Last updated: 2021-12-30
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Knit directory: ms_mariposas_biodiversity/
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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)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:
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)))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)ntotal_transecto_visita <- d %>%
group_by(id_visita, id_transecto, transecto, year) %>%
summarise(ntotal = sum(total)) 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(!(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$id_visita))) %>%
mutate(ntotal = 0) %>%
dplyr::select(-month, -day) %>%
inner_join((transectos %>% dplyr::select(transecto, id_transecto))) %>%
relocate(id_visita, transecto, id_transecto)Unimos los dos datasets anteriores y le adjuntamos información de los transectos.
Filtramos los datos de 2018. Eliminamos todas las visitas de 2018 excepto para los transectos Pitres, Dúrcal, Turbera, Laguna (“16_45”,“16_46”,“16_48”,“16_49”)
ntotalraw <- bind_rows(ntotal_transecto_visita, ntotal_transecto_visitas_cero) %>% inner_join(transectos)
ntotal <- ntotalraw %>%
filter(!(year == 2018 & !id_transecto %in% c("16_45","16_46","16_48","16_49")))densidad_by_year <- ntotal %>%
group_by(id_transecto, transecto, site, elev, year) %>%
summarise(abundancia = sum(ntotal),
long_total = sum(longitud) / 100) %>%
mutate(den = abundancia / long_total)write_csv(densidad_by_year, here::here("data/densidad_by_year.csv"))datatable(densidad_by_year)taxones_anotados <- d %>%
dplyr::select(id_especie, nombre_cientifico) %>% unique() %>%
mutate(w = stringr::str_count(nombre_cientifico, "\\w+"))
especies <- taxones_anotados %>% filter(w>1)m <- d %>%
filter(!(year == 2018 & !id_transecto %in% c("16_45","16_46","16_48","16_49"))) %>%
filter(nombre_cientifico %in% especies$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)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)
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] vegan_2.5-7 lattice_0.20-41 permute_0.9-5 DT_0.17
[5] lubridate_1.7.10 here_1.0.1 janitor_2.1.0 readxl_1.3.1
[9] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.6 purrr_0.3.4
[13] readr_1.4.0 tidyr_1.1.3 tibble_3.1.2 ggplot2_3.3.5
[17] tidyverse_1.3.1 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] httr_1.4.2 sass_0.3.1 splines_4.0.2 jsonlite_1.7.2
[5] modelr_0.1.8 bslib_0.2.4 assertthat_0.2.1 getPass_0.2-2
[9] cellranger_1.1.0 yaml_2.2.1 pillar_1.6.1 backports_1.2.1
[13] glue_1.4.2 digest_0.6.27 promises_1.2.0.1 rvest_1.0.0
[17] snakecase_0.11.0 colorspace_2.0-2 Matrix_1.3-2 htmltools_0.5.2
[21] httpuv_1.5.5 pkgconfig_2.0.3 broom_0.7.9 haven_2.3.1
[25] scales_1.1.1.9000 processx_3.5.1 whisker_0.4 later_1.1.0.1
[29] git2r_0.28.0 mgcv_1.8-33 generics_0.1.0 ellipsis_0.3.2
[33] withr_2.4.1 cli_2.5.0 magrittr_2.0.1 crayon_1.4.1
[37] evaluate_0.14 ps_1.5.0 fs_1.5.0 fansi_0.4.2
[41] nlme_3.1-152 MASS_7.3-53 xml2_1.3.2 tools_4.0.2
[45] hms_1.0.0 lifecycle_1.0.1 munsell_0.5.0 reprex_2.0.0
[49] cluster_2.1.0 callr_3.7.0 compiler_4.0.2 jquerylib_0.1.3
[53] rlang_0.4.12 grid_4.0.2 rstudioapi_0.13 htmlwidgets_1.5.3
[57] crosstalk_1.1.1 rmarkdown_2.8 gtable_0.3.0 DBI_1.1.1
[61] R6_2.5.1 knitr_1.31 fastmap_1.1.0 utf8_1.1.4
[65] rprojroot_2.0.2 stringi_1.7.4 parallel_4.0.2 Rcpp_1.0.7
[69] vctrs_0.3.8 dbplyr_2.1.1 tidyselect_1.1.1 xfun_0.23