Heho feeder use analysis
2024-03-22
Background
Analysis of feeder visitation data for helmeted honeyeaters (hehos) at Yellingbo. We are comparing feeder use by wild-bred (Yellingbo; YB) versus captive-bred (Healesville Sanctuary; HS) individuals. The data come from cameras placed near individual feeders that enable recording of visits to feeders by colour-banded individuals, and interactions between those individuals and other birds.
Note: the feeder data has not been systematically captured. This means some feeders have had more monitoring than others, and feeders have been recorded at different times of the day. Also, individuals may vary in how long they were present at the study site (and thus were able to make use of feeders). This should be kept in mind when interpreting the results.
Approach
We use various datasets from feeder observations to address three research questions:
Question 1: do wild- and captive-bred birds differ in their use of feeders at Yellingbo? - how many feeders are used by birds of different origins? - how often do birds of different origins visit feeders? - does the range of feeders used and frequency of visitation differ by sex?
Question 2:
library(ggplot2)
library(dplyr)
library(plotrix)
library(reshape2)
library(tidyr)
library(ggrepel)
library(scales)
library(scatterpie)
library(ggthemes)knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE, tidy = TRUE)Data
The data are in a file with 2,377 feeder visits by 29 individuals as rows. For each row there are four descriptor columns: Feeder_ID (the feeder’s label), Bird_ID (bird identity), Bird_origin (HS or YB) and Sex (M/F). There are 13 captive-bred (HS) and 16 wild-bred (YB) birds in the dataset.
#load data
feeder<-read.csv("data/bird.feeder.csv")
head(feeder)## Date Feeder_ID Bird_ID Sex Bird_origin Persistence Feeder_days
## 1 22/11/21 A1 LB/O-B F YB full 38
## 2 22/11/21 A1 LB/O-B F YB full 38
## 3 22/11/21 A1 M/W/DB-S M HS full 38
## 4 22/11/21 A1 R/W/M-S M HS full 38
## 5 22/11/21 A1 R/W/M-S M HS full 38
## 6 22/11/21 A1 R/Y-LB M YB full 38Results
We first create a summary file that shows how many feeders were visited by each bird (range 1-13).
#sumarise number of unique feeders visited by each bird
feeder.use<- feeder %>%
group_by(Bird_origin, Bird_ID, Sex)%>%
summarise(count_distinct = n_distinct(Feeder_ID))
feeder.use## # A tibble: 29 × 4
## # Groups: Bird_origin, Bird_ID [29]
## Bird_origin Bird_ID Sex count_distinct
## <chr> <chr> <chr> <int>
## 1 HS DB/M/DB-S M 12
## 2 HS DB/M/Y-S M 9
## 3 HS DB/Y/LG-S M 11
## 4 HS LB/M/LG-S F 10
## 5 HS LG/DB/LG-S M 11
## 6 HS LG/R/LB-S M 8
## 7 HS LG/R/Y-S F 7
## 8 HS M/LG/M-S M 13
## 9 HS M/W/DB-S M 12
## 10 HS R/DB/LG-S M 9
## # ℹ 19 more rowsWe can see the full range, with one bird recorded only at a single feeder, and one bird recorded at all 13 feeders.
What about the number of feeder visits made by each individual?
#total number visits to all feeders for each bird
bird.visits<- feeder %>%
group_by(Bird_origin, Bird_ID, Sex)%>%
summarise(n = n()) %>%
mutate(sum(n))
bird.visits## # A tibble: 29 × 5
## # Groups: Bird_origin, Bird_ID [29]
## Bird_origin Bird_ID Sex n `sum(n)`
## <chr> <chr> <chr> <int> <int>
## 1 HS DB/M/DB-S M 91 91
## 2 HS DB/M/Y-S M 131 131
## 3 HS DB/Y/LG-S M 128 128
## 4 HS LB/M/LG-S F 121 121
## 5 HS LG/DB/LG-S M 176 176
## 6 HS LG/R/LB-S M 38 38
## 7 HS LG/R/Y-S F 104 104
## 8 HS M/LG/M-S M 285 285
## 9 HS M/W/DB-S M 164 164
## 10 HS R/DB/LG-S M 53 53
## # ℹ 19 more rowsThe number of total visits recorded ranges from 6 to 285. We can also break this down by feeder and date.
#number of visits to each feeder by individual birds
feeder.date.visits<-feeder%>% count(Bird_origin, Date, Feeder_ID, Bird_ID)
feeder.date.visits## Bird_origin Date Feeder_ID Bird_ID n
## 1 HS 01/09/21 A6 DB/M/Y-S 1
## 2 HS 01/09/21 A6 DB/Y/LG-S 2
## 3 HS 01/09/21 A6 Y/R/LB-S 1
## 4 HS 01/11/21 A10 LG/DB/LG-S 2
## 5 HS 01/11/21 A10 R/W/M-S 5
## 6 HS 01/11/21 A2 DB/M/Y-S 4
## 7 HS 01/11/21 A2 DB/Y/LG-S 4
## 8 HS 01/11/21 A2 LG/DB/LG-S 3
## 9 HS 01/11/21 A2 LG/R/Y-S 3
## 10 HS 01/11/21 A2 M/LG/M-S 18
## 11 HS 01/11/21 A2 M/W/DB-S 7
## 12 HS 01/11/21 A2 R/W/M-S 3
## 13 HS 01/11/21 A2 Y/LB/Y-S 4
## 14 HS 01/11/21 A2 Y/R/LB-S 6
## 15 HS 01/11/21 A4 M/LG/M-S 2
## 16 HS 01/11/21 A4 Y/R/LB-S 2
## 17 HS 01/11/21 A7 DB/M/Y-S 1
## 18 HS 01/11/21 A7 LB/M/LG-S 2
## 19 HS 01/11/21 A7 LG/DB/LG-S 1
## 20 HS 01/11/21 A7 R/W/M-S 1
## 21 HS 01/11/21 A8 DB/M/DB-S 3
## 22 HS 03/12/21 A2 DB/M/Y-S 3
## 23 HS 03/12/21 A2 DB/Y/LG-S 7
## 24 HS 03/12/21 A2 LG/DB/LG-S 2
## 25 HS 03/12/21 A2 M/LG/M-S 22
## 26 HS 03/12/21 A2 M/W/DB-S 4
## 27 HS 03/12/21 A2 R/W/M-S 1
## 28 HS 03/12/21 A2 Y/LB/Y-S 10
## 29 HS 03/12/21 A2 Y/R/LB-S 2
## 30 HS 03/12/21 A4 DB/Y/LG-S 1
## 31 HS 03/12/21 A7 DB/M/Y-S 2
## 32 HS 03/12/21 A7 LB/M/LG-S 8
## 33 HS 03/12/21 A7 LG/DB/LG-S 6
## 34 HS 03/12/21 A7 R/W/M-S 3
## 35 HS 04/11/21 A10 DB/M/DB-S 1
## 36 HS 04/11/21 A10 DB/Y/LG-S 1
## 37 HS 04/11/21 A10 LB/M/LG-S 2
## 38 HS 04/11/21 A10 LG/DB/LG-S 4
## 39 HS 04/11/21 A10 M/LG/M-S 2
## 40 HS 04/11/21 A10 