Last updated: 2021-05-20

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html 465c356 Antonio J Perez-Luque 2021-05-20 Build site.
Rmd b3d9091 Antonio J Perez-Luque 2021-05-20 filter Delichon urbicum
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Rmd 8feb234 Antonio J Perez-Luque 2021-04-30 fix taxonomic error Cuculus
html 1cb0658 Antonio J Perez-Luque 2021-04-29 Build site.
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html ec67c0c Antonio J Perez-Luque 2021-04-29 Build site.
Rmd 03e8fc6 Antonio J Perez-Luque 2021-04-29 prepare and cleaning data; add exported data
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Rmd a6cd9dc Antonio J Perez-Luque 2021-04-29 cleaning old censuses
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Rmd 1ed7427 Antonio J Perez-Luque 2021-04-28 Add my first analysis

knitr::opts_chunk$set(echo = TRUE, 
                      warning = FALSE, 
                      message = FALSE)

Data sources

Data coming from two sources:

  • Old bird censuses provided by R. Zamora, consists in bird censuses in three locations: oak population (1700 masl); juniper-scrubland (2230 masl) and summit environments (3200 masl). Range temporal cover from 1981 to 1985.

  • Obsnev bird censuses provided by OBSNEV, realized in several transects distributed along Sierra Nevada. Temporal range from 2008 to 2020. The data were downloaded from new information system of OBSNEV (i.e. PostgreSQL db01.obsnev.es)

All data are stored in the folder /data_raw

Prepare Old bird censuses

Notes for Old bird censuses

  • File RObledal año 1981 RZAves_SN_10ha.xls:

    • habitat: oak woodlands (Q. pyrenaica)
    • elevation: 1700 masl
    • year: 1981
    • variable: bird abundance monthly aggregated (ind / 10 ha)
    • sample size: n = 3 (may, june, july)
    • notes: The data aren’t the original bird censuses but are monthly aggregated
  • File Enebral año 1985 RZAves_SN_10ha.xls

    • habitat: juniper-scrubland
    • elevation: 2300 masl
    • year: 1985
    • variable: bird abundance monthly aggregated (ind / 10 ha)
    • sample size: n = 3 (may, june, july)
    • notes: The data aren’t the original bird censuses but are monthly aggregated
  • File Aves_SN_meses_reproduccion.xls

    • habitat: jseveral habitats. We selected juniper-scrubland and summit environments
    • elevation: 2300 and 3200 masl
    • year: 1984 (juniper); 1982 (summit environment)
    • variable: raw bird abundance. For juniper ind / 10.2 ha; and for summit ind / 20 ha.
    • sample size: n = 9 transects (juniper) and 6 transects (juniper) during may-july
    • notes: Original bird censuses
library("tidyverse")
library("here")
library("readxl")
library("DT")
robledal1981 <- read_excel(here::here("data/data_raw/RObledal año 1981 RZAves_SN_10ha.xls")) %>% 
  pivot_longer(cols= mayo_1981:julio_1981, names_to="fecha") %>% 
  separate(fecha, into = c("mes", "year"), sep="_", remove = FALSE) %>% 
  rename("especie" = Aves, "den" = value) %>% 
  mutate(year = as.numeric(year), 
         habitat = "robledal", 
         cota = 1700,
         mes = case_when(
           mes == "mayo" ~ as.numeric(5), 
           mes == "junio" ~ as.numeric(6),
           mes == "julio" ~ as.numeric(7)), 
         fecha = format(as.Date(paste(year, mes, "01", sep="-")), format="%Y-%m-%d")) 

