vignettes/PDIAnalysis.md
In umd-lib/animalmove: Analysis of animal population-level movement patterns
Population Dispersion Index
Load package library
library(lubridate)
library(animalmove)
library(plyr)
library(ggplot2)
Subsample Data
Buffalo dataset has been saved in the package data directory , and loaded on the package load.
Raw buffalo dataset contains unaltered original data.
data(buffalo)
head(buffalo)
## event.id visible timestamp location.long location.lat
## 1 10210419 true 2005-02-17 05:05:00.000 31.77 -24.54
## 2 10210423 true 2005-02-17 05:05:00.000 31.77 -24.54
## 3 10210428 true 2005-02-17 06:08:00.000 31.76 -24.54
## 4 10210434 true 2005-02-17 06:08:00.000 31.76 -24.54
## 5 10210456 true 2005-02-17 07:05:00.000 31.76 -24.55
## 6 10210458 true 2005-02-17 08:05:00.000 31.76 -24.55
## behavioural.classification comments manually.marked.outlier sensor.type
## 1 0 24.3 NA gps
## 2 0 24.3 NA gps
## 3 0 29.5 NA gps
## 4 0 29.5 NA gps
## 5 0 35.8 NA gps
## 6 0 37.3 NA gps
## individual.taxon.canonical.name tag.local.identifier
## 1 Syncerus caffer #1764820
## 2 Syncerus caffer #1764820
## 3 Syncerus caffer #1764820
## 4 Syncerus caffer #1764820
## 5 Syncerus caffer #1764820
## 6 Syncerus caffer #1764820
## individual.local.identifier
## 1 Queen
## 2 Queen
## 3 Queen
## 4 Queen
## 5 Queen
## 6 Queen
## study.name utm.easting
## 1 Kruger African Buffalo, GPS tracking, South Africa 375051
## 2 Kruger African Buffalo, GPS tracking, South Africa 375051
## 3 Kruger African Buffalo, GPS tracking, South Africa 374851
## 4 Kruger African Buffalo, GPS tracking, South Africa 374851
## 5 Kruger African Buffalo, GPS tracking, South Africa 374527
## 6 Kruger African Buffalo, GPS tracking, South Africa 374492
## utm.northing utm.zone study.timezone study.local.timestamp
## 1 7285726 36S South Africa Standard Time 2005-02-17 07:05:00.000
## 2 7285726 36S South Africa Standard Time 2005-02-17 07:05:00.000
## 3 7285502 36S South Africa Standard Time 2005-02-17 08:08:00.000
## 4 7285502 36S South Africa Standard Time 2005-02-17 08:08:00.000
## 5 7284538 36S South Africa Standard Time 2005-02-17 09:05:00.000
## 6 7284644 36S South Africa Standard Time 2005-02-17 10:05:00.000
nrow(buffalo)
## [1] 28410
currentnames <- colnames(buffalo)
currentnames
## [1] "event.id" "visible"
## [3] "timestamp" "location.long"
## [5] "location.lat" "behavioural.classification"
## [7] "comments" "manually.marked.outlier"
## [9] "sensor.type" "individual.taxon.canonical.name"
## [11] "tag.local.identifier" "individual.local.identifier"
## [13] "study.name" "utm.easting"
## [15] "utm.northing" "utm.zone"
## [17] "study.timezone" "study.local.timestamp"
names(buffalo)[names(buffalo) == "timestamp"] <- "time"
names(buffalo)[names(buffalo) == "utm.easting"] <- "x"
names(buffalo)[names(buffalo) == "utm.northing"] <- "y"
names(buffalo)[names(buffalo) == "tag.local.identifier"] <- "id"
names(buffalo)[names(buffalo) == "individual.taxon.canonical.name"] <- "pop.type"
# Data Conversion
buffalo$time <- as.POSIXct(strptime(buffalo$time, format = "%Y-%m-%d %H:%M",
tz = "GMT"))
# Display new names
newnames <- colnames(buffalo)
newnames
## [1] "event.id" "visible"
## [3] "time" "location.long"
## [5] "location.lat" "behavioural.classification"
## [7] "comments" "manually.marked.outlier"
## [9] "sensor.type" "pop.type"
## [11] "id" "individual.local.identifier"
## [13] "study.name" "x"
## [15] "y" "utm.zone"
## [17] "study.timezone" "study.local.timestamp"
head(buffalo)