M/W/DB-S 1
## 41 HS 04/11/21 A10 R/W/M-S 4
## 42 HS 04/11/21 A2 DB/M/Y-S 4
## 43 HS 04/11/21 A2 DB/Y/LG-S 5
## 44 HS 04/11/21 A2 LG/DB/LG-S 2
## 45 HS 04/11/21 A2 M/LG/M-S 14
## 46 HS 04/11/21 A2 M/W/DB-S 9
## 47 HS 04/11/21 A2 R/W/M-S 1
## 48 HS 04/11/21 A2 Y/LB/Y-S 6
## 49 HS 04/11/21 A2 Y/R/LB-S 4
## 50 HS 04/11/21 A7 LB/M/LG-S 4
## 51 HS 04/11/21 A7 LG/DB/LG-S 1
## 52 HS 06/09/21 A10 LG/DB/LG-S 8
## 53 HS 06/09/21 A10 LG/R/LB-S 2
## 54 HS 06/09/21 A10 M/LG/M-S 2
## 55 HS 06/09/21 A10 M/W/DB-S 9
## 56 HS 06/09/21 A10 R/DB/LG-S 4
## 57 HS 06/09/21 A10 R/W/M-S 11
## 58 HS 06/09/21 A10 Y/LB/Y-S 5
## 59 HS 06/09/21 A13 LG/R/Y-S 1
## 60 HS 06/09/21 A7 DB/M/Y-S 2
## 61 HS 06/09/21 A7 LB/M/LG-S 8
## 62 HS 06/09/21 A7 LG/DB/LG-S 12
## 63 HS 06/09/21 A7 LG/R/LB-S 3
## 64 HS 06/09/21 A7 M/LG/M-S 1
## 65 HS 06/09/21 A7 R/DB/LG-S 6
## 66 HS 06/09/21 A7 R/W/M-S 1
## 67 HS 06/10/21 A2 DB/Y/LG-S 2
## 68 HS 06/10/21 A2 M/LG/M-S 1
## 69 HS 06/10/21 A2 M/W/DB-S 2
## 70 HS 06/10/21 A2 Y/LB/Y-S 2
## 71 HS 06/10/21 A4 DB/M/Y-S 1
## 72 HS 06/10/21 A4 DB/Y/LG-S 2
## 73 HS 06/10/21 A4 LG/DB/LG-S 2
## 74 HS 06/10/21 A4 M/LG/M-S 1
## 75 HS 06/10/21 A4 M/W/DB-S 1
## 76 HS 06/10/21 A4 R/W/M-S 1
## 77 HS 06/10/21 A4 Y/LB/Y-S 1
## 78 HS 06/10/21 A4 Y/R/LB-S 8
## 79 HS 06/10/21 A7 DB/M/DB-S 1
## 80 HS 06/10/21 A7 LB/M/LG-S 3
## 81 HS 06/10/21 A7 LG/DB/LG-S 2
## 82 HS 06/10/21 A7 M/W/DB-S 1
## 83 HS 06/10/21 A7 R/W/M-S 1
## 84 HS 08/09/21 A3 DB/M/Y-S 7
## 85 HS 08/09/21 A3 LG/DB/LG-S 1
## 86 HS 08/09/21 A3 LG/R/Y-S 11
## 87 HS 08/09/21 A4 DB/M/Y-S 1
## 88 HS 08/09/21 A4 LG/DB/LG-S 1
## 89 HS 08/09/21 A4 LG/R/LB-S 3
## 90 HS 08/09/21 A4 M/LG/M-S 2
## 91 HS 08/09/21 A4 M/W/DB-S 3
## 92 HS 08/09/21 A4 Y/R/LB-S 5
## 93 HS 08/10/21 A10 DB/M/DB-S 2
## 94 HS 08/10/21 A10 LB/M/LG-S 5
## 95 HS 08/10/21 A10 LG/DB/LG-S 1
## 96 HS 08/10/21 A10 R/W/M-S 5
## 97 HS 08/10/21 A10 Y/R/LB-S 1
## 98 HS 08/10/21 A2 DB/M/DB-S 3
## 99 HS 08/10/21 A2 DB/M/Y-S 1
## 100 HS 08/10/21 A2 DB/Y/LG-S 5
## 101 HS 08/10/21 A2 LG/DB/LG-S 2
## 102 HS 08/10/21 A2 M/LG/M-S 8
## 103 HS 08/10/21 A2 M/W/DB-S 1
## 104 HS 08/10/21 A2 Y/LB/Y-S 6
## 105 HS 08/10/21 A3 DB/M/Y-S 7
## 106 HS 08/10/21 A3 LG/R/Y-S 7
## 107 HS 08/10/21 A6 DB/Y/LG-S 4
## 108 HS 08/10/21 A6 LG/DB/LG-S 1
## 109 HS 08/10/21 A6 M/LG/M-S 1
## 110 HS 08/10/21 A6 Y/R/LB-S 1
## 111 HS 08/12/21 A1 LB/M/LG-S 2
## 112 HS 08/12/21 A1 LG/DB/LG-S 3
## 113 HS 08/12/21 A1 LG/R/Y-S 1
## 114 HS 08/12/21 A1 M/LG/M-S 1
## 115 HS 08/12/21 A1 M/W/DB-S 1
## 116 HS 08/12/21 A1 R/W/M-S 2
## 117 HS 08/12/21 A1 Y/R/LB-S 2
## 118 HS 08/12/21 A10 DB/M/DB-S 1
## 119 HS 08/12/21 A10 LB/M/LG-S 1
## 120 HS 08/12/21 A10 LG/DB/LG-S 4
## 121 HS 08/12/21 A10 R/W/M-S 3
## 122 HS 08/12/21 A4 DB/M/DB-S 3
## 123 HS 08/12/21 A4 DB/Y/LG-S 2
## 124 HS 08/12/21 A4 R/W/M-S 2
## 125 HS 10/12/21 A11 DB/M/DB-S 3
## 126 HS 10/12/21 A11 LB/M/LG-S 1
## 127 HS 10/12/21 A11 LG/DB/LG-S 2
## 128 HS 10/12/21 A11 R/W/M-S 2
## 129 HS 10/12/21 A2 DB/M/DB-S 1
## 130 HS 10/12/21 A2 DB/M/Y-S 3
## 131 HS 10/12/21 A2 DB/Y/LG-S 5
## 132 HS 10/12/21 A2 M/LG/M-S 31
## 133 HS 10/12/21 A2 M/W/DB-S 9
## 134 HS 10/12/21 A2 R/W/M-S 9
## 135 HS 10/12/21 A2 Y/LB/Y-S 7
## 136 HS 10/12/21 A2 Y/R/LB-S 7
## 137 HS 10/12/21 A6 DB/M/DB-S 2
## 138 HS 10/12/21 A6 DB/Y/LG-S 1
## 139 HS 10/12/21 A6 LG/DB/LG-S 2
## 140 HS 10/12/21 A6 LG/R/Y-S 2
## 141 HS 10/12/21 A6 M/W/DB-S 1
## 142 HS 10/12/21 A7 LB/M/LG-S 7
## 143 HS 10/12/21 A7 LG/DB/LG-S 7
## 144 HS 10/12/21 A7 R/W/M-S 5
## 145 HS 11/10/21 A2 DB/M/DB-S 6
## 146 HS 11/10/21 A2 DB/M/Y-S 3
## 147 HS 11/10/21 A2 DB/Y/LG-S 3
## 148 HS 11/10/21 A2 LG/DB/LG-S 4
## 149 HS 11/10/21 A2 M/LG/M-S 10
## 150 HS 11/10/21 A2 M/W/DB-S 9
## 151 HS 11/10/21 A2 R/W/M-S 1
## 152 HS 11/10/21 A2 Y/LB/Y-S 4
## 153 HS 11/10/21 A2 Y/R/LB-S 4
## 154 HS 13/09/21 A2 DB/M/DB-S 10
## 155 HS 13/09/21 A2 DB/Y/LG-S 6
## 156 HS 13/09/21 A2 LG/R/LB-S 3
## 157 HS 13/09/21 A2 M/LG/M-S 13
## 158 HS 13/09/21 A2 M/W/DB-S 9
## 159 HS 13/09/21 A2 R/DB/LG-S 1
## 160 HS 13/09/21 A2 R/W/M-S 6
## 161 HS 13/09/21 A2 Y/LB/Y-S 5
## 162 HS 13/09/21 A2 Y/R/LB-S 5
## 163 HS 13/10/21 A2 DB/M/Y-S 1
## 164 HS 13/10/21 A2 DB/Y/LG-S 2
## 165 HS 13/10/21 A2 LG/DB/LG-S 1
## 166 HS 13/10/21 A2 M/LG/M-S 14
## 167 HS 13/10/21 A2 M/W/DB-S 4
## 168 HS 13/10/21 A2 Y/LB/Y-S 2
## 169 HS 13/10/21 A2 Y/R/LB-S 2
## 170 HS 13/10/21 A3 DB/M/Y-S 6
## 171 HS 13/10/21 A3 LG/R/Y-S 10
## 172 HS 13/10/21 A7 LB/M/LG-S 6
## 173 HS 13/10/21 A7 LG/DB/LG-S 3
## 174 HS 13/10/21 A7 R/W/M-S 1
## 175 HS 13/10/21 A7 Y/LB/Y-S 1
## 176 HS 13/10/21 A8 DB/M/DB-S 3
## 177 HS 13/10/21 A8 DB/Y/LG-S 2
## 178 HS 13/10/21 A8 LG/DB/LG-S 4
## 179 HS 13/10/21 A8 M/LG/M-S 3
## 180 HS 13/10/21 A8 M/W/DB-S 1
## 181 HS 13/10/21 A8 R/W/M-S 3
## 182 HS 13/10/21 A8 Y/R/LB-S 1
## 183 HS 13/12/21 A1 DB/M/DB-S 1
## 184 HS 13/12/21 A1 LG/DB/LG-S 2
## 185 HS 13/12/21 A1 LG/R/Y-S 2
## 186 HS 13/12/21 A1 M/LG/M-S 1
## 187 HS 13/12/21 A1 M/W/DB-S 1
## 188 HS 13/12/21 A1 Y/R/LB-S 2
## 189 HS 13/12/21 A4 DB/M/DB-S 1
## 190 HS 13/12/21 A4 DB/M/Y-S 1
## 191 HS 13/12/21 A4 DB/Y/LG-S 3
## 192 HS 13/12/21 A4 R/W/M-S 1
## 193 