head(robledal1981)
# A tibble: 6 x 7
  especie              fecha        mes  year   den habitat   cota
  <chr>                <chr>      <dbl> <dbl> <dbl> <chr>    <dbl>
1 Phylloscopus bonelli 1981-05-01     5  1981   8   robledal  1700
2 Phylloscopus bonelli 1981-06-01     6  1981  13.8 robledal  1700
3 Phylloscopus bonelli 1981-07-01     7  1981  14.6 robledal  1700
4 Sylvia atricapilla   1981-05-01     5  1981   4.4 robledal  1700
5 Sylvia atricapilla   1981-06-01     6  1981   4.6 robledal  1700
6 Sylvia atricapilla   1981-07-01     7  1981   6.8 robledal  1700
enebral1985 <- read_excel(here::here("data/data_raw/Enebral año 1985 RZAves_SN_10ha.xls")) %>%   pivot_longer(cols= mayo_1985:julio_1985, names_to="fecha") %>% 
  separate(fecha, into = c("mes", "year"), sep="_") %>% 
  rename("especie" = Aves, "den" = value) %>% 
  mutate(year = as.numeric(year), 
         habitat = "enebral", 
         cota = 2230, 
         mes = case_when(
           mes == "mayo" ~ as.numeric(5), 
           mes == "junio" ~ as.numeric(6),
           mes == "julio" ~ as.numeric(7)),
         fecha = format(as.Date(paste(year, mes, "01", sep="-")), format="%Y-%m-%d")) 

head(enebral1985)
# A tibble: 6 x 7
  especie               mes  year   den habitat  cota fecha     
  <chr>               <dbl> <dbl> <dbl> <chr>   <dbl> <chr>     
1 Carduelis carduelis     5  1985   0   enebral  2230 1985-05-01
2 Carduelis carduelis     6  1985   0   enebral  2230 1985-06-01
3 Carduelis carduelis     7  1985   0.1 enebral  2230 1985-07-01
4 Alauda arvensis         5  1985   5.1 enebral  2230 1985-05-01
5 Alauda arvensis         6  1985   5.4 enebral  2230 1985-06-01
6 Alauda arvensis         7  1985   5.7 enebral  2230 1985-07-01
enebral1984 <- read_excel(here::here("data/data_raw/Aves_SN_meses_reproduccion.xlsx"),
                           sheet = "2230") %>% 
  rename("especie" = Ave, "den" = `Número`) %>% 
  mutate(den = round(den*(10/10.2),2),
         habitat = "enebral", 
         cota = 2230, 
         mes = lubridate::month(Fecha), 
         year = lubridate::year(Fecha), 
         Fecha = strftime(Fecha, format="%Y-%m-%d")) %>% 
  rename(fecha = Fecha)

head(enebral1984)  
# A tibble: 6 x 7
  especie             fecha        den habitat  cota   mes  year
  <chr>               <chr>      <dbl> <chr>   <dbl> <dbl> <dbl>
1 Oenanthe oenanthe   1984-05-05 15.7  enebral  2230     5  1984
2 Alauda arvensis     1984-05-05  3.92 enebral  2230     5  1984
3 Emberiza cia        1984-05-05 15.7  enebral  2230     5  1984
4 Phoenicurus ochuros 1984-05-05  0.98 enebral  2230     5  1984
5 Anthus campestris   1984-05-05  0.98 enebral  2230     5  1984
6 Alectoris rufa      1984-05-05  0.98 enebral  2230     5  1984
cumbres1982 <- read_excel(here::here("data/data_raw/Aves_SN_meses_reproduccion.xlsx"),
                           sheet = "3200") %>% 
  rename("especie" = Ave, "den" = `Número`) %>% 
  mutate(den = round(den*(10/20),2),
         habitat = "cumbres", 
         cota = 3200, 
         mes = lubridate::month(Fecha), 
         year = lubridate::year(Fecha),
        Fecha = strftime(Fecha, format="%Y-%m-%d")) %>% 
  rename(fecha = Fecha)

head(cumbres1982)
# A tibble: 6 x 7
  especie             fecha        den habitat  cota   mes  year
  <chr>               <chr>      <dbl> <chr>   <dbl> <dbl> <dbl>
1 Oenanthe oenanthe   1982-06-06   0.5 cumbres  3200     6  1982
2 Phoenicurus ochuros 1982-06-06   3   cumbres  3200     6  1982
3 Prunella collaris   1982-06-06   2.5 cumbres  3200     6  1982
4 Oenanthe oenanthe   1982-06-07   0.5 cumbres  3200     6  1982
5 Phoenicurus ochuros 1982-06-07   1   cumbres  3200     6  1982
6 Prunella collaris   1982-06-07   4   cumbres  3200     6  1982
  • Bind old data
old_census <- bind_rows(cumbres1982, enebral1984, enebral1985, robledal1981)
datatable(old_census)

Prepare OBSNEV bird censuses

  • Raw data were downloaded from OBSNEV information system. The downloaded tables were: contactos_paseriformes.csv; dicc_especies.csv; geo.csv; visitas.csv.