## event.id visible time location.long location.lat
## 1 10210419 true 2005-02-17 05:05:00 31.77 -24.54
## 2 10210423 true 2005-02-17 05:05:00 31.77 -24.54
## 3 10210428 true 2005-02-17 06:08:00 31.76 -24.54
## 4 10210434 true 2005-02-17 06:08:00 31.76 -24.54
## 5 10210456 true 2005-02-17 07:05:00 31.76 -24.55
## 6 10210458 true 2005-02-17 08:05:00 31.76 -24.55
## behavioural.classification comments manually.marked.outlier sensor.type
## 1 0 24.3 NA gps
## 2 0 24.3 NA gps
## 3 0 29.5 NA gps
## 4 0 29.5 NA gps
## 5 0 35.8 NA gps
## 6 0 37.3 NA gps
## pop.type id individual.local.identifier
## 1 Syncerus caffer #1764820 Queen
## 2 Syncerus caffer #1764820 Queen
## 3 Syncerus caffer #1764820 Queen
## 4 Syncerus caffer #1764820 Queen
## 5 Syncerus caffer #1764820 Queen
## 6 Syncerus caffer #1764820 Queen
## study.name x y
## 1 Kruger African Buffalo, GPS tracking, South Africa 375051 7285726
## 2 Kruger African Buffalo, GPS tracking, South Africa 375051 7285726
## 3 Kruger African Buffalo, GPS tracking, South Africa 374851 7285502
## 4 Kruger African Buffalo, GPS tracking, South Africa 374851 7285502
## 5 Kruger African Buffalo, GPS tracking, South Africa 374527 7284538
## 6 Kruger African Buffalo, GPS tracking, South Africa 374492 7284644
## utm.zone study.timezone study.local.timestamp
## 1 36S South Africa Standard Time 2005-02-17 07:05:00.000
## 2 36S South Africa Standard Time 2005-02-17 07:05:00.000
## 3 36S South Africa Standard Time 2005-02-17 08:08:00.000
## 4 36S South Africa Standard Time 2005-02-17 08:08:00.000
## 5 36S South Africa Standard Time 2005-02-17 09:05:00.000
## 6 36S South Africa Standard Time 2005-02-17 10:05:00.000
Number of rows in the buffalo data & data set structure
length(table((buffalo$id)))
## [1] 6
str(buffalo)
## 'data.frame': 28410 obs. of 18 variables:
## $ event.id : int 10210419 10210423 10210428 10210434 10210456 10210458 10210483 10210535 10210544 10210558 ...
## $ visible : chr "true" "true" "true" "true" ...
## $ time : POSIXct, format: "2005-02-17 05:05:00" "2005-02-17 05:05:00" ...
## $ location.long : num 31.8 31.8 31.8 31.8 31.8 ...
## $ location.lat : num -24.5 -24.5 -24.5 -24.5 -24.5 ...
## $ behavioural.classification : int 0 0 0 0 0 0 0 0 0 0 ...
## $ comments : num 24.3 24.3 29.5 29.5 35.8 37.3 38.9 39.6 40 38.1 ...
## $ manually.marked.outlier : logi NA NA NA NA NA NA ...
## $ sensor.type : chr "gps" "gps" "gps" "gps" ...
## $ pop.type : chr "Syncerus caffer" "Syncerus caffer" "Syncerus caffer" "Syncerus caffer" ...
## $ id : chr "#1764820" "#1764820" "#1764820" "#1764820" ...
## $ individual.local.identifier: chr "Queen" "Queen" "Queen" "Queen" ...
## $ study.name : chr "Kruger African Buffalo, GPS tracking, South Africa" "Kruger African Buffalo, GPS tracking, South Africa" "Kruger African Buffalo, GPS tracking, South Africa" "Kruger African Buffalo, GPS tracking, South Africa" ...
## $ x : num 375051 375051 374851 374851 374527 ...
## $ y : num 7285726 7285726 7285502 7285502 7284538 ...
## $ utm.zone : chr "36S" "36S" "36S" "36S" ...
## $ study.timezone : chr "South Africa Standard Time" "South Africa Standard Time" "South Africa Standard Time" "South Africa Standard Time" ...
## $ study.local.timestamp : chr "2005-02-17 07:05:00.000" "2005-02-17 07:05:00.000" "2005-02-17 08:08:00.000" "2005-02-17 08:08:00.000" ...