HS 13/12/21 A8 R/W/M-S 1
## 194 HS 14/09/21 A4 DB/M/DB-S 1
## 195 HS 14/09/21 A4 DB/M/Y-S 2
## 196 HS 14/09/21 A4 M/LG/M-S 2
## 197 HS 14/09/21 A4 M/W/DB-S 1
## 198 HS 14/09/21 A4 Y/R/LB-S 7
## 199 HS 14/09/21 A8 LG/DB/LG-S 1
## 200 HS 16/08/21 A3 Y/R/LB-S 1
## 201 HS 16/12/21 A2 DB/M/DB-S 1
## 202 HS 16/12/21 A2 DB/M/Y-S 3
## 203 HS 16/12/21 A2 DB/Y/LG-S 2
## 204 HS 16/12/21 A2 M/LG/M-S 15
## 205 HS 16/12/21 A2 M/W/DB-S 5
## 206 HS 16/12/21 A2 R/W/M-S 2
## 207 HS 16/12/21 A2 Y/LB/Y-S 6
## 208 HS 16/12/21 A2 Y/R/LB-S 3
## 209 HS 16/12/21 A7 LB/M/LG-S 4
## 210 HS 16/12/21 A7 LG/DB/LG-S 2
## 211 HS 16/12/21 A7 R/W/M-S 1
## 212 HS 17/09/21 A10 DB/M/DB-S 3
## 213 HS 17/09/21 A10 DB/Y/LG-S 1
## 214 HS 17/09/21 A10 LB/M/LG-S 2
## 215 HS 17/09/21 A10 LG/DB/LG-S 8
## 216 HS 17/09/21 A10 LG/R/LB-S 7
## 217 HS 17/09/21 A10 M/LG/M-S 1
## 218 HS 17/09/21 A10 M/W/DB-S 2
## 219 HS 17/09/21 A10 R/DB/LG-S 7
## 220 HS 17/09/21 A10 R/W/M-S 8
## 221 HS 17/09/21 A10 Y/LB/Y-S 1
## 222 HS 17/09/21 A10 Y/R/LB-S 1
## 223 HS 17/09/21 A11 Y/LB/Y-S 1
## 224 HS 17/09/21 A7 LB/M/LG-S 7
## 225 HS 17/09/21 A7 LG/DB/LG-S 2
## 226 HS 17/09/21 A7 LG/R/LB-S 2
## 227 HS 17/09/21 A7 M/LG/M-S 1
## 228 HS 17/09/21 A7 R/DB/LG-S 6
## 229 HS 17/09/21 A7 Y/R/LB-S 1
## 230 HS 18/08/21 A3 LB/M/LG-S 1
## 231 HS 18/08/21 A3 R/DB/LG-S 3
## 232 HS 18/08/21 A7 LB/M/LG-S 1
## 233 HS 18/08/21 A7 LG/DB/LG-S 3
## 234 HS 18/08/21 A7 LG/R/LB-S 3
## 235 HS 18/08/21 A7 R/DB/LG-S 2
## 236 HS 19/08/21 A10 DB/M/DB-S 3
## 237 HS 19/08/21 A10 DB/Y/LG-S 2
## 238 HS 19/08/21 A10 LB/M/LG-S 2
## 239 HS 19/08/21 A10 LG/DB/LG-S 4
## 240 HS 19/08/21 A10 LG/R/LB-S 2
## 241 HS 19/08/21 A10 M/LG/M-S 3
## 242 HS 19/08/21 A10 M/W/DB-S 3
## 243 HS 19/08/21 A10 R/DB/LG-S 2
## 244 HS 19/08/21 A10 R/W/M-S 3
## 245 HS 19/08/21 A10 Y/LB/Y-S 2
## 246 HS 19/08/21 A10 Y/R/LB-S 3
## 247 HS 19/08/21 A11 DB/M/DB-S 1
## 248 HS 19/08/21 A11 DB/M/Y-S 1
## 249 HS 19/08/21 A11 DB/Y/LG-S 2
## 250 HS 19/08/21 A11 LB/M/LG-S 2
## 251 HS 19/08/21 A11 LG/DB/LG-S 3
## 252 HS 19/08/21 A11 LG/R/LB-S 2
## 253 HS 19/08/21 A11 LG/R/Y-S 1
## 254 HS 19/08/21 A11 M/LG/M-S 2
## 255 HS 19/08/21 A11 M/W/DB-S 2
## 256 HS 19/08/21 A11 R/DB/LG-S 4
## 257 HS 19/08/21 A11 R/W/M-S 2
## 258 HS 19/08/21 A11 Y/LB/Y-S 2
## 259 HS 19/08/21 A11 Y/R/LB-S 1
## 260 HS 19/11/21 A2 DB/M/Y-S 3
## 261 HS 19/11/21 A2 DB/Y/LG-S 3
## 262 HS 19/11/21 A2 M/LG/M-S 16
## 263 HS 19/11/21 A2 M/W/DB-S 10
## 264 HS 19/11/21 A2 Y/LB/Y-S 8
## 265 HS 19/11/21 A3 DB/M/Y-S 6
## 266 HS 19/11/21 A3 DB/Y/LG-S 1
## 267 HS 19/11/21 A3 LG/R/Y-S 6
## 268 HS 19/11/21 A4 DB/Y/LG-S 2
## 269 HS 19/11/21 A7 DB/M/DB-S 1
## 270 HS 19/11/21 A7 LB/M/LG-S 6
## 271 HS 19/11/21 A7 LG/DB/LG-S 6
## 272 HS 19/11/21 A7 R/W/M-S 2
## 273 HS 20/08/21 A13 DB/M/DB-S 2
## 274 HS 20/08/21 A13 DB/M/Y-S 2
## 275 HS 20/08/21 A13 DB/Y/LG-S 3
## 276 HS 20/08/21 A13 LG/DB/LG-S 1
## 277 HS 20/08/21 A13 LG/R/LB-S 1
## 278 HS 20/08/21 A13 LG/R/Y-S 1
## 279 HS 20/08/21 A13 M/LG/M-S 2
## 280 HS 20/08/21 A13 M/W/DB-S 2
## 281 HS 20/08/21 A13 Y/LB/Y-S 1
## 282 HS 20/08/21 A13 Y/R/LB-S 4
## 283 HS 20/08/21 A14 DB/M/DB-S 3
## 284 HS 20/08/21 A14 DB/M/Y-S 1
## 285 HS 20/08/21 A14 DB/Y/LG-S 1
## 286 HS 20/08/21 A14 LB/M/LG-S 5
## 287 HS 20/08/21 A14 M/LG/M-S 6
## 288 HS 20/08/21 A14 M/W/DB-S 5
## 289 HS 20/08/21 A14 R/DB/LG-S 7
## 290 HS 20/08/21 A14 Y/LB/Y-S 4
## 291 HS 20/08/21 A14 Y/R/LB-S 4
## 292 HS 20/10/21 A2 DB/M/Y-S 3
## 293 HS 20/10/21 A2 DB/Y/LG-S 6
## 294 HS 20/10/21 A2 LG/DB/LG-S 3
## 295 HS 20/10/21 A2 LG/R/Y-S 1
## 296 HS 20/10/21 A2 M/LG/M-S 13
## 297 HS 20/10/21 A2 M/W/DB-S 11
## 298 HS 20/10/21 A2 R/W/M-S 3
## 299 HS 20/10/21 A2 Y/LB/Y-S 1
## 300 HS 20/10/21 A2 Y/R/LB-S 6
## 301 HS 20/10/21 A3 DB/M/Y-S 3
## 302 HS 20/10/21 A3 LG/DB/LG-S 1
## 303 HS 20/10/21 A3 LG/R/Y-S 6
## 304 HS 20/10/21 A3 R/W/M-S 1
## 305 HS 20/10/21 A7 LB/M/LG-S 4
## 306 HS 20/10/21 A7 R/W/M-S 5
## 307 HS 20/10/21 A8 LG/DB/LG-S 4
## 308 HS 20/12/21 A1 LB/M/LG-S 1
## 309 HS 20/12/21 A1 LG/DB/LG-S 1
## 310 HS 20/12/21 A1 R/W/M-S 2
## 311 HS 22/09/21 A3 DB/M/Y-S 7
## 312 HS 22/09/21 A3 LG/R/Y-S 6
## 313 HS 22/09/21 A7 LB/M/LG-S 6
## 314 HS 22/09/21 A7 LG/DB/LG-S 11
## 315 HS 22/09/21 A7 LG/R/LB-S 4
## 316 HS 22/11/21 A1 M/W/DB-S 1
## 317 HS 22/11/21 A1 R/W/M-S 2
## 318 HS 22/11/21 A1 Y/LB/Y-S 1
## 319 HS 22/11/21 A10 DB/M/DB-S 1
## 320 HS 22/11/21 A10 LG/DB/LG-S 4
## 321 HS 22/11/21 A10 R/W/M-S 3
## 322 HS 22/11/21 A11 DB/M/DB-S 1
## 323 HS 22/11/21 A4 DB/Y/LG-S 3
## 324 HS 22/11/21 A8 R/W/M-S 1
## 325 HS 22/12/21 A1 DB/M/DB-S 2
## 326 HS 22/12/21 A1 DB/Y/LG-S 4
## 327 HS 22/12/21 A1 LG/DB/LG-S 1
## 328 HS 22/12/21 A1 M/LG/M-S 2
## 329 HS 22/12/21 A1 M/W/DB-S 1
## 330 HS 22/12/21 A1 R/W/M-S 1
## 331 HS 22/12/21 A11 DB/M/DB-S 3
## 332 HS 22/12/21 A11 LG/DB/LG-S 1
## 333 HS 22/12/21 A4 DB/Y/LG-S 4
## 334 HS 22/12/21 A4 M/LG/M-S 1
## 335 HS 22/12/21 A4 M/W/DB-S 6
## 336 HS 24/08/21 A3 DB/M/DB-S 7
## 337 HS 24/08/21 A3 DB/M/Y-S 14
## 338 HS 24/08/21 A3 DB/Y/LG-S 11
## 339 HS 24/08/21 A3 LG/R/LB-S 1
## 340 HS 24/08/21 A3 LG/R/Y-S 7
## 341 HS 24/08/21 A3 M/LG/M-S 6
## 342 HS 24/08/21 A3 