  • The protocol sampling number for passerine in the database is 5.

  • Select the three locations: “Cortijo del Hornillo” (oak), “Campos de Otero” (juniper), and “Aguas Verdes” (summits).

  • Filter data only for may, june, july

  • Filter out data contacted over 30 m

  • Select only the taxonomic level = species (level > 6)

contactos <- read_csv(here::here("data/data_raw/contactos_paseriformes.csv")) %>% 
  dplyr::select(-fcreacion, -fmodificacion)

dicc_sp <- read_csv(here::here("data/data_raw/dicc_especies.csv")) %>% 
  dplyr::select(idesp, nombre_cientifico, nivel)

# El protocolo de paseriformes es el 5
dicc_visita <- read_csv(here::here("data/data_raw/visitas.csv"), 
                        col_types = 
                          cols(.default ="?", 
                               idgeo = col_character(), 
                               fvisita = col_datetime(format="%Y-%m-%d %H:%M:%S"))) %>%
  filter(protocolo == 5) 
  
dicc_geo <- read_csv(here::here("data/data_raw/geo.csv"), 
                     col_types = cols(.default ="?", 
                               longitud_m = col_double()))

visita_geo <- 
  dicc_visita %>% inner_join(dicc_geo, by = "idgeo") %>% 
  dplyr::select(idvisitas, fvisita, nombre, longitud_m) 

dfraw <- contactos %>% 
  inner_join(visita_geo, by = "idvisitas") %>% 
  inner_join(dicc_sp, by = "idesp") %>% 
  mutate(year = lubridate::year(fvisita), 
         mes = lubridate::month(fvisita))

df <- dfraw %>% 
  filter(nombre %in% c("Cortijo del Hornillo", "Campos de Otero", "Aguas Verdes")) %>% 
  mutate(habitat = recode(nombre, 
                          "Campos de Otero" = "enebral",
                          "Cortijo del Hornillo" = "robledal",
                          "Aguas Verdes" = "cumbres")) %>%
  mutate(cota = case_when(
    habitat == "enebral" ~ 2230, 
    habitat == "robledal" ~ 1700,
    habitat == "cumbres" ~ 3200
  )) %>% 
  mutate(year = lubridate::year(fvisita), 
         mes = lubridate::month(fvisita),
         fecha = strftime(fvisita, format="%Y-%m-%d")) %>% 
  filter(mes %in% c(5,6,7)) %>% 
  filter(desplazamiento < 31) %>% 
  filter(nivel > 6) 

head(df)
# A tibble: 6 x 17
     id idvisitas idesp numero distancia desplazamiento observaciones
  <dbl>     <dbl> <dbl>  <dbl>     <dbl>          <dbl> <chr>        
1  2010      1823 24641      1       260             15 NULL         
2  2011      1823 24592      1       255             15 NULL         
3  2012      1823 24587      1       280              5 NULL         
4  2013      1823 24587      1       295              5 NULL         
5  2014      1823 24587      2       370              5 NULL         
6  2015      1823 24587      1       411              5 NULL         
# … with 10 more variables: fvisita <dttm>, nombre <chr>, longitud_m <dbl>,
#   nombre_cientifico <chr>, nivel <dbl>, year <dbl>, mes <dbl>, habitat <chr>,
#   cota <dbl>, fecha <chr>
  • Aggregated data to get the sum of all contacts for a specie recorded in the same visit of the same transect

  • Remove an error “Prunus avium” in species codification

dfab <- df %>% 
  filter(nombre_cientifico != "Prunus avium") %>% 
  group_by(nombre_cientifico, fecha, year, mes, longitud_m, nombre, habitat, nivel, cota) %>%
  summarise(total_ind = sum(numero)) %>%  
  mutate(den = round((total_ind * 10000 * 10 / (longitud_m * 60)),2)) %>% 
  ungroup() %>% 
  rename(especie = nombre_cientifico) %>% 
  dplyr::select(-nivel, -longitud_m, -nombre, -total_ind)
  • Join old and new data, and export data. Be caution with this data, since it includes some species that specialists indicated to remove them.