Subsample data within time interval
We select at most 6 individuals within 2009, time interval 50 hours, and accuracy 50 hours, and subsampling scheme for Realized Mobility Index
pdi.subsample.data <- subsample(dat = buffalo, start = c("2005-02-17 00:00:00"),
end = "2006-12-31 00:00:00", interval = c("50 hours"), accuracy = c("3 hours"),
minIndiv = 3, maxIndiv = 6, mustIndiv = NULL, index.type = "pdi")
## [1] "50 hours"
## [1] "3 hours"
## [1] "2005-02-17 00:00:00"
## 'data.frame': 8 obs. of 9 variables:
## $ numberOfIndividuals: int 3 4 5 6 5 5 4 3
## $ pairsOfCompleteSets: int 32 19 8 7 9 18 37 49
## $ completeSets : int 34 21 10 9 10 20 39 52
## $ scanInterval : chr "50 hours" "50 hours" "50 hours" "50 hours" ...
## $ scanAccuracy : chr "3 hours" "3 hours" "3 hours" "3 hours" ...
## $ firstScantime : chr "2005-02-17 00:00:00" "2005-02-17 00:00:00" "2005-02-17 00:00:00" "2005-02-17 00:00:00" ...
## $ firstOverlap : chr "2005-07-27 10:00" "2005-08-23 12:00" "2005-09-15 10:00" "2005-09-17 12:00" ...
## $ lastOverlap : chr "2005-10-06 06:00" "2005-10-06 06:00" "2005-10-06 06:00" "2005-10-06 06:00" ...
## $ lastScantime : chr "2006-12-31 00:00:00" "2006-12-31 00:00:00" "2006-12-31 00:00:00" "2006-12-31 00:00:00" ...
buffalo.indiv <- Individuals(pdi.subsample.data, id = "id", time = "time", x = "x",
y = "y", group.by = "pop.type", proj4string = CRS("+proj=utm +zone=28 +datum=WGS84"))
Analysis
Display bounding box and scale for Specie1
bbox.coord1 <- bbox.coordinates(buffalo.indiv, percent = 100, unin = "m", unout = "km2")
bbox.coord1
## min max
## x 369957 394639
## y 7227424 7319263
specie1.scale <- bbox.scale(buffalo.indiv, percent = 100, unin = "m", unout = "km2")
specie1.scale
## [1] 0 617 1234 1851 2468 3085 3702 4319 4936 5553 6170
## [12] 6787 7404 8021 8639 9256 9873 10490 11107 11724 12341
Specie1 polygon
specie1.poly <- mcp.population(buffalo.indiv, percent = 100)
specie1.poly
## Object of class "SpatialPolygonsDataFrame" (package sp):
##
## Number of SpatialPolygons: 1
##
## Variables measured:
## id area
## Syncerus caffer Syncerus caffer 110025
color = "green"
plot(specie1.poly, col = color, axes = TRUE)
points(buffalo.indiv, pch = 3)
title(main = "Syncerus caffer", xlab = "X", ylab = "Y")
Compute PDI for each specie
pdi.index.specie1 <- pdi.index(buffalo.indiv, percent = 100, specie1.scale,
unin = "m", unout = "km2")
Compute summaries of each specie PDI
summary.specie1 <- summary.pdi(pdi.index.specie1)
summary.specie1
## rowid pop.type max.pdi min.pdi mean.pdi se.pdi scale
## 1 1 Syncerus caffer 0 0 0.0 0.0 0
## 2 2 Syncerus caffer 617 -2978 -1944.3 524.5 617
## 3 3 Syncerus caffer 1234 -3478 -1887.1 677.4 1234
## 4 4 Syncerus caffer 1851 -4054 -2030.9 864.9 1851
## 5 5 Syncerus caffer 2468 -5630 -2693.5 1214.8 2468
## 6 6 Syncerus caffer 3085 -6551 -2817.4 1449.5 3085
## 7 7 Syncerus caffer 3702 -6414 -2508.0 1520.7 3702
## 8 8 Syncerus caffer 4319 -6549 -2381.6 1622.4 4319
## 9 9 Syncerus caffer 4936 -6700 -2223.2 1720.5 4936
## 10 10 Syncerus caffer 5553 -6617 -2013.2 1803.9 5553
## 11 11 Syncerus caffer 6170 -6312 -1589.8 1857.6 6170
## 12 12 Syncerus caffer 6787 -5695 -1070.0 1847.0 6787
## 13 13 Syncerus caffer 7404 -5371 -667.6 1875.3 7404
## 14 14 Syncerus caffer 8021 -5902 -726.4 2082.6 8021
## 15 15 Syncerus caffer 8639 -5854 -418.3 2190.2 8639
## 16 16 Syncerus caffer 9256 -6238 -526.4 2436.5 9256
## 17 17 Syncerus caffer 6683 -6397 -984.9 2210.6 9873
## 18 18 Syncerus caffer 6558 -6678 -1111.3 2322.2 10490
## 19 19 Syncerus caffer 5925 -6157 -947.7 2152.1 11107
## 20 20 Syncerus caffer 4621 -5961 -1369.0 1895.2 11724
## 21 21 Syncerus caffer 3742 -5958 -1662.6 1779.1 12341
Plot PDI
