Last updated: 2021-04-29
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Knit directory: booksn_dispersantes/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | bf02c97 | Antonio J Perez-Luque | 2021-04-29 | fix taxonomic erros |
| 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 |
| html | 5f6b690 | Antonio J Perez-Luque | 2021-04-29 | Build site. |
| Rmd | a6cd9dc | Antonio J Perez-Luque | 2021-04-29 | cleaning old censuses |
| html | 7281d42 | Antonio J Perez-Luque | 2021-04-28 | Build site. |
| Rmd | 1ed7427 | Antonio J Perez-Luque | 2021-04-28 | Add my first analysis |
knitr::opts_chunk$set(echo = TRUE,
warning = FALSE,
message = FALSE)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
File RObledal año 1981 RZAves_SN_10ha.xls:
File Enebral año 1985 RZAves_SN_10ha.xls
File Aves_SN_meses_reproduccion.xls
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 1700enebral1985 <- 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 1985-07-01enebral1984 <- 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 1984cumbres1982 <- 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 1982old_census <- bind_rows(cumbres1982, enebral1984, enebral1985, robledal1981)
datatable(old_census)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:
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",
TRUE ~ especie
))
datatable(birds)write_csv(birds, here::here("data/birds.csv"))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")
passerine <- birds %>%
filter(!especie %in% remove_sp)
datatable(passerine)write_csv(passerine, here::here("data/passerine.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