  • Export data as birds.csv

  • Recode especies according to:

    • Acanthis cannabina to Carduelis cannabina;
    • Parus caeruleus to Cyanistes caeruleus;
    • Parus ater to Periparus ater;
    • Phoenicurus ochuros to Phoenicurus ochruros;
    • Cuculos canorus to Cuculus canorus
birds <- bind_rows(old_census, dfab) %>% 
  mutate(especie = case_when(
    especie == "Acanthis cannabina" ~ "Carduelis cannabina",
    especie == "Parus caeruleus" ~ "Cyanistes caeruleus",
    especie == "Parus ater" ~ "Periparus ater", 
    especie == "Phoenicurus ochuros" ~ "Phoenicurus ochruros", 
    especie == "Cuculos canorus" ~ "Cuculus canorus",
    TRUE ~ especie
  ))
datatable(birds)
write_csv(birds, here::here("data/birds.csv"))
  • Generate an abbreviation for the especies composed by the three first characters of Genus and the three first characters of species, separated by a dot.

  • Filter out species. After consultation of specialist we’ll remove the following species:

remove_sp<- c("Acrocephalus dumetorum", "Alectoris rufa", "Columba palumbus", "Corvus corax", "Ficedula albicollis", "Himantopus himantopus", "Ixobrychus sturmii", "Luscinia svecica", "Monticola solitarius","Oceanodroma leucorhoa", "Prunus avium", "Puffinus yelkouan", "Pyrrhocorax pyrrhocorax", "Ptyonoprogne rupestris", "Delichon urbicum")

passerine  <- birds %>% 
  filter(!especie %in% remove_sp) %>% 
  separate(especie, c("genus", "sp")) %>% 
  mutate(g.ab = substr(genus, 1, 3), 
         s.ab = substr(sp, 1, 3)) %>% 
  unite("sp.abb", g.ab:s.ab, sep=".") %>% 
  unite("especie", genus:sp, sep=" ")

datatable(passerine)
passerine_abbreviations <- passerine %>% 
  dplyr::select(especie, sp.abb) %>% 
  unique()

write_csv(passerine, here::here("data/passerine.csv"))
write_csv(passerine_abbreviations, here::here("data/passerine_abbrev.csv"))

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] DT_0.17         readxl_1.3.1    here_1.0.1      forcats_0.5.1  
 [5] stringr_1.4.0   dplyr_1.0.4     purrr_0.3.4     readr_1.4.0    
 [9] tidyr_1.1.2     tibble_3.0.6    ggplot2_3.3.3   tidyverse_1.3.0
[13] workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.6        lubridate_1.7.10  assertthat_0.2.1  rprojroot_2.0.2  
 [5] digest_0.6.27     utf8_1.1.4        R6_2.5.0          cellranger_1.1.0 
 [9] backports_1.2.1   reprex_1.0.0      evaluate_0.14     httr_1.4.2       
[13] pillar_1.4.7      rlang_0.4.10      rstudioapi_0.13   whisker_0.4      
[17] jquerylib_0.1.3   rmarkdown_2.6.6   htmlwidgets_1.5.3 munsell_0.5.0    
[21] broom_0.7.4       compiler_4.0.2    httpuv_1.5.5      modelr_0.1.8     
[25] xfun_0.20         pkgconfig_2.0.3   htmltools_0.5.1.1 tidyselect_1.1.0 
[29] fansi_0.4.2       crayon_1.4.1      dbplyr_2.1.0      withr_2.4.1      
[33] later_1.1.0.1     grid_4.0.2        jsonlite_1.7.2    gtable_0.3.0     
[37] lifecycle_1.0.0   DBI_1.1.1         git2r_0.28.0      magrittr_2.0.1   
[41] scales_1.1.1      cli_2.3.0         stringi_1.5.3     fs_1.5.0         
[45] promises_1.2.0.1  xml2_1.3.2        bslib_0.2.4       ellipsis_0.3.1   
[49] generics_0.1.0    vctrs_0.3.6       tools_4.0.2       glue_1.4.2       
[53] hms_1.0.0         crosstalk_1.1.1   yaml_2.2.1        colorspace_2.0-0 
[57] rvest_0.3.6       knitr_1.31        haven_2.3.1       sass_0.3.1