Prepara los datos de mariposas
Antonio J. Pérez-Luque
2021-12-28
Last updated: 2022-07-10
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Knit directory: ms_mariposas_biodiversity/
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Introduction
En este apartado usamos los datos de contactos de mariposas para su preparación.
library(tidyverse)
library(readxl)
library(janitor)
library(here)
library(lubridate)
library(DT)
library(vegan)
library(writexl)Preparación de datos
Usamos datos descargados directamente de linaria.obsnev.es. Tenemos dos archivos: conteos y visitas.
Utilizamos solo fecha de inicio y computamos mes, año y día de cada contacto.
Filtramos los datos:
- No utilizaremos los años 2008, 2009, 2010, 2011, 2021
- No utilizaremos datos de los meses de marzo, abril, septiembre y octubre
- No usamos los datos: Robledal de Cáñar ni Hoya de la Mora
rawdata <- read_delim(here::here("data/mariposas_diurnas_contactos_transectos.csv"), delim = ";") %>%
janitor::clean_names() %>%
mutate(year = lubridate::year(fecha_inicio),
month = lubridate::month(fecha_inicio),
day = lubridate::day(fecha_inicio)) %>%
dplyr::select(-fecha_fin, -fecha_inicio)
d <- rawdata %>%
filter(year >= 2011) %>%
filter(year < 2021) %>%
filter(!(month %in% c(3,4,9,10))) %>%
filter(transecto != "Hoya de la Mora") %>%
filter(transecto != "Robledal de Cáñar") - Leer información de transectos: longitud, abreviatura; y creamos una variable llamada elev (elevación) que corresponde al promedio entre la altura mínima y máxima del transecto.
metadata_transectos <- read_excel(here::here("data/longitud_transectos.xlsx"),
sheet = "Longitud_transectos") %>%
janitor::clean_names() %>%
mutate(id_transecto = paste0("16_",transectid))
abrev <- read_csv(here::here("data/transect_abrev.csv")) %>% janitor::clean_names() %>%
rename(id_transecto = id_transect)
transectos <- metadata_transectos %>%
inner_join(abrev) %>%
dplyr::select(-transectid, -transect) %>%
rowwise() %>%
mutate(elev = round(((min_altitu+max_altitu)/2),0)) %>%
rename(transecto = name)
write_csv(transectos, here::here("data/transectos_tabla.csv"))Total contactos transecto y año
- Generamos un dataset con el total (número de contactos) por transecto y visita
ntotal_transecto_visita <- d %>%
group_by(id_visita, id_transecto, transecto, year) %>%
summarise(ntotal = sum(total)) - Leemos información de las visitas realizadas a los transectos. Genero un dataset de visitas con aquellas visitas sin contactos
rawvisitas <- read_delim(here::here("data/mariposas_diurnas_visitas.csv"),
delim = ";", col_types = cols(Temperatura = col_number())) %>% janitor::clean_names() %>%
mutate(year = lubridate::year(fecha_inicio),
month = lubridate::month(fecha_inicio),
day = lubridate::day(fecha_inicio)) %>%
dplyr::select(-fecha_fin, -fecha_inicio)
visitas <- rawvisitas %>%
filter(year >= 2011) %>%
filter(year < 2021) %>%
filter(transecto_parcela != "Hoya de la Mora") %>%
filter(transecto_parcela != "Robledal de Cáñar") %>%
filter(!(month %in% c(3,4,9,10))) %>%
dplyr::select(id_visita=id, transecto=transecto_parcela, year, month, day)
ntotal_transecto_visitas_cero <- visitas %>%
filter(!(id_visita %in% unique(d$id_visita))) %>%
mutate(ntotal = 0) %>%
dplyr::select(-month, -day) %>%
inner_join((transectos %>% dplyr::select(transecto, id_transecto))) %>%
relocate(id_visita, transecto, id_transecto)Unimos los dos datasets anteriores y le adjuntamos información de los transectos.