M/W/DB-S 6
## 343 HS 24/08/21 A3 R/DB/LG-S 2
## 344 HS 24/08/21 A3 R/W/M-S 2
## 345 HS 24/08/21 A3 Y/LB/Y-S 1
## 346 HS 24/08/21 A3 Y/R/LB-S 3
## 347 HS 24/08/21 A4 DB/M/DB-S 1
## 348 HS 24/08/21 A4 LB/M/LG-S 1
## 349 HS 24/08/21 A4 LG/R/LB-S 1
## 350 HS 24/08/21 A4 M/LG/M-S 3
## 351 HS 24/08/21 A4 R/DB/LG-S 1
## 352 HS 24/08/21 A4 Y/LB/Y-S 3
## 353 HS 24/08/21 A4 Y/R/LB-S 3
## 354 HS 24/08/21 A6 DB/M/DB-S 1
## 355 HS 24/08/21 A6 DB/M/Y-S 1
## 356 HS 24/08/21 A6 LB/M/LG-S 3
## 357 HS 24/08/21 A6 LG/R/LB-S 1
## 358 HS 24/08/21 A6 LG/R/Y-S 1
## 359 HS 24/08/21 A6 M/LG/M-S 1
## 360 HS 24/08/21 A6 M/W/DB-S 1
## 361 HS 24/08/21 A6 R/W/M-S 2
## 362 HS 24/08/21 A6 Y/LB/Y-S 4
## 363 HS 24/08/21 A6 Y/R/LB-S 5
## 364 HS 24/09/21 A3 DB/M/Y-S 7
## 365 HS 24/09/21 A3 LG/R/Y-S 8
## 366 HS 24/09/21 A3 R/W/M-S 1
## 367 HS 24/11/21 A2 DB/M/DB-S 1
## 368 HS 24/11/21 A2 DB/M/Y-S 3
## 369 HS 24/11/21 A2 DB/Y/LG-S 6
## 370 HS 24/11/21 A2 LG/DB/LG-S 1
## 371 HS 24/11/21 A2 M/LG/M-S 16
## 372 HS 24/11/21 A2 M/W/DB-S 6
## 373 HS 24/11/21 A2 R/W/M-S 2
## 374 HS 24/11/21 A2 Y/LB/Y-S 5
## 375 HS 24/11/21 A3 DB/M/Y-S 4
## 376 HS 24/11/21 A3 LG/R/Y-S 4
## 377 HS 24/11/21 A6 DB/Y/LG-S 1
## 378 HS 24/11/21 A7 DB/M/Y-S 1
## 379 HS 24/11/21 A7 LB/M/LG-S 7
## 380 HS 24/11/21 A7 LG/DB/LG-S 8
## 381 HS 24/11/21 A7 LG/R/Y-S 3
## 382 HS 24/11/21 A7 R/W/M-S 4
## 383 HS 24/11/21 A8 DB/M/DB-S 1
## 384 HS 25/08/21 A12 DB/M/DB-S 4
## 385 HS 25/08/21 A12 DB/Y/LG-S 4
## 386 HS 25/08/21 A12 LB/M/LG-S 6
## 387 HS 25/08/21 A12 M/LG/M-S 12
## 388 HS 25/08/21 A12 M/W/DB-S 12
## 389 HS 25/08/21 A12 R/DB/LG-S 1
## 390 HS 25/08/21 A12 R/W/M-S 1
## 391 HS 25/08/21 A12 Y/LB/Y-S 1
## 392 HS 25/08/21 A12 Y/R/LB-S 6
## 393 HS 26/08/21 A7 LB/M/LG-S 1
## 394 HS 26/08/21 A7 LG/DB/LG-S 4
## 395 HS 26/08/21 A7 LG/R/LB-S 1
## 396 HS 26/08/21 A7 R/DB/LG-S 3
## 397 HS 26/08/21 A8 LG/DB/LG-S 1
## 398 HS 26/08/21 A8 R/W/M-S 1
## 399 HS 27/08/21 A4 DB/Y/LG-S 1
## 400 HS 27/08/21 A9 LG/DB/LG-S 1
## 401 HS 27/08/21 A9 M/LG/M-S 1
## 402 HS 27/08/21 A9 R/DB/LG-S 1
## 403 HS 27/09/21 A2 DB/M/DB-S 1
## 404 HS 27/09/21 A2 LB/M/LG-S 1
## 405 HS 27/09/21 A2 LG/DB/LG-S 2
## 406 HS 27/09/21 A2 M/LG/M-S 9
## 407 HS 27/09/21 A2 M/W/DB-S 6
## 408 HS 27/09/21 A2 R/W/M-S 2
## 409 HS 27/09/21 A2 Y/LB/Y-S 4
## 410 HS 27/09/21 A2 Y/R/LB-S 1
## 411 HS 27/09/21 A8 LG/DB/LG-S 1
## 412 HS 27/10/21 A2 DB/M/DB-S 1
## 413 HS 27/10/21 A2 DB/M/Y-S 4
## 414 HS 27/10/21 A2 DB/Y/LG-S 4
## 415 HS 27/10/21 A2 LG/DB/LG-S 3
## 416 HS 27/10/21 A2 LG/R/Y-S 5
## 417 HS 27/10/21 A2 M/LG/M-S 23
## 418 HS 27/10/21 A2 M/W/DB-S 7
## 419 HS 27/10/21 A2 R/W/M-S 2
## 420 HS 27/10/21 A2 Y/LB/Y-S 9
## 421 HS 27/10/21 A2 Y/R/LB-S 6
## 422 HS 27/10/21 A3 DB/M/Y-S 2
## 423 HS 27/10/21 A3 LG/R/Y-S 5
## 424 HS 27/10/21 A6 DB/M/DB-S 3
## 425 HS 27/10/21 A6 DB/M/Y-S 2
## 426 HS 27/10/21 A6 DB/Y/LG-S 5
## 427 HS 27/10/21 A6 Y/R/LB-S 3
## 428 HS 27/10/21 A7 DB/M/Y-S 1
## 429 HS 27/10/21 A7 LB/M/LG-S 7
## 430 HS 27/10/21 A7 LG/DB/LG-S 8
## 431 HS 27/10/21 A7 R/W/M-S 3
## 432 HS 27/10/21 A8 DB/M/DB-S 1
## 433 HS 29/11/21 A1 LG/DB/LG-S 1
## 434 HS 29/11/21 A1 R/W/M-S 1
## 435 HS 29/11/21 A11 DB/M/DB-S 2
## 436 HS 29/11/21 A11 LG/DB/LG-S 1
## 437 HS 29/11/21 A11 R/W/M-S 2
## 438 HS 29/11/21 A6 DB/M/DB-S 2
## 439 HS 29/11/21 A6 DB/M/Y-S 3
## 440 HS 29/11/21 A6 DB/Y/LG-S 4
## 441 HS 29/11/21 A6 LG/DB/LG-S 1
## 442 HS 29/11/21 A6 LG/R/Y-S 1
## 443 HS 29/11/21 A7 LB/M/LG-S 4
## 444 HS 29/11/21 A7 LG/DB/LG-S 1
## 445 HS 29/11/21 A7 R/W/M-S 2
## 446 HS 30/09/21 A10 DB/M/DB-S 2
## 447 HS 30/09/21 A10 DB/M/Y-S 1
## 448 HS 30/09/21 A10 DB/Y/LG-S 1
## 449 HS 30/09/21 A10 LG/DB/LG-S 2
## 450 HS 30/09/21 A10 M/LG/M-S 1
## 451 HS 30/09/21 A10 R/W/M-S 1
## 452 HS 30/09/21 A10 Y/R/LB-S 1
## 453 HS 31/08/21 A10 R/DB/LG-S 1
## 454 HS 31/08/21 A10 R/W/M-S 3
## 455 HS 31/08/21 A3 DB/M/DB-S 1
## 456 HS 31/08/21 A3 DB/M/Y-S 9
## 457 HS 31/08/21 A3 LB/M/LG-S 1
## 458 HS 31/08/21 A3 LG/DB/LG-S 2
## 459 HS 31/08/21 A3 LG/R/LB-S 2
## 460 HS 31/08/21 A3 LG/R/Y-S 12
## 461 HS 31/08/21 A3 M/LG/M-S 2
## 462 HS 31/08/21 A3 M/W/DB-S 4
## 463 HS 31/08/21 A3 R/DB/LG-S 2
## 464 HS 31/08/21 A3 R/W/M-S 1
## 465 HS 31/08/21 A3 Y/LB/Y-S 4
## 466 YB 01/09/21 A1 DB/M-W/S 1
## 467 YB 01/09/21 A1 LB/O-B 5
## 468 YB 01/09/21 A6 O/M-P 1
## 469 YB 01/11/21 A10 Gold/W-LB 1
## 470 YB 01/11/21 A10 O/M-P 4
## 471 YB 01/11/21 A10 R/Y-LB 2
## 472 YB 01/11/21 A10 W/M/W-S 1
## 473 YB 01/11/21 A2 W/M/W-S 1
## 474 YB 01/11/21 A4 M/R/M-W/S 3
## 475 YB 01/11/21 A8 LB/DB/LB-LB 7
## 476 YB 01/11/21 A8 R/Y-LB 4
## 477 YB 03/12/21 A2 W/M/W-S 5
## 478 YB 03/12/21 A4 Gold/LG-LG 2
## 479 YB 03/12/21 A4 M/R/M-W/S 2
## 480 YB 03/12/21 A7 DB/M-W/S 2
## 481 YB 03/12/21 A7 Gold/W-LB 1
## 482 YB 04/11/21 A10 DB/M-W/S 2
## 483 YB 04/11/21 A10 Gold/W-LB 4
## 484 YB 04/11/21 A10 O/M-P 3
## 485 YB 04/11/21 A10 R/Y-LB 4
## 486 YB 04/11/21 A10 W/M/W-S 2
## 487 YB 04/11/21 A2 Gold/W-LB 2
## 488 YB 04/11/21 A2 W/M/W-S 2
## 489 YB 04/11/21 A7 DB/M-W/S 1
## 490 YB 04/11/21 A7 W/M/W-S 2
## 491 YB 04/11/21 A8 LB/DB/LB-LB 4
## 492 YB 04/11/21 A8 R/Y-LB 3
## 493 YB 06/09/21 A10 O/M-P 1