colorSpecie1 = "pink"
altColorSpecie1 = "red"
plot(pdi.index.specie1, col = colorSpecie1, linecol = altColorSpecie1, title = "Syncerus caffer")
Extra Plotting
library(RColorBrewer)
g = 11
my.cols <- rev(brewer.pal(g, "RdYlBu"))
Smooth scatter
require(KernSmooth)
smoothScatter(pdi.subsample.data$location.long, pdi.subsample.data$location.lat,
nrpoints = 0.3 * 1e+05, colramp = colorRampPalette(my.cols), pch = 19, cex = 0.3,
col = "green1")
Kernel density using MASS
library(MASS)
z <- kde2d(pdi.subsample.data$location.long, pdi.subsample.data$location.lat,
n = 50)
plot(pdi.subsample.data$location.long, pdi.subsample.data$location.lat, xlab = "X",
ylab = "Y", pch = 19, cex = 0.3, col = "gray60")
contour(z, drawlabels = FALSE, nlevels = g, col = my.cols, add = TRUE, lwd = 2)
abline(h = mean(pdi.subsample.data$location.long), v = mean(pdi.subsample.data$location.lat),
lwd = 2, col = "black")
legend("topleft", paste("r=", round(cor(pdi.subsample.data$location.long, pdi.subsample.data$location.lat),
2)), bty = "n")
umd-lib/animalmove documentation built on May 3, 2019, 2:24 p.m.
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Population Dispersion Index
Load package library
library(lubridate)
library(animalmove)
library(plyr)
library(ggplot2)
Subsample Data
Buffalo dataset has been saved in the package data directory , and loaded on the package load.
Raw buffalo dataset contains unaltered original data.
data(buffalo)
head(buffalo)
## event.id visible timestamp location.long location.lat
## 1 10210419 true 2005-02-17 05:05:00.000 31.77 -24.54
## 2 10210423 true 2005-02-17 05:05:00.000 31.77 -24.54
## 3 10210428 true 2005-02-17 06:08:00.000 31.76 -24.54
## 4 10210434 true 2005-02-17 06:08:00.000 31.76 -24.54
## 5 10210456 true 2005-02-17 07:05:00.000 31.76 -24.55
## 6 10210458 true 2005-02-17 08:05:00.000 31.76 -24.55
## behavioural.classification comments manually.marked.outlier sensor.type
## 1 0 24.3 NA gps
## 2 0 24.3 NA gps
## 3 0 29.5 NA gps
## 4 0 29.5 NA gps
## 5 0 35.8 NA gps
## 6 0 37.3 NA gps
## individual.taxon.canonical.name tag.local.identifier
## 1 Syncerus caffer #1764820
## 2 Syncerus caffer #1764820
## 3 Syncerus caffer #1764820
## 4 Syncerus caffer #1764820
## 5 Syncerus caffer #1764820
## 6 Syncerus caffer #1764820
## individual.local.identifier
## 1 Queen
## 2 Queen
## 3 Queen
## 4 Queen
## 5 Queen
## 6 Queen
## study.name utm.easting
## 1 Kruger African Buffalo, GPS tracking, South Africa 375051
## 2 Kruger African Buffalo, GPS tracking, South Africa 375051
## 3 Kruger African Buffalo, GPS tracking, South Africa 374851
## 4 Kruger African Buffalo, GPS tracking, South Africa 374851
## 5 Kruger African Buffalo, GPS tracking, South Africa 374527
## 6 Kruger African Buffalo, GPS tracking, South Africa 374492
## utm.northing utm.zone study.timezone study.local.timestamp
## 1 7285726 36S South Africa Standard Time 2005-02-17 07:05:00.000
## 2 7285726 36S South Africa Standard Time 2005-02-17 07:05:00.000
## 3 7285502 36S South Africa Standard Time 2005-02-17 08:08:00.000
## 4 7285502 36S South Africa Standard Time 2005-02-17 08:08:00.000
## 5 7284538 36S South Africa Standard Time 2005-02-17 09:05:00.000
## 6 7284644 36S South Africa Standard Time 2005-02-17 10:05:00.000
nrow(buffalo)
