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History_of_collection_activities.R
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# Map history of vascular plant surveys in Átl’ka7tsem
# Load libraries
library(dplyr)
library(gapminder)
library(gganimate)
library(ggplot2)
library(ggthemes)
library(gifski)
library(hrbrthemes)
library(leaflet)
library(raster)
library(sf)
library(tidyr)
library(jsonlite)
library(viridis)
library(plotly)
# Source dependencies
source("scripts/utils.R")
# Analysis of historical collection activities
plants <- read.csv("tabular_data/Howe_Sound_vascular_plant_records_consolidated.csv")
plants$yearRecorded <- as.numeric(substr(plants$eventDate, 1, 4))
plants <- plants %>% drop_na(yearRecorded)
min(plants$yearRecorded) # Earliest record dates to 1897
plants.1890.1900 <- plants %>% filter(between(yearRecorded,1890,1900))
plants.1890.1910 <- plants %>% filter(between(yearRecorded,1890,1910))
plants.1890.1920 <- plants %>% filter(between(yearRecorded,1890,1920))
plants.1890.1930 <- plants %>% filter(between(yearRecorded,1890,1930))
plants.1890.1940 <- plants %>% filter(between(yearRecorded,1890,1940))
plants.1890.1950 <- plants %>% filter(between(yearRecorded,1890,1950))
plants.1890.1960 <- plants %>% filter(between(yearRecorded,1890,1960))
plants.1890.1970 <- plants %>% filter(between(yearRecorded,1890,1970))
plants.1890.1980 <- plants %>% filter(between(yearRecorded,1890,1980))
plants.1890.1990 <- plants %>% filter(between(yearRecorded,1890,1990))
plants.1890.2000 <- plants %>% filter(between(yearRecorded,1890,2000))
plants.1890.2010 <- plants %>% filter(between(yearRecorded,1890,2010))
plants.1890.2020 <- plants %>% filter(between(yearRecorded,1890,2020))
plants.1890.2022 <- plants %>% filter(between(yearRecorded,1890,2022))
y.1900 <- 1900
y.1900.cum.obs <- nrow(plants.1890.1900) # cumulative no. observations
y.1900.cum.spp <- length(unique(plants.1890.1900$scientificName)) # cumulative no. species
y.1900.cum.ind <- length(unique(plants.1890.1900$recordedBy)) # cumulative observers
y.1900.obs <- nrow(plants.1890.1900) # no. observations
y.1900.spp <- length(unique(plants.1890.1900$scientificName)) # no. species
y.1900.ind <- length(unique(plants.1890.1900$recordedBy)) # no. observers
y.1910 <- 1910
y.1910.cum.obs <- nrow(plants.1890.1910) # cumulative no. observations
y.1910.cum.spp <- length(unique(plants.1890.1910$scientificName)) # cumulative no. species
y.1910.cum.ind <- length(unique(plants.1890.1910$recordedBy)) # cumulative observers
y.1910.obs <- nrow(plants.1890.1910) - nrow(plants.1890.1900) # no. obs
y.1910.spp <- y.1910.cum.spp - y.1900.cum.spp # no. species
y.1910.ind <- y.1910.cum.ind - y.1900.cum.ind # no. observers
y.1920 <- 1920
y.1920.cum.obs <- nrow(plants.1890.1920) # cumulative no. observations
y.1920.cum.spp <- length(unique(plants.1890.1920$scientificName)) # cumulative no. species
y.1920.cum.ind <- length(unique(plants.1890.1920$recordedBy)) # cumulative observers
y.1920.obs <- nrow(plants.1890.1920) - nrow(plants.1890.1910) # no. obs
y.1920.spp <- y.1920.cum.spp - y.1910.cum.spp # no. species
