-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy path1_Review_NF_Estuaries_LMH77.qmd
316 lines (271 loc) · 13.7 KB
/
1_Review_NF_Estuaries_LMH77.qmd
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
---
title: "Review of LMH 77 Skunkcabbage Forests"
author: "William H MacKenzie"
date: "20/09/2024"
format:
typst:
toc: true
toc-depth: 2
toc-title: Contents
section-numbering: 1.1.1
columns: 1
editor: source
execute:
echo: false
error: false
warning: false
message: false
fig.width: 6
fig.height: 4
fig.align: 'left'
fig.cap: true
fig.pos: H
out.width: '100%'
dev: pdf
fig.ext: pdf
cache: false
fig.retina: 2
dpi: 600
fig.asp: 1.5
fig.path: "./figures/"
---
This script is designed to review the site series within each BGC, primarily to identify where site series do not sufficiently differentiate and need to be reviewed. The script also identifies site series with too few plots or with low diagnostic potential for review and interpretation of the quantitative analysis. The script generates a table list of site unit pairs that exceed a threshold similarity and a dendrogram of the cluster analysis of site units for each BGC.
# Historic BEC correlation approach
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
require(tidyverse)
require(DBI)
require(data.table)
require(cluster)
require(dendextend)
require(dynamicTreeCut)
require(gtable)
require(gtsummary)
require(colorspace)
require(openxlsx)
require(tictoc)
require(labdsv)
require(indicspecies)
require(Polychrome)
require(ggdendro)
require(purrr)
require(ggplotify)
require(grid)
set.seed(1279)
source("./_functions/_bec_dist.R")
source("./_functions/_bec_dist_matrix.R")
source("./_functions/_lump_species.R")
source("./_functions/_lump_species2.R")
source("./_functions/_create_su_vegdata.R")
source("./_functions/_create_analysis_vegsum.R")
source("./_functions/_TabletoTree.R")
source("./_functions/_TreetoTable.R")
source("./_functions/_add_vars.R")
source("./_functions/_do_pairwise.R")
source("./_functions/_create_diagnostic_veg.R")
source("./_functions/_return_similar_pairs.R")
source("./_functions/_read_sppmaster.R")
source("./_functions/_combined_su.R")
source("./_functions/_create_veg_sum_all.R")
#source('./_functions/_create_veg_sum2.R')
source('./_functions/_build_species_ordering.R')
source('./_functions/_format_veg_table.R')
source('./_functions/_format_veg_table2.R')
source('./_functions/_encode_veg_sum.R')
source('./_functions/_create_dendro.R')
source('./_functions/_create_dendro_all.R')
source('./_functions/_create_dendro_bybgc.R')
source('./_functions/_draw_ss_edatope.R')
source('./_functions/_create_VGS_table.R')
```
# Evaluate site series within each BGC
## Read in data
Vegetation data is read in from saved .RDS file generated from the BECMaster cleaning scripts. A compiled SU table is build from all BGC \_SU tables stored in the coast guide Vpro database. Taxonomy is read in from the species taxonomy database. A species lumping code table is read in from the Correlation2_Spp_lump.accdb database and the vegetation data is lumped using the lump_species function.