## [1] 28410
currentnames <- colnames(buffalo)
currentnames
## [1] "event.id" "visible"
## [3] "timestamp" "location.long"
## [5] "location.lat" "behavioural.classification"
## [7] "comments" "manually.marked.outlier"
## [9] "sensor.type" "individual.taxon.canonical.name"
## [11] "tag.local.identifier" "individual.local.identifier"
## [13] "study.name" "utm.easting"
## [15] "utm.northing" "utm.zone"
## [17] "study.timezone" "study.local.timestamp"
names(buffalo)[names(buffalo) == "timestamp"] <- "time"
names(buffalo)[names(buffalo) == "utm.easting"] <- "x"
names(buffalo)[names(buffalo) == "utm.northing"] <- "y"
names(buffalo)[names(buffalo) == "tag.local.identifier"] <- "id"
names(buffalo)[names(buffalo) == "individual.taxon.canonical.name"] <- "pop.type"
# Data Conversion
buffalo$time <- as.POSIXct(strptime(buffalo$time, format = "%Y-%m-%d %H:%M",
tz = "GMT"))
# Display new names
newnames <- colnames(buffalo)
newnames
## [1] "event.id" "visible"
## [3] "time" "location.long"
## [5] "location.lat" "behavioural.classification"
## [7] "comments" "manually.marked.outlier"
## [9] "sensor.type" "pop.type"
## [11] "id" "individual.local.identifier"
## [13] "study.name" "x"
## [15] "y" "utm.zone"
## [17] "study.timezone" "study.local.timestamp"
head(buffalo)
## event.id visible time location.long location.lat
## 1 10210419 true 2005-02-17 05:05:00 31.77 -24.54
## 2 10210423 true 2005-02-17 05:05:00 31.77 -24.54
## 3 10210428 true 2005-02-17 06:08:00 31.76 -24.54
## 4 10210434 true 2005-02-17 06:08:00 31.76 -24.54
## 5 10210456 true 2005-02-17 07:05:00 31.76 -24.55
## 6 10210458 true 2005-02-17 08:05:00 31.76 -24.55
## behavioural.classification comments manually.marked.outlier sensor.type
## 1 0 24.3 NA gps
## 2 0 24.3 NA gps
## 3 0 29.5 NA gps
## 4 0 29.5 NA gps
## 5 0 35.8 NA gps
## 6 0 37.3 NA gps
## pop.type id individual.local.identifier
## 1 Syncerus caffer #1764820 Queen
## 2 Syncerus caffer #1764820 Queen
## 3 Syncerus caffer #1764820 Queen
## 4 Syncerus caffer #1764820 Queen
## 5 Syncerus caffer #1764820 Queen
## 6 Syncerus caffer #1764820 Queen
## study.name x y
## 1 Kruger African Buffalo, GPS tracking, South Africa 375051 7285726
## 2 Kruger African Buffalo, GPS tracking, South Africa 375051 7285726
## 3 Kruger African Buffalo, GPS tracking, South Africa 374851 7285502
## 4 Kruger African Buffalo, GPS tracking, South Africa 374851 7285502
## 5 Kruger African Buffalo, GPS tracking, South Africa 374527 7284538
## 6 Kruger African Buffalo, GPS tracking, South Africa 374492 7284644
## utm.zone study.timezone study.local.timestamp
## 1 36S South Africa Standard Time 2005-02-17 07:05:00.000
## 2 36S South Africa Standard Time 2005-02-17 07:05:00.000
## 3 36S South Africa Standard Time 2005-02-17 08:08:00.000
## 4 36S South Africa Standard Time 2005-02-17 08:08:00.000
## 5 36S South Africa Standard Time 2005-02-17 09:05:00.000
## 6 36S South Africa Standard Time 2005-02-17 10:05:00.000
Number of rows in the buffalo data & data set structure
length(table((buffalo$id)))
## [1] 6
str(buffalo)