y.1920.ind <- y.1920.cum.ind - y.1910.cum.ind # no. observers
y.1930 <- 1930
y.1930.cum.obs <- nrow(plants.1890.1930) # cumulative no. observations
y.1930.cum.spp <- length(unique(plants.1890.1930$scientificName)) # cumulative no. species
y.1930.cum.ind <- length(unique(plants.1890.1930$recordedBy)) # cumulative observers
y.1930.obs <- nrow(plants.1890.1930) - nrow(plants.1890.1920) # no. obs
y.1930.spp <- y.1930.cum.spp - y.1920.cum.spp # no. species
y.1930.ind <- y.1930.cum.ind - y.1920.cum.ind # no. observers
y.1940 <- 1940
y.1940.cum.obs <- nrow(plants.1890.1940) # cumulative no. observations
y.1940.cum.spp <- length(unique(plants.1890.1940$scientificName)) # cumulative no. species
y.1940.cum.ind <- length(unique(plants.1890.1940$recordedBy)) # cumulative observers
y.1940.obs <- nrow(plants.1890.1940) - nrow(plants.1890.1930) # no. obs
y.1940.spp <- y.1940.cum.spp - y.1930.cum.spp # no. species
y.1940.ind <- y.1940.cum.ind - y.1930.cum.ind # no. observers
y.1950 <- 1950
y.1950.cum.obs <- nrow(plants.1890.1950) # cumulative no. observations
y.1950.cum.spp <- length(unique(plants.1890.1950$scientificName)) # cumulative no. species
y.1950.cum.ind <- length(unique(plants.1890.1950$recordedBy)) # cumulative observers
y.1950.obs <- nrow(plants.1890.1950) - nrow(plants.1890.1940) # no. obs
y.1950.spp <- y.1950.cum.spp - y.1940.cum.spp # no. species
y.1950.ind <- y.1950.cum.ind - y.1940.cum.ind # no. observers
y.1960 <- 1960
y.1960.cum.obs <- nrow(plants.1890.1960) # cumulative no. observations
y.1960.cum.spp <- length(unique(plants.1890.1960$scientificName)) # cumulative no. species
y.1960.cum.ind <- length(unique(plants.1890.1960$recordedBy)) # cumulative observers
y.1960.obs <- nrow(plants.1890.1960) - nrow(plants.1890.1950) # no. obs
y.1960.spp <- y.1960.cum.spp - y.1950.cum.spp # no. species
y.1960.ind <- y.1960.cum.ind - y.1950.cum.ind # no. observers
y.1970 <- 1970
y.1970.cum.obs <- nrow(plants.1890.1970) # cumulative no. observations
y.1970.cum.spp <- length(unique(plants.1890.1970$scientificName)) # cumulative no. species
y.1970.cum.ind <- length(unique(plants.1890.1970$recordedBy)) # cumulative observers
y.1970.obs <- nrow(plants.1890.1970) - nrow(plants.1890.1960) # no. obs
y.1970.spp <- y.1970.cum.spp - y.1960.cum.spp # no. species
y.1970.ind <- y.1970.cum.ind - y.1960.cum.ind # no. observers
y.1980 <- 1980
y.1980.cum.obs <- nrow(plants.1890.1980) # cumulative no. observations
y.1980.cum.spp <- length(unique(plants.1890.1980$scientificName)) # cumulative no. species
y.1980.cum.ind <- length(unique(plants.1890.1980$recordedBy)) # cumulative observers
y.1980.obs <- nrow(plants.1890.1980) - nrow(plants.1890.1970) # no. obs
y.1980.spp <- y.1980.cum.spp - y.1970.cum.spp # no. species
y.1980.ind <- y.1980.cum.ind - y.1970.cum.ind # no. observers
y.1990 <- 1990
y.1990.cum.obs <- nrow(plants.1890.1990) # cumulative no. observations
y.1990.cum.spp <- length(unique(plants.1890.1990$scientificName)) # cumulative no. species
y.1990.cum.ind <- length(unique(plants.1890.1990$recordedBy)) # cumulative observers