## 494 YB 06/09/21 A10 R/Y-LB 4
## 495 YB 06/09/21 A10 W/M/W-S 5
## 496 YB 06/09/21 A13 Gold/W-LB 1
## 497 YB 06/09/21 A13 P/LG/P-G 1
## 498 YB 06/10/21 A2 W/M/W-S 1
## 499 YB 06/10/21 A4 DB/M-W/S 1
## 500 YB 06/10/21 A4 M/R/M-W/S 3
## 501 YB 06/10/21 A4 R/Y-LB 1
## 502 YB 06/10/21 A7 W/M/W-S 1
## 503 YB 06/10/21 A8 LB/DB/LB-LB 5
## 504 YB 06/10/21 A8 R/Y-LB 5
## 505 YB 08/09/21 A4 M/R/M-W/S 2
## 506 YB 08/09/21 A4 O/M-P 2
## 507 YB 08/09/21 A4 R/Y-LB 1
## 508 YB 08/09/21 A4 W/M/W-S 1
## 509 YB 08/09/21 A8 R/Y-LB 3
## 510 YB 08/10/21 A10 Gold/W-LB 6
## 511 YB 08/10/21 A10 O/M-P 1
## 512 YB 08/10/21 A10 R/Y-LB 3
## 513 YB 08/10/21 A2 W/M/W-S 3
## 514 YB 08/10/21 A6 DB/M-W/S 3
## 515 YB 08/10/21 A6 M/R/M-W/S 2
## 516 YB 08/12/21 A1 DB/M-W/S 6
## 517 YB 08/12/21 A1 Gold/LG-LG 3
## 518 YB 08/12/21 A1 Gold/W-LB 2
## 519 YB 08/12/21 A1 LB/DB/LB-LB 1
## 520 YB 08/12/21 A1 LB/O-B 2
## 521 YB 08/12/21 A1 W/M/W-S 3
## 522 YB 08/12/21 A10 Gold/LG-LG 2
## 523 YB 08/12/21 A10 R/Y-LB 2
## 524 YB 08/12/21 A10 W/M/W-S 1
## 525 YB 08/12/21 A4 DB/M-W/S 1
## 526 YB 08/12/21 A4 Gold/LG-LG 2
## 527 YB 08/12/21 A4 M/R/M-W/S 6
## 528 YB 08/12/21 A4 W/M/W-S 1
## 529 YB 08/12/21 A8 LB/DB/LB-LB 4
## 530 YB 08/12/21 A8 R/Y-LB 1
## 531 YB 10/12/21 A11 Gold/LG-LG 2
## 532 YB 10/12/21 A11 LB/DB/LB-LB 1
## 533 YB 10/12/21 A11 R/Y-LB 1
## 534 YB 10/12/21 A2 DB/M-W/S 2
## 535 YB 10/12/21 A2 W/M/W-S 6
## 536 YB 10/12/21 A6 DB/M-W/S 2
## 537 YB 10/12/21 A6 M/R/M-W/S 4
## 538 YB 10/12/21 A7 W/M/W-S 1
## 539 YB 11/10/21 A2 DB/M-W/S 1
## 540 YB 11/10/21 A2 R/Y-LB 1
## 541 YB 11/10/21 A2 W/M/W-S 12
## 542 YB 13/09/21 A2 DB/M-W/S 2
## 543 YB 13/09/21 A2 Gold/W-LB 8
## 544 YB 13/09/21 A2 LB/O-B 6
## 545 YB 13/09/21 A2 LB/Y-LB 3
## 546 YB 13/09/21 A2 W/M/W-S 11
## 547 YB 13/10/21 A2 Gold/W-LB 1
## 548 YB 13/10/21 A2 W/M/W-S 7
## 549 YB 13/10/21 A7 O/M-P 3
## 550 YB 13/10/21 A8 LB/DB/LB-LB 15
## 551 YB 13/10/21 A8 O/M-P 2
## 552 YB 13/10/21 A8 R/Y-LB 17
## 553 YB 13/12/21 A1 DB/M-W/S 1
## 554 YB 13/12/21 A1 LB/O-B 1
## 555 YB 13/12/21 A1 M/R/M-W/S 1
## 556 YB 13/12/21 A1 R/Y-LB 2
## 557 YB 13/12/21 A1 W/M/W-S 5
## 558 YB 13/12/21 A4 M/R/M-W/S 2
## 559 YB 13/12/21 A8 LB/DB/LB-LB 5
## 560 YB 13/12/21 A8 R/Y-LB 3
## 561 YB 14/09/21 A4 M/R/M-W/S 4
## 562 YB 14/09/21 A8 LB/DB/LB-LB 6
## 563 YB 14/09/21 A8 R/Y-LB 7
## 564 YB 16/12/21 A2 DB/M-W/S 2
## 565 YB 16/12/21 A2 R/Y-LB 1
## 566 YB 16/12/21 A2 W/M/W-S 1
## 567 YB 16/12/21 A7 Gold/LG-LG 1
## 568 YB 17/09/21 A10 Gold/W-LB 2
## 569 YB 17/09/21 A10 LB/Y-LB 6
## 570 YB 17/09/21 A10 W/M/W-S 2
## 571 YB 17/09/21 A11 M/R/M-W/S 1
## 572 YB 17/09/21 A4 M/R/M-W/S 1
## 573 YB 17/09/21 A7 LB/O-B 1
## 574 YB 18/08/21 A3 LG/Gold-LB 1
## 575 YB 18/08/21 A7 Gold/W-LB 3
## 576 YB 19/08/21 A10 LB/R/LB-R 2
## 577 YB 19/08/21 A10 M/R/M-W/S 1
## 578 YB 19/08/21 A10 R/W-LB 2
## 579 YB 19/08/21 A11 Gold/W-LB 1
## 580 YB 19/08/21 A11 LB/Y-LB 3
## 581 YB 19/08/21 A11 LG/Gold-LB 7
## 582 YB 19/08/21 A11 M/R/M-W/S 1
## 583 YB 19/08/21 A11 P/O-LB 6
## 584 YB 19/08/21 A11 R/W-LB 1
## 585 YB 19/08/21 A11 R/Y-LB 2
## 586 YB 19/08/21 A12 O/M-P 1
## 587 YB 19/08/21 A12 R/W-LB 5
## 588 YB 19/08/21 A12 R/Y-LB 1
## 589 YB 19/11/21 A2 Gold/W-LB 4
## 590 YB 19/11/21 A2 O/M-P 6
## 591 YB 19/11/21 A2 W/M/W-S 8
## 592 YB 19/11/21 A4 M/R/M-W/S 1
## 593 YB 19/11/21 A7 LB/DB/LB-LB 1
## 594 YB 19/11/21 A7 R/Y-LB 2
## 595 YB 20/08/21 A13 DB/M-W/S 3
## 596 YB 20/08/21 A13 LB/DB/LB-LB 2
## 597 YB 20/08/21 A13 P/LG/P-G 2
## 598 YB 20/08/21 A13 R/Y-LB 1
## 599 YB 20/08/21 A13 W/M/W-S 2
## 600 YB 20/08/21 A14 DB/M-W/S 6
## 601 YB 20/08/21 A14 Gold/W-LB 2
## 602 YB 20/08/21 A14 LB/DB/LB-LB 3
## 603 YB 20/08/21 A14 LB/O-B 2
## 604 YB 20/08/21 A14 LB/R/LB-R 4
## 605 YB 20/08/21 A14 O/M-P 4
## 606 YB 20/08/21 A14 P/LG/P-G 3
## 607 YB 20/08/21 A14 R/W-LB 1
## 608 YB 20/08/21 A14 R/Y-LB 8
## 609 YB 20/08/21 A14 W/M/W-S 4
## 610 YB 20/08/21 A9 LB/O-B 1
## 611 YB 20/10/21 A2 LB/DB/LB-LB 1
## 612 YB 20/10/21 A2 R/Y-LB 3
## 613 YB 20/10/21 A2 W/M/W-S 7
## 614 YB 20/10/21 A3 M/R/M-W/S 1
## 615 YB 20/10/21 A8 LB/DB/LB-LB 6
## 616 YB 20/10/21 A8 R/Y-LB 6
## 617 YB 20/12/21 A1 DB/M-W/S 1
## 618 YB 20/12/21 A1 O/M-P 2
## 619 YB 20/12/21 A1 W/M/W-S 1
## 620 YB 20/12/21 A12 O/M-P 1
## 621 YB 22/09/21 A3 DB/M-W/S 1
## 622 YB 22/09/21 A3 LB/DB/LB-LB 1
## 623 YB 22/11/21 A1 LB/O-B 2
## 624 YB 22/11/21 A1 R/Y-LB 1
## 625 YB 22/11/21 A1 W/M/W-S 1
## 626 YB 22/11/21 A10 O/M-P 6
## 627 YB 22/11/21 A11 O/M-P 1
## 628 YB 22/11/21 A4 M/R/M-W/S 2
## 629 YB 22/11/21 A8 LB/DB/LB-LB 4
## 630 YB 22/11/21 A8 LB/O-B 1
## 631 YB 22/11/21 A8 R/Y-LB 5
## 632 YB 22/12/21 A1 DB/M-W/S 4
## 633 YB 22/12/21 A1 Gold/LG-LG 2
## 634 YB 22/12/21 A1 Gold/W-LB 5
## 635 YB 22/12/21 A1 LB/O-B 3
## 636 YB 22/12/21 A1 O/M-P 4
## 637 YB 22/12/21 A1 W/M/W-S 9
## 638 YB 22/12/21 A11 Gold/W-LB 2
## 639 YB 22/12/21 A11 LB/DB/LB-LB 1
## 640 YB 22/12/21 A4 M/R/M-W/S 5
## 641 YB 22/12/21 A4 W/M/W-S 1
## 642 YB 24/08/21 A3 W/M/W-S 1
## 643 YB 24/08/21 A4 Y/LG-LB 1
## 644 YB 24/08/21 A6 DB/M-W/S 4
## 645 YB 24/08/21 A6 Y/LG-LB 1
## 646 YB 24/11/21 A2 Gold/W-LB 2
## 647 YB 24/11/21 