## 'data.frame': 28410 obs. of 18 variables:
## $ event.id : int 10210419 10210423 10210428 10210434 10210456 10210458 10210483 10210535 10210544 10210558 ...
## $ visible : chr "true" "true" "true" "true" ...
## $ time : POSIXct, format: "2005-02-17 05:05:00" "2005-02-17 05:05:00" ...
## $ location.long : num 31.8 31.8 31.8 31.8 31.8 ...
## $ location.lat : num -24.5 -24.5 -24.5 -24.5 -24.5 ...
## $ behavioural.classification : int 0 0 0 0 0 0 0 0 0 0 ...
## $ comments : num 24.3 24.3 29.5 29.5 35.8 37.3 38.9 39.6 40 38.1 ...
## $ manually.marked.outlier : logi NA NA NA NA NA NA ...
## $ sensor.type : chr "gps" "gps" "gps" "gps" ...
## $ pop.type : chr "Syncerus caffer" "Syncerus caffer" "Syncerus caffer" "Syncerus caffer" ...
## $ id : chr "#1764820" "#1764820" "#1764820" "#1764820" ...
## $ individual.local.identifier: chr "Queen" "Queen" "Queen" "Queen" ...
## $ study.name : chr "Kruger African Buffalo, GPS tracking, South Africa" "Kruger African Buffalo, GPS tracking, South Africa" "Kruger African Buffalo, GPS tracking, South Africa" "Kruger African Buffalo, GPS tracking, South Africa" ...
## $ x : num 375051 375051 374851 374851 374527 ...
## $ y : num 7285726 7285726 7285502 7285502 7284538 ...
## $ utm.zone : chr "36S" "36S" "36S" "36S" ...
## $ study.timezone : chr "South Africa Standard Time" "South Africa Standard Time" "South Africa Standard Time" "South Africa Standard Time" ...
## $ study.local.timestamp : chr "2005-02-17 07:05:00.000" "2005-02-17 07:05:00.000" "2005-02-17 08:08:00.000" "2005-02-17 08:08:00.000" ...
Subsample data within time interval
We select at most 6 individuals within 2009, time interval 50 hours, and accuracy 50 hours, and subsampling scheme for Realized Mobility Index
pdi.subsample.data <- subsample(dat = buffalo, start = c("2005-02-17 00:00:00"),
end = "2006-12-31 00:00:00", interval = c("50 hours"), accuracy = c("3 hours"),
minIndiv = 3, maxIndiv = 6, mustIndiv = NULL, index.type = "pdi")