```{r load data, echo=FALSE, message=FALSE}
veg.dat <- readRDS("./clean_data/Analysis_BECMaster_Veg.rds") ### named veg.dat
taxon.all <- read_sppmaster()
taxon.lifeform <- taxon.all %>%
filter(Codetype == "U" | Codetype == "X" | Codetype == "D") %>%
dplyr::select(Code, ScientificName, EnglishName, Lifeform) %>%
distinct()
veglump <- dbConnect(
odbc::odbc(),
.connection_string = "Driver={Microsoft Access Driver (*.mdb, *.accdb)}; DBQ=D:/BC_Correlation2_Vpro_2023/Correlation2_Spp_lump.accdb;")
lump <- dbReadTable(veglump, "CorrelationII_Lump")
dbDisconnect(veglump)
veg.dat2 <- lump_species(vegdata = veg.dat, lump, use.subtaxa = FALSE)
db <- "D:/BC_Correlation2_Vpro_2023/Coast_NonForest_SUnew.accdb"
master_su <- dbConnect(odbc::odbc(), .connection_string = paste0("Driver={Microsoft Access Driver (*.mdb, *.accdb)}; DBQ=", db,";"))
su <- dbReadTable(master_su, "Coast_Estuary_v1_SU")
dbDisconnect(master_su)
SU <- su %>% mutate(bgc = substr(SiteUnit,1,9)) %>% drop_na() %>% distinct(PlotNumber, .keep_all = TRUE) %>%
arrange(desc(PlotNumber)) %>% mutate(bgc = gsub(" ", "", bgc, fixed = TRUE))
ss.unique <- SU %>% select(SiteUnit) %>% distinct
su <- SU %>% dplyr::select(PlotNumber, SiteUnit, bgc)
su$SiteUnit.orig <- su$SiteUnit
su$SiteUnit <- sub(".*/", "", su$SiteUnit)
# su <- su %>% mutate(SiteUnit = gsub("/", "_", su$SiteUnit))
# su <- su %>% mutate(SiteUnit = gsub(" ", "", su$SiteUnit, fixed = TRUE))
#su <- combined_su(db)
# su <- su %>%
# filter(grepl('_[[:alpha:]]', SiteUnit))
#su$SiteUnit <- gsub(".*_","",su$SiteUnit) ## extracts only the site association code
su <- su %>%
filter(!grepl('poor|low|-S|add|nudum|browsed|X|!|support|Unplaced', SiteUnit)) %>% select(-SiteUnit.orig) %>% arrange(SiteUnit)
# filter(grepl('01', SiteUnit)) ###zonal specific
# su <- su %>%
# filter(!bgc %in% c('CWHvh3', 'CWHwh1', 'CWHwh2', 'CWHvh3', 'MHwh'))###BGC specific|CWHms
# su <- su %>%
# filter(bgc %in% c('CWHvh1', 'CWHvh2', 'CWHvm1', 'CWHvm2', 'CWHvm3', 'CWHvm4', 'CWHwm'))###BGC specific|CWHms
#filter(bgc %in% c('CWHmm1'))
bgc.unique <- unique(su$bgc)
ss.unique <- su %>% select(SiteUnit, bgc) %>% distinct
becmaster <- dbConnect(odbc::odbc(), .connection_string = "Driver={Microsoft Access Driver (*.mdb, *.accdb)}; DBQ=D:/BECMaster/BECMaster_fixing.accdb;")
plot.env <- dbReadTable(becmaster, "BECMaster_fixing_Env")
dbDisconnect(becmaster)
```
## Examine site series within each BGC
1. Identify site series that have fewer than 5 plots (difficult to quantitatively analyse)
2. Identify site series that have low diagnostic potential (review units for membership consistency).
```{r build pair.wise similarity matrix, echo=FALSE}
key.site.indicators <- c("PICESIT", "POPUTRI", "LYSIAME", "GAULSHA", "OPLOHOR", "ELLIPYR","ATHYFIL", "RUBUSPE", "EQUIARV", "GYMNDRY", "VALESIT", "CASSMER", "LUETPEC", "POLYMUN", "STRUSPI", "RHODGRO", "EMPENIG")#, "TIARELLA", "DRYOEXP" )
reduced.exceptions <- c("SPHAGNUM", "CLADONIA", "CLADINA", "RACOMITR", "MNIUM")
# reduced.lifeforms = c(1,2)
reduced.lifeforms <- c(9, 10, 11)
### select units to run
su2 <- su
vegsum.pairs <- do_pairwise(veg.dat2,
su = su2, minimportance = 0, minconstancy = 60,
noiseconstancy = 10,
minplots = 1,
minor = 1,
use.ksi = TRUE, ksi = key.site.indicators, ksi.value = 1.5,
reduce.lifeform = TRUE, reduced.lifeforms = reduced.lifeforms, reduction = .1,
reduced.exceptions = reduced.exceptions
)
xx <- vegsum.pairs %>%
filter(Unit1 == "CWHvm1_103", Unit2 == "CWHvm1_103->111.2")
unit.compare <- vegsum.pairs %>%filter(nplots.x>0) %>%
select(Unit1, Unit2, BEC.sim.min, nplots.x, nplots.y, unit.diag.sum.x, unit.diag.sum.y) %>%
mutate(BEC.sim = round(BEC.sim.min, 2)) %>%
distinct()
# yy <- vegsum.pairs %>%
# select(Unit1, Unit2, Species, diagnostic.potential.x, diagnostic.potential.y, shared.diag, diff.pts.x, diff.pts.y, sum.shared.diag,diff.tot.x, diff.tot.y, diff.ratio.x, diff.ratio.y, diff.ratio,diff.ratio.mean, BEC.sim) %>%
# filter(Unit1 == "CWHvm1_111.1", Unit2 == "CWHvm1_101->111.1")
```