y.1990.obs <- nrow(plants.1890.1990) - nrow(plants.1890.1980) # no. obs
y.1990.spp <- y.1990.cum.spp - y.1980.cum.spp # no. species
y.1990.ind <- y.1990.cum.ind - y.1980.cum.ind # no. observers
y.2000 <- 2000
y.2000.cum.obs <- nrow(plants.1890.2000) # cumulative no. observations
y.2000.cum.spp <- length(unique(plants.1890.2000$scientificName)) # cumulative no. species
y.2000.cum.ind <- length(unique(plants.1890.2000$recordedBy)) # cumulative observers
y.2000.obs <- nrow(plants.1890.2000) - nrow(plants.1890.1990) # no. obs
y.2000.spp <- y.2000.cum.spp - y.1990.cum.spp # no. species
y.2000.ind <- y.2000.cum.ind - y.1990.cum.ind # no. observers
y.2010 <- 2010
y.2010.cum.obs <- nrow(plants.1890.2010) # cumulative no. observations
y.2010.cum.spp <- length(unique(plants.1890.2010$scientificName)) # cumulative no. species
y.2010.cum.ind <- length(unique(plants.1890.2010$recordedBy)) # cumulative observers
y.2010.obs <- nrow(plants.1890.2010) - nrow(plants.1890.2000) # no. obs
y.2010.spp <- y.2010.cum.spp - y.2000.cum.spp # no. species
y.2010.ind <- y.2010.cum.ind - y.2000.cum.ind # no. observers
y.2020 <- 2020
y.2020.cum.obs <- nrow(plants.1890.2020) # cumulative no. observations
y.2020.cum.spp <- length(unique(plants.1890.2020$scientificName)) # cumulative no. species
y.2020.cum.ind <- length(unique(plants.1890.2020$recordedBy)) # cumulative observers
y.2020.obs <- nrow(plants.1890.2020) - nrow(plants.1890.2010) # no. obs
y.2020.spp <- y.2020.cum.spp - y.2010.cum.spp # no. species
y.2020.ind <- y.2020.cum.ind - y.2010.cum.ind # no. observers
y.2022 <- 2022
y.2022.cum.obs <- nrow(plants.1890.2022) # cumulative no. observations
y.2022.cum.spp <- length(unique(plants.1890.2022$scientificName)) # cumulative no. species
y.2022.cum.ind <- length(unique(plants.1890.2022$recordedBy)) # cumulative observers
y.2022.obs <- nrow(plants.1890.2022) - nrow(plants.1890.2020) # no. obs
y.2022.spp <- y.2022.cum.spp - y.2020.cum.spp # no. species
y.2022.ind <- y.2022.cum.ind - y.2020.cum.ind # no. observers
year <- c(y.1900,y.1910,y.1920,y.1930,y.1940,y.1950,y.1960,y.1970,y.1980,y.1990,y.2000,y.2010,y.2020,y.2022)
obs <- c(y.1900.obs,y.1910.obs,y.1920.obs,y.1930.obs,y.1940.obs,y.1950.obs,y.1960.obs,y.1970.obs,y.1980.obs,y.1990.obs,y.2000.obs,y.2010.obs,y.2020.obs,y.2022.obs)
spp <- c(y.1900.spp,y.1910.spp,y.1920.spp,y.1930.spp,y.1940.spp,y.1950.spp,y.1960.spp,y.1970.spp,y.1980.spp,y.1990.spp,y.2000.spp,y.2010.spp,y.2020.spp,y.2022.spp)
ind <- c(y.1900.ind,y.1910.ind,y.1920.ind,y.1930.ind,y.1940.ind,y.1950.ind,y.1960.ind,y.1970.ind,y.1980.ind,y.1990.ind,y.2000.ind,y.2010.ind,y.2020.ind,y.2022.ind)
cum.obs <- c(y.1900.cum.obs,y.1910.cum.obs,y.1920.cum.obs,y.1930.cum.obs,y.1940.cum.obs,y.1950.cum.obs,y.1960.cum.obs,y.1970.cum.obs,y.1980.cum.obs,y.1990.cum.obs,y.2000.cum.obs,y.2010.cum.obs,y.2020.cum.obs,y.2022.cum.obs)
cum.spp <- c(y.1900.cum.spp,y.1910.cum.spp,y.1920.cum.spp,y.1930.cum.spp,y.1940.cum.spp,y.1950.cum.spp,y.1960.cum.spp,y.1970.cum.spp,y.1980.cum.spp,y.1990.cum.spp,y.2000.cum.spp,y.2010.cum.spp,y.2020.cum.spp,y.2022.cum.spp)
