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