NA NA
## 2 116 0.6304348 999600 905600
## 3 116 0.6304348 999600 905600
## 4 116 0.6304348 999600 905600
## 5 116 0.6304348 999600 905600
## 6 116 0.6304348 999600 905600
## TCD_open_patch_size CLC_open_patch_size x y optional
## 1 NA NA 4403227 2557846 TRUE
## 2 NA NA 4403290 2558521 TRUE
## 3 NA NA 4403244 2558778 TRUE
## 4 NA NA 4403501 2558511 TRUE
## 5 NA NA 4403286 2558574 TRUE
## 6 NA NA 4403468 2558505 TRUEApply patch_stat function
# CALCULATE NUMBER OF PATCHES AND NUMBER OF FIXES IN A PATCH SMALLER THAN 25 HECTARE
patch_ce_l <- patch_statistics(ce, patchsize = 250000)
patch_ca_l <- patch_statistics(ca, patchsize = 250000)nr of small patches
First, we calculated the number of patches per individual. Then we sum up all the patches over all populations per species.
# NR OF PATCHES
patch_nr_ce <- patch_ce_l[[1]]
patch_nr_ce$species <- 'red'
patch_nr_ca <- patch_ca_l[[1]]
patch_nr_ca$species <- 'roe'
patch_nr <- rbind(patch_nr_ce,patch_nr_ca)
patch_nr <- melt(patch_nr, id=c("species","animals_id"))
# STATISTICS ABOUT NR OF PATCHES PER INDIVIDUAL OVER ALL POPULATIONS PER SPECIES
(r <- group_by(patch_nr, species, variable) %>%
summarise(sum = sum(value),
mean= mean(value),
median=median(value),
max=max(value),
min=min(value)))## # A tibble: 8 × 7
## # Groups: species [2]
## species variable sum mean median max min
## <chr> <fct> <int> <dbl> <int> <int> <int>
## 1 red TCD_F 406 4.78 3 18 2
## 2 red CLC_F 208 2.45 2 6 2
## 3 red TCD_O 707 8.32 8 20 2
## 4 red CLC_O 269 3.16 3 7 2
## 5 roe TCD_F 490 4.67 3 27 1
## 6 roe CLC_F 223 2.12 2 4 1
## 7 roe TCD_O 340 3.24 2 9 1
## 8 roe CLC_O 231 2.2 2 6 1barplot(r$sum,
cex.names=0.6,
names.arg=c(paste0(r$species, ' ' , r$variable)),
main='total number of patches over all population')
nr of fixes in patches < 25 hectares
First, we calculated the proportion of fixes that is in a patch smaller than 25 hectare, for each individual. Then, we calculate the mean of that proportion over all populations (and thus all individuals)
patch_fx_ce <- patch_ce_l[[2]]
patch_fx_ce$species <- 'red'
patch_fx_ca <- patch_ca_l[[2]]
patch_fx_ca$species <- 'roe'
patch_fx <- rbind(patch_fx_ce,patch_fx_ca)
(r <- group_by(patch_fx, species, variable) %>%
summarise(mean= mean(Freq),
median=median(Freq),
max=max(Freq),
min=min(Freq)))## # A tibble: 8 × 6
## # Groups: species [2]
## species variable mean median max min
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 red CLC_F 0.00663 0 0.543 0
## 2 red CLC_O 0.00122 0 0.0556 0
## 3 red TCD_F 0.0451 0.00549 0.779 0
## 4 red TCD_O 0.432 0.238 1 0
## 5 roe CLC_F 0.103 0 1 0
## 6 roe CLC_O 0.0528 0 1 0
## 7 roe TCD_F 0.389 0.0599 1 0
## 8 roe TCD_O 0.201 0 1 0barplot(r$mean,
cex.names=0.6,
names.arg=c(paste0(r$species, ' ', r$variable)),
main='mean proportion of fixes in small patches (<25ha) per individual over all populations')
#boxplot(Freq ~ species + variable, data=patch_fx, cex.axis=0.6, main='proportion of fixes in small patches (<25 ha) per individual')
pirateplot(Freq ~ variable + species, data=patch_fx, cex.axis=0.6, main='proportion of fixes in small patches (<25 ha) per individual')