cum.ind <- c(y.1900.cum.ind,y.1910.cum.ind,y.1920.cum.ind,y.1930.cum.ind,y.1940.cum.ind,y.1950.cum.ind,y.1960.cum.ind,y.1970.cum.ind,y.1980.cum.ind,y.1990.cum.ind,y.2000.cum.ind,y.2010.cum.ind,y.2020.cum.ind,y.2022.cum.ind)
history <- data.frame(year,obs,spp,ind,cum.obs,cum.spp,cum.ind)
# Plot
# speciesPlot <- plot_ly(width=700, height=420, x=year, y=cum.spp, type='scatter', mode='lines') %>%
# layout(title='Vascular plant species recorded in Átl’ḵa7tsem/Howe Sound 1890-2022', yaxis=list(title='Reported Species'), xaxis = list(title="Year"))
#
# speciesPlot
speciesPlot <- plot_ly(width = 700, height = 420)
speciesPlot <- speciesPlot %>%
layout(title='Vascular plant species recorded in Átl’ḵa7tsem/Howe Sound 1890-2022', showlegend = FALSE,
xaxis = list(title="Year", range = c(1900, 2022)),
yaxis = list(title='Reported Species', range=c(0, max(cum.spp)))
)
# speciesPlot <- add_lines(speciesPlot, x=year, y=cum.spp, type='scatter', mode='lines')
steps <- list()
# General method cribbed from https://plotly.com/r/sliders/#sine-wave-slider
for (i in 1:length(year)) {
args <- list('visible', rep(FALSE, length(year)))
args[[2]][i] = TRUE
steps[[i]] <- list(label = year[[i]], method="restyle", args=args)
sppRange <- cum.spp[1:i]
yearRange <- year[1:i]
speciesPlot <- speciesPlot %>% add_lines(x=yearRange, y=sppRange, line=list(color='green'), type='scatter', mode='lines', visible = i == 1)
}
speciesPlot <- layout(speciesPlot, meta = list(mx_widgetId = "speciesPlot"),
sliders = list(list(active=0, steps = steps)))
speciesPlot
# Most historical data collected in the 1920s and between the 1960s and 1980s;
# Large increase in observations and recorded species with the emergence of
# iNaturalist in 2010s; no. species reported for Howe Sound has nearly doubled
# over the last two decades
# Plot gridded choropleth illustrating historical timeline of plant surveys 1897-2022
# Note: this plot adds one layer, including cumulative series of features for multiple decades
# to illustrate historical timeline;
# alternatively, we might add multiple layers of gridded species richness, one for each decade
# Load map layers
# Layer 1: hillshade raster
hillshade <- raster("spatial_data/rasters/Hillshade_80m.tif")
# Layer 2: coastline
coastline <- mx_read("spatial_data/vectors/Islands_and_Mainland")
# Layer 3: watershed boundary
watershed.boundary <- mx_read("spatial_data/vectors/Howe_Sound")
# Layer 4: gridded history choropleth
gridded.history <- mx_read("spatial_data/vectors/gridded_history")
gridded.history <- gridded.history %>% drop_na(richness)
# Create color palette for species richness
richness <- gridded.history$richness
values <- richness %>% unique
values <- sort(values)
t <- length(values)
pal <- leaflet::colorFactor(viridis_pal(option = "D")(t), domain = values)
history.1900 <- filter(gridded.history, year == 1900)
history.1910 <- filter(gridded.history, year == 1910)
history.1920 <- filter(gridded.history, year == 1920)
history.1930 <- filter(gridded.history, year == 1930)
history.1940 <- filter(gridded.history, year == 1940)
history.1950 <- filter(gridded.history, year == 1950)