Filtramos los datos de 2018. Eliminamos todas las visitas de 2018 excepto para los transectos Pitres, Dúrcal, Turbera, Laguna (“16_45”,“16_46”,“16_48”,“16_49”)
ntotalraw <- bind_rows(ntotal_transecto_visita, ntotal_transecto_visitas_cero) %>% inner_join(transectos)
ntotal <- ntotalraw %>%
filter(!(year == 2018 & !id_transecto %in% c("16_45","16_46","16_48","16_49")))Densidad
- Densidad por año
densidad_by_year <- ntotal %>%
group_by(id_transecto, transecto, site, elev, year) %>%
summarise(abundancia = sum(ntotal),
long_total = sum(longitud) / 100) %>%
mutate(den = abundancia / long_total)write_csv(densidad_by_year, here::here("data/densidad_by_year.csv"))datatable(densidad_by_year)Diversidad
- ¿Cuantas especies se han contactado?. Observamos que hay registros de taxones identificados a diferentes niveles. Vamos a ver aquellos que estén registrados a nivel al menos específico.
taxones_anotados <- d %>%
dplyr::select(id_especie, nombre_cientifico) %>% unique() %>%
mutate(w = stringr::str_count(nombre_cientifico, "\\w+"))
especies <- taxones_anotados %>% filter(w>1)m <- d %>%
filter(!(year == 2018 & !id_transecto %in% c("16_45","16_46","16_48","16_49"))) %>%
filter(nombre_cientifico %in% especies$nombre_cientifico) %>%
mutate(sp = stringr::word(nombre_cientifico, start = 1, end = 2)) %>%
mutate(spabrev = stringr::str_replace(sp," ", ".")) %>%
dplyr::select(-sp) %>%
mutate(sp = str_replace(spabrev, " ", ".")) %>%
group_by(transecto, spabrev, year) %>%
summarise(n_ind = sum(total)) %>%
pivot_wider(names_from = year,
values_from=n_ind,
names_prefix = "y", values_fill = 0) %>% as.data.frame()
years <- c("y2012","y2013","y2014","y2015","y2016","y2017","y2018","y2019","y2020")
out_h <- data.frame()
for (y in years){
vars <- c("spabrev", "transecto", y)
aux_diversidad <- m %>%
dplyr::select(all_of(vars)) %>%
pivot_wider(names_from = spabrev, values_from = y, values_fill = 0) %>%
column_to_rownames(var = "transecto")
h <- vegan::diversity(aux_diversidad) %>% as.data.frame()
names(h) <- "diversidad"
h$year <- y
h$transecto <- row.names(h)
out_h <- rbind(out_h, h)
}
# Ojo en el cómputo de diversidad aparecen años y transectos con 0. Creo que es un error. Los dejo con NA
rownames(out_h) <- NULL
diversidad <- out_h %>%
mutate(year = as.numeric(substring(year,2)),
diversidad = na_if(diversidad,0)) %>%
inner_join(transectos)
write_csv(diversidad, here::here("data/diversidad_by_year.csv"))datatable(diversidad)Riqueza
riq <- d %>%
filter(!(year == 2018 & !id_transecto %in% c("16_45","16_46","16_48","16_49"))) %>%
filter(nombre_cientifico %in% especies$nombre_cientifico) %>%
mutate(sp = stringr::word(nombre_cientifico, start = 1, end = 2)) %>%
mutate(spabrev = stringr::str_replace(sp," ", ".")) %>%
dplyr::select(-sp) %>%
group_by(transecto, year) %>%
summarise(sp_unique = unique(spabrev)) %>%
group_by(transecto, year) %>%
count() %>%
rename(riq = n) %>%
inner_join(transectos)
write_csv(riq, here::here("data/riqueza_by_year.csv"))datatable(riq)riq_site <- d %>%
filter(!(year == 2018 & !id_transecto %in% c("16_45","16_46","16_48","16_49"))) %>%
filter(nombre_cientifico %in% especies$nombre_cientifico) %>%
mutate(sp = stringr::word(nombre_cientifico, start = 1, end = 2)) %>%
mutate(spabrev = stringr::str_replace(sp," ", ".")) %>%
dplyr::select(-sp) %>%
group_by(transecto) %>%
summarise(sp_unique = unique(spabrev)) %>%
group_by(transecto) %>%
count() %>%
rename(riq = n) %>%
inner_join(transectos)
write_csv(riq_site, here::here("data/riqueza_by_site.csv"))Tabla suplementaria especies - transectos