A2 O/M-P 1
## 648 YB 24/11/21 A2 W/M/W-S 7
## 649 YB 24/11/21 A6 Gold/LG-LG 2
## 650 YB 24/11/21 A7 Gold/LG-LG 3
## 651 YB 24/11/21 A8 Gold/LG-LG 4
## 652 YB 24/11/21 A8 LB/DB/LB-LB 12
## 653 YB 24/11/21 A8 R/Y-LB 6
## 654 YB 25/08/21 A12 DB/M-W/S 2
## 655 YB 25/08/21 A12 LB/O-B 1
## 656 YB 25/08/21 A12 LB/Y-LB 5
## 657 YB 25/08/21 A12 P/LG/P-G 1
## 658 YB 25/08/21 A14 LB/DB/LB-LB 2
## 659 YB 25/08/21 A14 LB/O-B 4
## 660 YB 25/08/21 A14 LB/R/LB-R 4
## 661 YB 25/08/21 A14 P/LG/P-G 4
## 662 YB 26/08/21 A8 LB/DB/LB-LB 4
## 663 YB 26/08/21 A8 R/Y-LB 6
## 664 YB 26/08/21 A8 W/M/W-S 8
## 665 YB 26/08/21 A8 Y/LG-LB 8
## 666 YB 27/08/21 A14 DB/M-W/S 1
## 667 YB 27/08/21 A14 LB/R/LB-R 1
## 668 YB 27/08/21 A9 LB/DB/LB-LB 1
## 669 YB 27/08/21 A9 O/M-P 1
## 670 YB 27/08/21 A9 R/Y-LB 1
## 671 YB 27/09/21 A2 LB/O-B 1
## 672 YB 27/09/21 A2 W/M/W-S 10
## 673 YB 27/09/21 A8 LB/DB/LB-LB 2
## 674 YB 27/09/21 A8 R/Y-LB 3
## 675 YB 27/09/21 A8 W/M/W-S 2
## 676 YB 27/10/21 A2 DB/M-W/S 1
## 677 YB 27/10/21 A2 M/R/M-W/S 1
## 678 YB 27/10/21 A2 R/Y-LB 1
## 679 YB 27/10/21 A2 W/M/W-S 4
## 680 YB 27/10/21 A6 DB/M-W/S 2
## 681 YB 27/10/21 A6 M/R/M-W/S 1
## 682 YB 27/10/21 A7 R/Y-LB 1
## 683 YB 27/10/21 A7 W/M/W-S 2
## 684 YB 27/10/21 A8 Gold/W-LB 1
## 685 YB 27/10/21 A8 LB/DB/LB-LB 15
## 686 YB 27/10/21 A8 O/M-P 1
## 687 YB 27/10/21 A8 R/W-LB 1
## 688 YB 27/10/21 A8 R/Y-LB 15
## 689 YB 27/10/21 A8 W/M/W-S 1
## 690 YB 29/11/21 A1 W/M/W-S 1
## 691 YB 29/11/21 A11 DB/M-W/S 6
## 692 YB 29/11/21 A11 Gold/W-LB 3
## 693 YB 29/11/21 A6 Gold/LG-LG 2
## 694 YB 29/11/21 A6 M/R/M-W/S 3
## 695 YB 29/11/21 A7 W/M/W-S 3
## 696 YB 30/09/21 A10 M/R/M-W/S 1
## 697 YB 30/09/21 A10 O/M-P 3
## 698 YB 30/09/21 A10 R/Y-LB 1
## 699 YB 30/09/21 A10 W/M/W-S 3
## 700 YB 30/09/21 A2 LB/O-B 1
## 701 YB 31/08/21 A10 Gold/W-LB 1
## 702 YB 31/08/21 A10 LB/O-B 2
## 703 YB 31/08/21 A10 LB/Y-LB 3
## 704 YB 31/08/21 A10 R/W-LB 1
## 705 YB 31/08/21 A10 R/Y-LB 1
## 706 YB 31/08/21 A10 W/M/W-S 1#number of visits to feeders by birds of different origins
origin.feeder<-feeder%>% count(Bird_origin, Feeder_ID)
origin.feeder## Bird_origin Feeder_ID n
## 1 HS A1 42
## 2 HS A10 177
## 3 HS A11 44
## 4 HS A12 47
## 5 HS A13 20
## 6 HS A14 36
## 7 HS A2 635
## 8 HS A3 222
## 9 HS A4 93
## 10 HS A6 64
## 11 HS A7 238
## 12 HS A8 32
## 13 HS A9 3
## 14 YB A1 69
## 15 YB A10 86
## 16 YB A11 39
## 17 YB A12 17
## 18 YB A13 12
## 19 YB A14 53
## 20 YB A2 136
## 21 YB A3 5
## 22 YB A4 45
## 23 YB A6 27
## 24 YB A7 28
## 25 YB A8 202
## 26 YB A9 4Number of visits to a single feeder by an individual ranges from 1 to 223!
To enable us to display various matrices, we create some subsets of the data (separate datafiles for HS and YB birds) and calculate proportions of visits across feeders.
#create subsets for each bird origin
HS.feeder.visits<-subset(feeder.date.visits, Bird_origin=="HS")
YB.feeder.visits<-subset(feeder.date.visits, Bird_origin=="YB")
#summarise and calculate proportions of visits across feeders for all birds
feeder.visits<-feeder %>%
group_by(Bird_ID, Feeder_ID) %>%
summarise(n = n()) %>%
mutate(freq = n / sum(n))Let’s look at feeder visits in the form of a matrix: all visits to all feeders by all birds.
#plot a matrix showing visit counts to each feeder by each bird
visit.counts<-acast(feeder.visits, Bird_ID ~ Feeder_ID , value.var='n',
fun.aggregate=sum, margins=TRUE)
visit.counts## A1 A10 A11 A12 A13 A14 A2 A3 A4 A6 A7 A8 A9 (all)
## DB/M-W/S 13 2 6 2 3 7 8 1 2 11 3 0 0 58
## DB/M/DB-S 3 13 10 4 2 3 24 8 6 8 2 8 0 91
## DB/M/Y-S 0 1 1 0 2 1 35 72 5 7 7 0 0 131
## DB/Y/LG-S 4 5 2 4 3 1 60 12 18 17 0 2 0 128
## Gold/LG-LG 5 2 2 0 0 0 0 0 4 4 4 4 0 25
## Gold/W-LB 7 14 6 0 1 2 17 0 0 0 4 1 0 52
## LB/DB/LB-LB 1 0 2 0 2 5 1 1 0 0 1 89 1 103
## LB/M/LG-S 3 12 3 6 0 5 1 2 1 3 85 0 0 121
## LB/O-B 13 2 0 1 0 6 8 0 0 0 1 1 1 33
## LB/R/LB-R 0 2 0 0 0 9 0 0 0 0 0 0 0 11
## LB/Y-LB 0 9 3 5 0 0 3 0 0 0 0 0 0 20
## LG/DB/LG-S 8 37 7 0 1 0 23 4 3 4 77 11 1 176
## LG/Gold-LB 0 0 7 0 0 0 0 1 0 0 0 0 0 8
## LG/R/LB-S 0 11 2 0 1 0 3 3 4 1 13 0 0 38
## LG/R/Y-S 3 0 1 0 2 0 9 82 0 4 3 0 0 104
## M/LG/M-S 4 9 2 12 2 6 223 8 11 2 2 3 1 285
## M/R/M-W/S 1 2 2 0 0 0 1 1 31 10 0 0 0 48
## M/W/DB-S 4 15 2 12 2 5 99 10 11 2 1 1 0 164
## O/M-P 6 18 1 2 0 4 7 0 2 1 3 3 1 48
## P/LG/P-G 0 0 0 1 3 7 0 0 0 0 0 0 0 11
## P/O-LB 0 0 6 0 0 0 0 0 0 0 0 0 0 6
## R/DB/LG-S 0 14 4 1 0 7 1 7 1 0 17 0 1 53
## R/W-LB 0 3 1 5 0 1 0 0 0 0 0 1 0 11
## R/W/M-S 8 46 6 1 0 0 32 5 4 2 29 6 0 139
## R/Y-LB 3 17 3 1 1 8 6 0 2 0 3 84 1 129
## W/M/W-S 20 15 0 0 2 4 85 1 3 0 9 11 0 150
## Y/LB/Y-S 1 8 3 1 1 4 79 5 4 4 1 0 0 111
## Y/LG-LB 0 0 0 0 0 0 0 0 1 1 0 8 0 10
## Y/R/LB-S 4 6 1 6 4 4 46 4 25 10 1 1 0 112
## (all) 111 263 83 64 32 89 771 227 138 91 266 234 7 2376There’s clearly variation in the ‘popularity’ of feeders, with overall visit counts ranging from 7 to 771. Keep in mind that this could simply be due to different feeders being recorded for different amounts of time. [If we know deployment time for each feeder, we can correct for that]
We can also visualise this as proportions.