```{r site series with too few plots, echo=FALSE}
#| label: tab-too-few-plots
#| tbl-cap: "Site Units with Fewer than 5 Plots"
#| tbl-cap-location: top
#| warning: false
#| tbl-align: "left"
compared <- unit.compare %>% distinct
ss_too.few1 <- compared %>%
select(Unit1, nplots.x) %>%
filter(nplots.x < 5, nplots.x>0) %>%
rename("Number of Plots" = nplots.x, "Site Unit" = Unit1) %>%
distinct()
ss_too.few2 <- compared %>%
select(Unit2, nplots.y) %>%
filter(nplots.y < 5, nplots.y>0) %>%
rename("Number of Plots" = nplots.y, "Site Unit" = Unit2) %>%
distinct()
ss_too.few <- rbind(ss_too.few1, ss_too.few2) %>% distinct() %>% arrange(`Site Unit`)
# low.num <- gt::as_gtable(gt::gt(ss_too.few) %>% gt::fmt_number(decimals = 0)|> gt::tab_options(table.font.size = 10), plot = TRUE)
gt::gt(ss_too.few) %>% gt::fmt_number(decimals = 0)|> gt::tab_options(table.font.size = 10)
```
```{r site series with low diagnostic potential, echo=FALSE}
#| label: tab-low-diagnostic
#| tbl-cap: "Site Units with Low Diagnostic Potential"
#| tbl-cap-location: top
#| warning: false
#| tbl-align: "left"
ss_low.diag1 <- compared %>%
select(Unit1, unit.diag.sum.x) %>% distinct() %>%
filter(unit.diag.sum.x < 30, unit.diag.sum.x >0) %>%
rename("Diagnostic Potential" = unit.diag.sum.x, "Site Unit" = Unit1) %>%
distinct()
ss_low.diag2 <- compared %>%
select(Unit2, unit.diag.sum.y) %>%
filter(unit.diag.sum.y <30, unit.diag.sum.y >0) %>%
rename("Diagnostic Potential" = unit.diag.sum.y, "Site Unit" = Unit2) %>%
distinct()
ss_low.diag <- rbind(ss_low.diag1, ss_low.diag2) %>% distinct()
# low.diag.pot <- gt::as_gtable(gt::gt(ss_low.diag) %>% gt::fmt_number(decimals = 2)|> gt::tab_options(table.font.size = 10), plot = TRUE)
gt::gt(ss_low.diag) %>% gt::fmt_number(decimals = 2)|> gt::tab_options(table.font.size = 10)
# require(gridExtra)
# grid.arrange(low.num, low.diag.pot, ncol = 2)
```
```{r site units that are too similar, echo=FALSE}
#| label: tab-sites-similar
#| tbl-cap: paste0("Site Series pairs with poor differentiation (BEC.sim >= .93)")
#| tbl-cap-location: top
#| warning: false
#| tbl-align: "left"
ss_similar <- compared %>%
select(Unit1, Unit2, BEC.sim) %>%
dplyr::filter(BEC.sim >= .93) %>% mutate(Units = paste0(Unit1, " vs ", Unit2)) %>%
select(-Unit2, -Unit1) %>%
rename("Similarity" = BEC.sim, "Site Units" = Units) %>%
distinct()
# low.diff <- gt::as_gtable((gt::gt(ss_similar) |> gt::fmt_number(decimals = 2)|> gt::tab_options(table.font.size = 10) |> gt::cols_width(Site.Units ~ gt::px(250))), plot = TRUE, text_grob = gridtext::richtext_grob)
gt::gt(ss_similar) |> gt::fmt_number(decimals = 2)|> gt::tab_options(table.font.size = 10)
```
# Compare site series within each BGCs
This section is to identify site series that do not differentiate adequately and require review.
## Dendrogram of cluster analysis by BGC
The dendrogram is an approximate representation of the similarity matrix. It is constructed using agglomerative hierarchical clustering which merges site units from the bottom up. The red line represents the minimal dissimilarity required to separate units into different site series. Any splits to the right of the red line should be considered for merging into the same site series (possibly as phases). Current threshold is set at 7% but assessment is required. Splits that occur between the green association threshold and the red minimum threshold will likely be merged at the subassociation level in the hierarchy. The green line represents the dissimilarity threshold for an association. Site unit 'leaves' to the right of this threshold will fall under the same association in the hierarchy. Current threshold is set at 17% but assessment is required.