history.1960 <- filter(gridded.history, year == 1960)
history.1970 <- filter(gridded.history, year == 1970)
history.1980 <- filter(gridded.history, year == 1980)
history.1990 <- filter(gridded.history, year == 1990)
history.2000 <- filter(gridded.history, year == 2000)
history.2010 <- filter(gridded.history, year == 2010)
history.2020 <- filter(gridded.history, year == 2020)
history.2022 <- filter(gridded.history, year == 2022)
historyData <- list(mapTitle = "Map 2. Historical collection activities")
write(jsonlite::toJSON(historyData, auto_unbox = TRUE, pretty = TRUE), "viz_data/History-plotData.json")
# Plot map
heatMap <- leaflet(options=list(mx_mapId="History")) %>%
setView(-123.2194, 49.66076, zoom = 8.5) %>%
addTiles(options = providerTileOptions(opacity = 0.5)) %>%
addRasterImage(hillshade, opacity = 0.8) %>%
addPolygons(data = coastline, color = "black", weight = 1.5, fillOpacity = 0, fillColor = NA) %>%
# These indices need to agree with positions in array "year"
addPolygons(data = history.1900, fillColor = ~pal(richness), fillOpacity = 0.6, weight = 0, options = list(mx_subLayerIndex = 0)) %>%
addPolygons(data = history.1910, fillColor = ~pal(richness), fillOpacity = 0.6, weight = 0, options = list(mx_subLayerIndex = 1)) %>%
addPolygons(data = history.1920, fillColor = ~pal(richness), fillOpacity = 0.6, weight = 0, options = list(mx_subLayerIndex = 2)) %>%
addPolygons(data = history.1930, fillColor = ~pal(richness), fillOpacity = 0.6, weight = 0, options = list(mx_subLayerIndex = 3)) %>%
addPolygons(data = history.1940, fillColor = ~pal(richness), fillOpacity = 0.6, weight = 0, options = list(mx_subLayerIndex = 4)) %>%
addPolygons(data = history.1950, fillColor = ~pal(richness), fillOpacity = 0.6, weight = 0, options = list(mx_subLayerIndex = 5)) %>%
addPolygons(data = history.1960, fillColor = ~pal(richness), fillOpacity = 0.6, weight = 0, options = list(mx_subLayerIndex = 6)) %>%
addPolygons(data = history.1970, fillColor = ~pal(richness), fillOpacity = 0.6, weight = 0, options = list(mx_subLayerIndex = 7)) %>%
addPolygons(data = history.1980, fillColor = ~pal(richness), fillOpacity = 0.6, weight = 0, options = list(mx_subLayerIndex = 8)) %>%
addPolygons(data = history.1990, fillColor = ~pal(richness), fillOpacity = 0.6, weight = 0, options = list(mx_subLayerIndex = 9)) %>%
addPolygons(data = history.2000, fillColor = ~pal(richness), fillOpacity = 0.6, weight = 0, options = list(mx_subLayerIndex = 10)) %>%
addPolygons(data = history.2010, fillColor = ~pal(richness), fillOpacity = 0.6, weight = 0, options = list(mx_subLayerIndex = 11)) %>%
addPolygons(data = history.2020, fillColor = ~pal(richness), fillOpacity = 0.6, weight = 0, options = list(mx_subLayerIndex = 12)) %>%
addPolygons(data = history.2022, fillColor = ~pal(richness), fillOpacity = 0.6, weight = 0, options = list(mx_subLayerIndex = 13)) %>%
addLegend(position = 'topright',
colors = viridis_pal(option = "D")(t),
labels = values) %>%
addPolygons(data = watershed.boundary, color = "black", weight = 4, fillOpacity = 0)
#Note that this statement is only effective in standalone R
print(heatMap)