#plot a matrix showing proportion of visits to feeders visited by each bird
visit.props<-acast(feeder.visits, Bird_ID ~ Feeder_ID , value.var='freq',
fun.aggregate=sum, margins=TRUE)
# can round if you want using: round(visit.props, digits = 2)
visit.props## A1 A10 A11 A12 A13
## DB/M-W/S 0.224137931 0.034482759 0.103448276 0.034482759 0.051724138
## DB/M/DB-S 0.032967033 0.142857143 0.109890110 0.043956044 0.021978022
## DB/M/Y-S 0.000000000 0.007633588 0.007633588 0.000000000 0.015267176
## DB/Y/LG-S 0.031250000 0.039062500 0.015625000 0.031250000 0.023437500
## Gold/LG-LG 0.200000000 0.080000000 0.080000000 0.000000000 0.000000000
## Gold/W-LB 0.134615385 0.269230769 0.115384615 0.000000000 0.019230769
## LB/DB/LB-LB 0.009708738 0.000000000 0.019417476 0.000000000 0.019417476
## LB/M/LG-S 0.024793388 0.099173554 0.024793388 0.049586777 0.000000000
## LB/O-B 0.393939394 0.060606061 0.000000000 0.030303030 0.000000000
## LB/R/LB-R 0.000000000 0.181818182 0.000000000 0.000000000 0.000000000
## LB/Y-LB 0.000000000 0.450000000 0.150000000 0.250000000 0.000000000
## LG/DB/LG-S 0.045454545 0.210227273 0.039772727 0.000000000 0.005681818
## LG/Gold-LB 0.000000000 0.000000000 0.875000000 0.000000000 0.000000000
## LG/R/LB-S 0.000000000 0.289473684 0.052631579 0.000000000 0.026315789
## LG/R/Y-S 0.028846154 0.000000000 0.009615385 0.000000000 0.019230769
## M/LG/M-S 0.014035088 0.031578947 0.007017544 0.042105263 0.007017544
## M/R/M-W/S 0.020833333 0.041666667 0.041666667 0.000000000 0.000000000
## M/W/DB-S 0.024390244 0.091463415 0.012195122 0.073170732 0.012195122
## O/M-P 0.125000000 0.375000000 0.020833333 0.041666667 0.000000000
## P/LG/P-G 0.000000000 0.000000000 0.000000000 0.090909091 0.272727273
## P/O-LB 0.000000000 0.000000000 1.000000000 0.000000000 0.000000000
## R/DB/LG-S 0.000000000 0.264150943 0.075471698 0.018867925 0.000000000
## R/W-LB 0.000000000 0.272727273 0.090909091 0.454545455 0.000000000
## R/W/M-S 0.057553957 0.330935252 0.043165468 0.007194245 0.000000000
## R/Y-LB 0.023255814 0.131782946 0.023255814 0.007751938 0.007751938
## W/M/W-S 0.133333333 0.100000000 0.000000000 0.000000000 0.013333333
## Y/LB/Y-S 0.009009009 0.072072072 0.027027027 0.009009009 0.009009009
## Y/LG-LB 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
## Y/R/LB-S 0.035714286 0.053571429 0.008928571 0.053571429 0.035714286
## (all) 1.568837632 3.629514455 2.953682479 1.238370361 0.560031962
## A14 A2 A3 A4 A6
## DB/M-W/S 0.120689655 0.137931034 0.017241379 0.034482759 0.189655172
## DB/M/DB-S 0.032967033 0.263736264 0.087912088 0.065934066 0.087912088
## DB/M/Y-S 0.007633588 0.267175573 0.549618321 0.038167939 0.053435115
## DB/Y/LG-S 0.007812500 0.468750000 0.093750000 0.140625000 0.132812500
## Gold/LG-LG 0.000000000 0.000000000 0.000000000 0.160000000 0.160000000
## Gold/W-LB 0.038461538 0.326923077 0.000000000 0.000000000 0.000000000
## LB/DB/LB-LB 0.048543689 0.009708738 0.009708738 0.000000000 0.000000000
## LB/M/LG-S 0.041322314 0.008264463 0.016528926 0.008264463 0.024793388
## LB/O-B 0.181818182 0.242424242 0.000000000 0.000000000 0.000000000
## LB/R/LB-R 0.818181818 0.000000000 0.000000000 0.000000000 0.000000000
## LB/Y-LB 0.000000000 0.150000000 0.000000000 0.000000000 0.000000000
## LG/DB/LG-S 0.000000000 0.130681818 0.022727273 0.017045455 0.022727273
## LG/Gold-LB 0.000000000 0.000000000 0.125000000 0.000000000 0.000000000
## LG/R/LB-S 0.000000000 0.078947368 0.078947368 0.105263158 0.026315789
## LG/R/Y-S 0.000000000 0.086538462 0.788461538 0.000000000 0.038461538
## M/LG/M-S 0.021052632 0.782456140 0.028070175 0.038596491 0.007017544
## M/R/M-W/S 0.000000000 0.020833333 0.020833333 0.645833333 0.208333333
## M/W/DB-S 0.030487805 0.603658537 0.060975610 0.067073171 0.012195122
## O/M-P 0.083333333 0.145833333 0.000000000 0.041666667 0.020833333
## P/LG/P-G 0.636363636 0.000000000 0.000000000 0.000000000 0.000000000
## P/O-LB 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000
## R/DB/LG-S 0.132075472 0.018867925 0.132075472 0.018867925 0.000000000
## R/W-LB 0.090909091 0.000000000 0.000000000 0.000000000 0.000000000
## R/W/M-S 0.000000000 0.230215827 0.035971223 0.028776978 0.014388489
## R/Y-LB 0.062015504 0.046511628 0.000000000 0.015503876 0.000000000
## W/M/W-S 0.026666667 0.566666667 0.006666667 0.020000000 0.000000000
## Y/LB/Y-S 0.036036036 0.711711712 0.045045045 0.036036036 0.036036036
## Y/LG-LB 0.000000000 0.000000000 0.000000000 0.100000000 0.100000000
## Y/R/LB-S 0.035714286 0.410714286 0.035714286 0.223214286 0.089285714
## (all) 2.452084779 5.708550426 2.155247442 1.805351601 1.224202436
## A7 A8 A9 (all)
## DB/M-W/S 0.051724138 0.000000000 0.000000000 1
## DB/M/DB-S 0.021978022 0.087912088 0.000000000 1
## DB/M/Y-S 0.053435115 0.000000000 0.000000000 1
## DB/Y/LG-S 0.000000000 0.015625000 0.000000000 1
## Gold/LG-LG 0.160000000 0.160000000 0.000000000 1
## Gold/W-LB 0.076923077 0.019230769 0.000000000 1
## LB/DB/LB-LB 0.009708738 0.864077670 0.009708738 1
## LB/M/LG-S 0.702479339 0.000000000 0.000000000 1
## LB/O-B 0.030303030 0.030303030 0.030303030 1
## LB/R/LB-R 0.000000000 0.000000000 0.000000000 1
## LB/Y-LB 0.000000000 0.000000000 0.000000000 1
## LG/DB/LG-S 0.437500000 0.062500000 0.005681818 1
## LG/Gold-LB 0.000000000 0.000000000 0.000000000 1
## LG/R/LB-S 0.342105263 0.000000000 0.000000000 1
## LG/R/Y-S 0.028846154 0.000000000 0.000000000 1
## M/LG/M-S 0.007017544 0.010526316 0.003508772 1
## M/R/M-W/S 0.000000000 0.000000000 0.000000000 1
## M/W/DB-S 0.006097561 0.006097561 0.000000000 1
## O/M-P 0.062500000 0.062500000 0.020833333 1
## P/LG/P-G 0.000000000 0.000000000 0.000000000 1
## P/O-LB 0.000000000 0.000000000 0.000000000 1
## R/DB/LG-S 0.320754717 0.000000000 0.018867925 1
## R/W-LB 0.000000000 0.090909091 0.000000000 1
## R/W/M-S 0.208633094 0.043165468 0.000000000 1
## R/Y-LB 0.023255814 0.651162791 0.007751938 1
## W/M/W-S 0.060000000 0.073333333 0.000000000 1
## Y/LB/Y-S 0.009009009 0.000000000 0.000000000 1
## Y/LG-LB 0.000000000 0.800000000 0.000000000 1
## Y/R/LB-S 0.008928571 0.008928571 0.000000000 1
## (all) 2.621199185 2.986271688 0.096655554 29Let’s now create separate matrices for HS and YB birds. First, the HS (captive-bred) birds.