## [1] "50 hours"
## [1] "3 hours"
## [1] "2005-02-17 00:00:00"
## 'data.frame': 8 obs. of 9 variables:
## $ numberOfIndividuals: int 3 4 5 6 5 5 4 3
## $ pairsOfCompleteSets: int 32 19 8 7 9 18 37 49
## $ completeSets : int 34 21 10 9 10 20 39 52
## $ scanInterval : chr "50 hours" "50 hours" "50 hours" "50 hours" ...
## $ scanAccuracy : chr "3 hours" "3 hours" "3 hours" "3 hours" ...
## $ firstScantime : chr "2005-02-17 00:00:00" "2005-02-17 00:00:00" "2005-02-17 00:00:00" "2005-02-17 00:00:00" ...
## $ firstOverlap : chr "2005-07-27 10:00" "2005-08-23 12:00" "2005-09-15 10:00" "2005-09-17 12:00" ...
## $ lastOverlap : chr "2005-10-06 06:00" "2005-10-06 06:00" "2005-10-06 06:00" "2005-10-06 06:00" ...
## $ lastScantime : chr "2006-12-31 00:00:00" "2006-12-31 00:00:00" "2006-12-31 00:00:00" "2006-12-31 00:00:00" ...
buffalo.indiv <- Individuals(pdi.subsample.data, id = "id", time = "time", x = "x",
y = "y", group.by = "pop.type", proj4string = CRS("+proj=utm +zone=28 +datum=WGS84"))
Analysis
Display bounding box and scale for Specie1
bbox.coord1 <- bbox.coordinates(buffalo.indiv, percent = 100, unin = "m", unout = "km2")
bbox.coord1
## min max
## x 369957 394639
## y 7227424 7319263
specie1.scale <- bbox.scale(buffalo.indiv, percent = 100, unin = "m", unout = "km2")
specie1.scale
## [1] 0 617 1234 1851 2468 3085 3702 4319 4936 5553 6170
## [12] 6787 7404 8021 8639 9256 9873 10490 11107 11724 12341
Specie1 polygon
specie1.poly <- mcp.population(buffalo.indiv, percent = 100)
specie1.poly
## Object of class "SpatialPolygonsDataFrame" (package sp):
##
## Number of SpatialPolygons: 1
##
## Variables measured:
## id area
## Syncerus caffer Syncerus caffer 110025
color = "green"
plot(specie1.poly, col = color, axes = TRUE)
points(buffalo.indiv, pch = 3)
title(main = "Syncerus caffer", xlab = "X", ylab = "Y")
Compute PDI for each specie
pdi.index.specie1 <- pdi.index(buffalo.indiv, percent = 100, specie1.scale,
unin = "m", unout = "km2")
Compute summaries of each specie PDI
summary.specie1 <- summary.pdi(pdi.index.specie1)
summary.specie1
## rowid pop.type max.pdi min.pdi mean.pdi se.pdi scale
## 1 1 Syncerus caffer 0 0 0.0 0.0 0
## 2 2 Syncerus caffer 617 -2978 -1944.3 524.5 617
## 3 3 Syncerus caffer 1234 -3478 -1887.1 677.4 1234
## 4 4 Syncerus caffer 1851 -4054 -2030.9 864.9 1851
## 5 5 Syncerus caffer 2468 -5630 -2693.5 1214.8 2468
## 6 6 Syncerus caffer 3085 -6551 -2817.4 1449.5 3085
## 7 7 Syncerus caffer 3702 -6414 -2508.0 1520.7 3702
## 8 8 Syncerus caffer 4319 -6549 -2381.6 1622.4 4319
## 9 9 Syncerus caffer 4936 -6700 -2223.2 1720.5 4936
## 10 10 Syncerus caffer 5553 -6617 -2013.2 1803.9 5553
## 11 11 Syncerus caffer 6170 -6312 -1589.8 1857.6 6170
## 12 12 Syncerus caffer 6787 -5695 -1070.0 1847.0 6787
## 13 13 Syncerus caffer 7404 -5371 -667.6 1875.3 7404
## 14 14 Syncerus caffer 8021 -5902 -726.4 2082.6 8021
## 15 15 Syncerus caffer 8639 -5854 -418.3 2190.2 8639
## 16 16 Syncerus caffer 9256 -6238 -526.4 2436.5 9256
## 17 17 Syncerus caffer 6683 -6397 -984.9 2210.6 9873
## 18 18 Syncerus caffer 6558 -6678 -1111.3 2322.2 10490
## 19 19 Syncerus caffer 5925 -6157 -947.7 2152.1 11107
## 20 20 Syncerus caffer 4621 -5961 -1369.0 1895.2 11724
## 21 21 Syncerus caffer 3742 -5958 -1662.6 1779.1 12341
Plot PDI
colorSpecie1 = "pink"
altColorSpecie1 = "red"
plot(pdi.index.specie1, col = colorSpecie1, linecol = altColorSpecie1, title = "Syncerus caffer")
Extra Plotting
library(RColorBrewer)
g = 11
my.cols <- rev(brewer.pal(g, "RdYlBu"))
Smooth scatter
require(KernSmooth)
smoothScatter(pdi.subsample.data$location.long, pdi.subsample.data$location.lat,
nrpoints = 0.3 * 1e+05, colramp = colorRampPalette(my.cols), pch = 19, cex = 0.3,
col = "green1")
Kernel density using MASS
library(MASS)
z <- kde2d(pdi.subsample.data$location.long, pdi.subsample.data$location.lat,
n = 50)
plot(pdi.subsample.data$location.long, pdi.subsample.data$location.lat, xlab = "X",
ylab = "Y", pch = 19, cex = 0.3, col = "gray60")
contour(z, drawlabels = FALSE, nlevels = g, col = my.cols, add = TRUE, lwd = 2)
abline(h = mean(pdi.subsample.data$location.long), v = mean(pdi.subsample.data$location.lat),
lwd = 2, col = "black")
legend("topleft", paste("r=", round(cor(pdi.subsample.data$location.long, pdi.subsample.data$location.lat),
2)), bty = "n")
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