ts <- d %>%
filter(!(year == 2018 & !id_transecto %in% c("16_45","16_46","16_48","16_49"))) %>%
filter(nombre_cientifico %in% especies$nombre_cientifico) %>%
group_by(transecto, nombre_cientifico, year) %>%
summarise(n_ind = sum(total)) %>%
pivot_wider(names_from = year,
values_from=n_ind,
names_prefix = "y", values_fill = 0) %>% as.data.frame() %>%
inner_join(transectos) %>%
rowwise() %>%
mutate(contactos = sum(across(starts_with("y")))) %>%
mutate(n_years_contacted = 9 - sum(across(starts_with("y")) == 0)) # Número total de años (9) - años con cero contactos
write_csv(ts, here::here("data/tabla_especies_transectos.csv")) Species accumulation curve
curvas_spec <- data.frame()
for (i in unique(m$transecto)) {
aux <- m %>% filter(transecto==i) %>%
relocate(y2018, .after=y2017) %>%
ungroup() %>% dplyr::select(-transecto) %>%
pivot_longer(-spabrev, names_to = "year", values_to = "nind") %>%
pivot_wider(names_from = spabrev, values_from = nind) %>% column_to_rownames("year") %>%
as.data.frame()
sca <- vegan::specaccum(aux, method = "collector")
sca_random <- vegan::specaccum(aux, method = "random", permutations = 499)
s <- data.frame(richness = sca_random$richness,
sd = sca_random$sd,
sites = sca_random$sites,
richness_real = sca$richness,
years = seq(2012,2020,1))
rownames(s) <- NULL
s$transecto <- i
curvas_spec <- rbind(curvas_spec, s)
}
plot_curvas <- curvas_spec %>%
ggplot(aes(x=years, y=richness)) +
theme_minimal() +
geom_ribbon(aes(ymin = richness - 1.96*sd, ymax = richness + 1.96*sd), fill="lightblue", alpha =.5) +
geom_line(colour = "blue") +
geom_line(aes(y=richness_real), col = "black") +
facet_wrap(~transecto, scales = "free_y", ncol = 4) +
xlab('year') + ylab('Richness') +
theme(panel.grid.minor = element_blank())ggsave(here::here("figs/plot_species_acumulation_area.pdf"),
device = "pdf",
width = 12, height = 11)
plot_curvas 
dev.off()null device
1
sessionInfo()R version 4.0.2 (2020-06-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.3
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] writexl_1.3.1 vegan_2.5-7 lattice_0.20-41 permute_0.9-5
[5] DT_0.17 lubridate_1.7.10 here_1.0.1 janitor_2.1.0
[9] readxl_1.3.1 forcats_0.5.1 stringr_1.4.0 dplyr_1.0.6
[13] purrr_0.3.4 readr_1.4.0 tidyr_1.1.3 tibble_3.1.2
[17] ggplot2_3.3.5 tidyverse_1.3.1 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] nlme_3.1-152 fs_1.5.0 httr_1.4.2 rprojroot_2.0.2
[5] tools_4.0.2 backports_1.2.1 bslib_0.3.1 utf8_1.1.4
[9] R6_2.5.1 DBI_1.1.1 mgcv_1.8-33 colorspace_2.0-2
[13] withr_2.4.1 tidyselect_1.1.1 processx_3.5.1 compiler_4.0.2
[17] git2r_0.28.0 textshaping_0.3.2 cli_2.5.0 rvest_1.0.0
[21] xml2_1.3.2 labeling_0.4.2 sass_0.4.1 scales_1.1.1.9000
[25] callr_3.7.0 systemfonts_1.0.0 digest_0.6.27 rmarkdown_2.14
[29] pkgconfig_2.0.3 htmltools_0.5.2 highr_0.8 dbplyr_2.1.1
[33] fastmap_1.1.0 htmlwidgets_1.5.4 rlang_0.4.12 rstudioapi_0.13
[37] farver_2.1.0 jquerylib_0.1.3 generics_0.1.0 jsonlite_1.7.2
[41] crosstalk_1.1.1 magrittr_2.0.1 Matrix_1.3-2 Rcpp_1.0.7
[45] munsell_0.5.0 fansi_0.4.2 lifecycle_1.0.1 stringi_1.7.4
[49] whisker_0.4 yaml_2.2.1 snakecase_0.11.0 MASS_7.3-53
[53] grid_4.0.2 parallel_4.0.2 promises_1.2.0.1 crayon_1.4.1
[57] haven_2.3.1 splines_4.0.2 hms_1.0.0 knitr_1.31
[61] ps_1.5.0 pillar_1.6.1 reprex_2.0.0 glue_1.4.2
[65] evaluate_0.14 getPass_0.2-2 modelr_0.1.8 vctrs_0.3.8
[69] httpuv_1.5.5 cellranger_1.1.0 gtable_0.3.0 assertthat_0.2.1
[73] xfun_0.30 broom_0.7.9 later_1.1.0.1 ragg_1.1.1
[77] cluster_2.1.0 ellipsis_0.3.2