HS.visit.counts<-acast(HS.feeder.visits, Bird_ID ~ Feeder_ID , value.var='n',
fun.aggregate=sum, margins=TRUE)
HS.visit.counts## A1 A10 A11 A12 A13 A14 A2 A3 A4 A6 A7 A8 A9 (all)
## DB/M/DB-S 3 13 10 4 2 3 24 8 6 8 2 8 0 91
## DB/M/Y-S 0 1 1 0 2 1 35 72 5 7 7 0 0 131
## DB/Y/LG-S 4 5 2 4 3 1 60 12 18 17 0 2 0 128
## LB/M/LG-S 3 12 3 6 0 5 1 2 1 3 85 0 0 121
## LG/DB/LG-S 8 37 7 0 1 0 23 4 3 4 77 11 1 176
## LG/R/LB-S 0 11 2 0 1 0 3 3 4 1 13 0 0 38
## LG/R/Y-S 3 0 1 0 2 0 9 82 0 4 3 0 0 104
## M/LG/M-S 4 9 2 12 2 6 223 8 11 2 2 3 1 285
## M/W/DB-S 4 15 2 12 2 5 99 10 11 2 1 1 0 164
## R/DB/LG-S 0 14 4 1 0 7 1 7 1 0 17 0 1 53
## R/W/M-S 8 46 6 1 0 0 32 5 4 2 29 6 0 139
## Y/LB/Y-S 1 8 3 1 1 4 79 5 4 4 1 0 0 111
## Y/R/LB-S 4 6 1 6 4 4 46 4 25 10 1 1 0 112
## (all) 42 177 44 47 20 36 635 222 93 64 238 32 3 1653Then the YB (wild-bred) birds.
YB.visit.counts<-acast(YB.feeder.visits, Bird_ID ~ Feeder_ID , value.var='n',
fun.aggregate=sum, margins=TRUE)
YB.visit.counts## A1 A10 A11 A12 A13 A14 A2 A3 A4 A6 A7 A8 A9 (all)
## DB/M-W/S 13 2 6 2 3 7 8 1 2 11 3 0 0 58
## Gold/LG-LG 5 2 2 0 0 0 0 0 4 4 4 4 0 25
## Gold/W-LB 7 14 6 0 1 2 17 0 0 0 4 1 0 52
## LB/DB/LB-LB 1 0 2 0 2 5 1 1 0 0 1 89 1 103
## LB/O-B 13 2 0 1 0 6 8 0 0 0 1 1 1 33
## LB/R/LB-R 0 2 0 0 0 9 0 0 0 0 0 0 0 11
## LB/Y-LB 0 9 3 5 0 0 3 0 0 0 0 0 0 20
## LG/Gold-LB 0 0 7 0 0 0 0 1 0 0 0 0 0 8
## M/R/M-W/S 1 2 2 0 0 0 1 1 31 10 0 0 0 48
## O/M-P 6 18 1 2 0 4 7 0 2 1 3 3 1 48
## P/LG/P-G 0 0 0 1 3 7 0 0 0 0 0 0 0 11
## P/O-LB 0 0 6 0 0 0 0 0 0 0 0 0 0 6
## R/W-LB 0 3 1 5 0 1 0 0 0 0 0 1 0 11
## R/Y-LB 3 17 3 1 1 8 6 0 2 0 3 84 1 129
## W/M/W-S 20 15 0 0 2 4 85 1 3 0 9 11 0 150
## Y/LG-LB 0 0 0 0 0 0 0 0 1 1 0 8 0 10
## (all) 69 86 39 17 12 53 136 5 45 27 28 202 4 723Interesting to note that all feeders were visited by both captive-bred (HS) and wild-bred (YB) individuals.
Comparing these matrices suggests that the 13 captive-bred (HS) individuals are using the feeders much more frequently than the 16 wild-bred (YB) birds (1,653 visits by 13 birds versus 723 visits by 16 birds).
Once we’ve done this we can do an ANOVA to look at the the mean number of visits per for each group.
First, number of feeders used.
## Df Sum Sq Mean Sq F value Pr(>F)
## Bird_origin 1 118.9 118.93 14.7 0.000686 ***
## Residuals 27 218.5 8.09
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Tables of means
## Grand mean
##
## 8.137931
##
## Bird_origin
## HS YB
## 10.4 6.31
## rep 13.0 16.00
The captive-bred (HS) birds used 10.4 feeders on average, compared to 6.3 for the wild-bred (YB) birds.
Does this differ by sex?
## Df Sum Sq Mean Sq F value Pr(>F)
## Bird_origin 1 118.93 118.93 14.350 0.000852 ***
## Sex 1 11.31 11.31 1.365 0.253667
## Bird_origin:Sex 1 0.00 0.00 0.000 1.000000
## Residuals 25 207.20 8.29
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Tables of means
## Grand mean
##
## 8.137931
##
## Bird_origin
## HS YB
## 10.4 6.31
## rep 13.0 16.00
##
## Sex
## F M
## 7.5 8.66
## rep 13.0 16.00
##
## Bird_origin:Sex
## Sex
## Bird_origin F M
## HS 9.33 10.70
## rep 3.00 10.00
## YB 5.80 7.17
## rep 10.00 6.00No significant effect of Sex. Plot the data.
What about the overall number of visits to feeders?
## Df Sum Sq Mean Sq F value Pr(>F)
## Bird_origin 1 48188 48188 17.33 0.000287 ***
## Residuals 27 75066 2780
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Tables of means
## Grand mean
##
## 81.93103
##
## Bird_origin
## HS YB
## 127 45.2
## rep 13 16.0
Around 127 feeder visits on average per captive-bred (HS) bird, compared to 45 per wild-bred (YB) bird.
BUT not all birds stayed at the site for the whole period, so we need to correct the visit tallies and express them as visit rates (visits divided by the number of recording days the bird was present at the site).
Let’s try to understand differences in site persistence by birds of different origins better.
Plot the presence of different individuals visually, using origin as a colour differentiator.
Looks like the captive-bred (Healesville) birds generally persisted onsite for longer than the Yellingbo birds. 3 Healesville birds disappeared shortly after (<two months) release, while 9 Yellingbo birds did. At last check there were still 9 Healesville birds definitely on site but only 2 Yellingbo birds.
Compare average site tenancy for birds from the two natal sites
## Df Sum Sq Mean Sq F value Pr(>F)
## Natal_site 1 1225638 1225638 9.436 0.0045 **
## Residuals 30 3896654 129888
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Tables of means
## Grand mean
##
## 482.875
##
## Natal_site
## Healesville Yellingbo
## 705 310
## rep 14 18Healesville birds had a mean tenancy of 705 days compared to 310 for Yellingbo birds. This is a problem because we might have more feeder visits by captive-bred birds simply because more of them were around.
Keep in mind however that the feeding data are from a relatively short window in 2021. Plot ‘decay’ lines for birds from the two origins over the 38 dates over four months that feeder recordings were taken (16 August 2021 to 22 December 2021).
About half of the 18 YB birds that were introduced disappeared within a month or so. Fewer HS birds died or left the site (2 of the original 13).
11 HS birds and 9 YB birds were present for all 38 feeder recording days. We can look at their feeding rates and use of feeders over the recording period without having to worry about correcting for presence. First, let’s look at feeder visitation rates over time for individuals from the two groups.
Bit of a messy forest of lines. Plot means with error bars by origin to get better clarity.
No real indication from this that there are temporal differences in how often the feeders were accessed.
Go back to the full dataset and calculate a feeder visit rate (feeder visits per day per bird) for each recording day for birds from each origin group, taking into account the number of individuals that were known to be present from each origin group on that recording day.
## Df Sum Sq Mean Sq F value Pr(>F)
## Bird_origin 1 19.46 19.464 12.71 0.00144 **
## Residuals 26 39.83 1.532
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Tables of means
## Grand mean
##
## 2.652864
##
## Bird_origin
## HS YB
## 3.55 1.88
## rep 13.00 15.00Looks like after this correction the Healesville birds are still visiting the feeders at a higher rate than the Yellingbo birds, and the difference is statistically significant.
Does this differ by sex?
## Df Sum Sq Mean Sq F value Pr(>F)
## Bird_origin 1 19.46 19.464 12.493 0.00169 **
## Sex 1 2.35 2.346 1.506 0.23167
## Bird_origin:Sex 1 0.09 0.087 0.056 0.81562
## Residuals 24 37.39 1.558
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Tables of means
## Grand mean
##
## 2.652864
##
## Bird_origin
## HS YB
## 3.55 1.88
## rep 13.00 15.00
##
## Sex
## F M
## 2.34 2.89
## rep 12.00 16.00
##
## Bird_origin:Sex
## Sex
## Bird_origin F M
## HS 2.95 3.73
## rep 3.00 10.00
## YB 1.66 2.20
## rep 9.00 6.00No significant effect of Sex. Plot the data.
No difference.
Overall, the data suggest more frequent visitation to feeders and greater variety of feeders used by HS (captive-bred) birds. This assumes there are no systematic differences between HS and YB birds in terms of how long they were present at the site. We now draw on another datafile to explore how long birds stayed at the site.
Plotting spatial use of feeders
Let’s explore how HS and YB birds used different feeders and see if there are broad or individual differences in patterns of feeder use. We first create two facet plots side by side that allow us to visually compare overall feeder use. We’ll display the spatial arrangement of the feeders by plotting them according to lat/lon, and create a bubble chart where the size of the bubble represents the number of visits to that feeder.
Another way of displaying the data is to segment each bubble into pie pieces that represent visits by individuals from the two source populations.
Healesville birds doing the lion’s share of feeding at most feeders,
except for A08.
We can also show this pattern for individual birds, sorted by origin. But it’s a bit of a mess.
#OTHER METHOD
feeder.birds<-read.csv(“data/feeder.birds.csv”) p <- ggplot() + geom_scatterpie(data=feeder.birds, aes(x=lon, y=lat, group=feeder, r=(total)0.1), cols=LETTERS[1:26], color=NA) + coord_equal() p + geom_scatterpie_legend(feeder.birds$total0.03, x=-145.78, y=-37.72) ```
#pie_bubbles<-function(xpos,ypos,radii,sectors, sector_col=NULL,main=““,xlab=”“,ylab=”“) { xlim<-c(min(xpos-radii),max(xpos+radii)) ylim<-c(min(ypos-radii),max(ypos+radii)) nbubbles<-length(xpos) if(is.null(sector_col)) { sector_col<-list() for(scol in 1:nbubbles) sector_col[[scol]]<-rainbow(length(sectors[[scol]])) } plot(0,xlim=xlim,ylim=ylim,type=”n”, main=main,xlab=xlab,ylab=ylab) for(bubble in 1:nbubbles) floating.pie(xpos=xpos[bubble],ypos=ypos[bubble], x=sectors[[bubble]],radius=radii[bubble], col=sector_col[[bubble]])
#library(plotrix) #pie_bubbles(lat,ypos,radii,sectors,main=“Pie bubbles”)