Last updated: 2022-07-12

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Knit directory: ms_mariposas_biodiversity/

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Rmd 3f62ed0 ajpelu 2022-07-12 rarefaction
html f07aef2 ajpelu 2022-07-11 update prepara datos. Add Species accumulation area
Rmd 02d90ff ajpelu 2022-07-11 datos nuevos modelos
html 915669b ajpelu 2022-02-04 Build site.
html c814af8 ajpelu 2022-01-18 fix error link ambientales
html d441a65 ajpelu 2022-01-18 Build site.
Rmd e6ecd35 ajpelu 2022-01-18 compute richness
html cece0cb ajpelu 2022-01-18 Build site.
Rmd 2cf9d33 ajpelu 2022-01-18 quita transectos Cáñar, HoyaMora
html a4134b0 ajpelu 2021-12-30 Build site.
Rmd ec59e79 ajpelu 2021-12-30 Generate script of prepara datos

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)
library(iNEXT)

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

Version Author Date
f07aef2 ajpelu 2022-07-11 
dev.off()
null device 
          1

Curvas Rarefaccion

mm <- m %>% 
  inner_join(
    transectos %>% dplyr::select(transecto, site)) %>%
  relocate(y2018, .after=y2017) %>% 
  ungroup() %>% rowwise() %>% 
  mutate(abun = sum(across(starts_with("y")))) %>% 
  dplyr::select(site, spabrev, abun) %>% 
  pivot_wider(names_from = site, values_from = abun, values_fill = 0) %>% 
  column_to_rownames("spabrev") %>% as.data.frame()

f <- iNEXT(mm, datatype = "abundance")
df <-  fortify(f, type = 1)


df.point <- df[which(df$method=="observed"),]
df.line <- df[which(df$method!="observed"),]
df.line$method <- factor(df.line$method,
 c("interpolated", "extrapolated"),
 c("interpolation", "extrapolation"))

plot_rarefy <- df %>% 
  ggplot(aes(x=x, y=y)) + 
  geom_point(size=2, data=df.point) +
  geom_line(aes(linetype=method), data=df.line) +
 geom_ribbon(aes(ymin=y.lwr, ymax=y.upr, 
                 colour=NULL), alpha=0.2) + 
  facet_wrap(~site, nrow = 5, scales = "free") +
  theme_minimal() +
  xlab("Number of individuals") + 
  ylab("Species Diversity") +
  theme(legend.position = "bottom",
        legend.title=element_blank(),
        text=element_text(size=12)) 
plot_rarefy

ggsave(here::here("figs/plot_rarefaction.pdf"),
       device = "pdf",
       width = 12, height = 11)
plot_rarefy 

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 10.15.7

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] iNEXT_2.0.20     writexl_1.3.1    vegan_2.5-7      lattice_0.20-41 
 [5] permute_0.9-5    DT_0.17          lubridate_1.7.10 here_1.0.1      
 [9] janitor_2.1.0    readxl_1.3.1     forcats_0.5.1    stringr_1.4.0   
[13] dplyr_1.0.6      purrr_0.3.4      readr_1.4.0      tidyr_1.1.3     
[17] tibble_3.1.2     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] plyr_1.8.6        grid_4.0.2        parallel_4.0.2    promises_1.2.0.1 
[57] crayon_1.4.1      haven_2.3.1       splines_4.0.2     hms_1.0.0        
[61] knitr_1.31        ps_1.5.0          pillar_1.6.1      reshape2_1.4.4   
[65] reprex_2.0.0      glue_1.4.2        evaluate_0.14     getPass_0.2-2    
[69] modelr_0.1.8      vctrs_0.3.8       httpuv_1.5.5      cellranger_1.1.0 
[73] gtable_0.3.0      assertthat_0.2.1  xfun_0.30         broom_0.7.9      
[77] later_1.1.0.1     ragg_1.1.1        cluster_2.1.0     ellipsis_0.3.2