```{r cluster analysis, echo=FALSE}
#bgc.unique <- c("CDFmm", "CWHvm3")
#for (bgc.choose in bgc.unique){
create_dendro_all(unit.compare, threshold.low = .07)
#}
```
## Generate comparative veg summary report
```{r report table}
# su2 <- su %>% mutate(bgc = str_extract(SiteUnit, "[^_]+"))
unit.list <- unique(su$SiteUnit)
unit.choose <- c("Em01","Em02","Em03","Em04","Em05","Em06", "Em07")
su.choose <- su %>% filter(SiteUnit %in% unit.choose)
table.name1 <- "Estuary1"
vegSum <- create_veg_sum_all(vdat = veg.dat2, siteUnits = su.choose, minimportance = 0, minconstancy = 50, noiseconstancy = 25)
##determine order of species by unit
#indic.order <- build_species_ordering_all(vdat = veg.dat2, vsum = vegSum, code.lump=lump, siteUnits = su.choose)
veg.sum.table1 <- format_veg_table2(vsum = vegSum, spp = taxon.lifeform)
create_VGS_table(veg.sum.table1, table.name = table.name1)
unit.choose <- c("Ed01", "Ed02", "Ed03", "Ed04 Heramax", "Es01", "Es02")
su.choose <- su %>% filter(SiteUnit %in% unit.choose)
table.name2 <- "Estuary2"
vegSum <- create_veg_sum_all(vdat = veg.dat2, siteUnits = su.choose, minimportance = 0, minconstancy = 50, noiseconstancy = 25)
##determine order of species by unit
indic.order <- build_species_ordering_all(vdat = veg.dat2, vsum = vegSum, code.lump=lump, siteUnits = su.choose)
veg.sum.table2 <- format_veg_table2(vsum = vegSum, spp = taxon.lifeform)
create_VGS_table(veg.sum.table2, table.name = table.name2)
# unit.choose <- c("Fl50","Heramax","Malufus","SCHOAME","Wm02.2", "Es01.2","Es03" )
# su.choose <- su %>% filter(SiteUnit %in% unit.choose)
# table.name3 <- "Beach3"
# vegSum <- create_veg_sum_all(vdat = veg.dat2, siteUnits = su.choose, minimportance = 0)
# ##determine order of species by unit
# indic.order <- build_species_ordering_all(vdat = veg.dat2, vsum = vegSum, code.lump=lump, siteUnits = su.choose)
# veg.sum.table3 <- format_veg_table2(vsum = vegSum, spp = taxon.lifeform)
# create_VGS_table(veg.sum.table3, table.name = table.name3)
vegsum.wbk <- createWorkbook()
openxlsx::addWorksheet(vegsum.wbk, sheetName = table.name1)
openxlsx::writeData(vegsum.wbk, sheet = table.name1, veg.sum.table1)
openxlsx::addWorksheet(vegsum.wbk, sheetName = table.name2)
openxlsx::writeData(vegsum.wbk, sheet = table.name2, veg.sum.table2)
# openxlsx::addWorksheet(vegsum.wbk, sheetName = table.name3)
# openxlsx::writeData(vegsum.wbk, sheet = table.name3, veg.sum.table3)
saveWorkbook(vegsum.wbk, "./vegsum.tables/LMH77_draft_estuary_table.xlsx", overwrite = TRUE)
```
```{r table of plots site unit by BGC}
#| label: tab-sites-bgc
#| tbl-cap: "Count of Site Units by BGC"
#| tbl-cap-location: top
#| warning: false
#| tbl-align: "left"
# ss_x_bgc <- su %>% select(SiteUnit, bgc) %>% group_by(SiteUnit, bgc) %>% summarise(n = n()) %>% pivot_wider(names_from = SiteUnit, values_from = n, values_fill = 0) %>% ungroup() %>% mutate_if(is.numeric, as.character) %>% arrange(bgc)
# ss_x_bgc[ss_x_bgc == 0] <- "-"
# # low.diff <- gt::as_gtable((gt::gt(ss_similar) |> gt::fmt_number(decimals = 2)|> gt::tab_options(table.font.size = 10) |> gt::cols_width(Site.Units ~ gt::px(250))), plot = TRUE, text_grob = gridtext::richtext_grob)
# gt::gt(ss_x_bgc) |> gt::fmt_number(decimals = 2)|> gt::tab_options(table.font.size = 10)
```