Last updated: 2021-05-27

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Knit directory: booksn_ppm/

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Rmd d5f92fb Antonio J Perez-Luque 2021-05-27 repeat NAO analysis for SN



El objetivo es generar un dataset con los datos de la NAO y los datos de infestación. - Utilizmaos el índice NAO de invierno (Hurrell’s winter NAO) cuyos datos podemos obtener en el National Center for Atmospheric Research. Descargamos los datos y los guardamos en data/nao_station_djfm.txt

  • Genero el dataset de NAO (con lag 1, 2, y 3) desde 1989 en adelante
nao <- read_csv(here::here("data/nao_station_djfm.csv"))

nao <- nao %>% filter(year > 1988) %>% 
  mutate(nao1 = lag(nao, n=1),
         nao2 = lag(nao, n=2),
         nao3 = lag(nao, n=3))
  • Siguiendo la aproximación de Hódar, Zamora, and Cayuela (2012) vamos a calcular el porcentaje de parcleas con infestación >= 3 para cada año.

  • Leer los datos para Sierra Nevada

  • Agrupar en high infestacion (infestacion >= 3) y low infestacion (infestacion < 3)

  • Computar valor para cada año

  • Filtramos para las especies: pinaster, halepensis, sylvetris, nigra

coplas2019 <- read_csv(here::here("data/coplas2019sn.csv")) 

df <- coplas2019 %>% 
  filter(! %>% 
  dplyr::select(code, especie, `1993`:`2019`) %>% 
  pivot_longer(`1993`:`2019`, names_to = "year", values_to = "infesta") %>% 
  mutate(type = case_when(
    infesta >= 3 ~ "high",
    TRUE ~ "low" 
  )) %>% 
  mutate(year = as.numeric(year))

genera.dfnao <- function(x) {
  n_teorico <- x %>% group_by(year) %>% summarise(ntotal = length(infesta))
  n_real <- x %>% 
    filter(! %>% 
    group_by(year) %>% 
    summarise(nreal = length(infesta)) %>% 
    mutate(year = as.numeric(year))  
  ppm <- x %>% 
    filter(! %>% 
    group_by(year, type) %>% 
    summarise(n = n()) %>% 
    group_by(year) %>% 
    mutate(pct = n /sum(n, na.rm = TRUE)*100)
  ppm_year <- ppm %>% 
    pivot_wider(names_from = type,
                values_from = c(n, pct))
  out <- ppm_year %>% 
    left_join(n_real) %>% 
nao_all  <- genera.dfnao(df) %>% 
  mutate(especie = "All")

nao_halepensis <- df %>% 
  filter(especie == "P. halepensis") %>% 
  genera.dfnao() %>% 
  mutate(especie = "P. halepensis")

nao_pinaster <- df %>% 
  filter(especie == "P. pinaster") %>% 
  genera.dfnao() %>% 
  mutate(especie = "P. pinaster")

nao_nigra <- df %>% 
  filter(especie == "P. nigra") %>% 
  genera.dfnao() %>% 
  mutate(especie = "P. nigra")

nao_sylvestris <- df %>% 
  filter(especie == "P. sylvestris") %>% 
  genera.dfnao() %>% 
  mutate(especie = "P. sylvestris")

dfnaos <- bind_rows(nao_all, nao_halepensis, nao_nigra, 
                    nao_pinaster, nao_sylvestris) %>% 
  mutate(pct_high = replace_na(pct_high, 0))

nao_ppm <- nao %>% left_join(dfnaos)
  • Repetir figura 1 del trabajo de Hódar, Zamora, and Cayuela (2012), ampliando la serie hasta 2019
df_plot <- nao_ppm %>%  
  filter(! %>% 
  dplyr::select(year, nao:nao3, pct_high, especie) %>% 
  pivot_longer(nao:nao3, names_to = "nao_index") 

pearson_plot <- df_plot %>% 
  ggplot(aes(x=value, y=pct_high)) + 
  geom_point() + 
  geom_smooth(method = "lm", se=FALSE, size=.4, color="black") +
  theme_bw() +
  ylim(c(0,40)) + 
  xlab("") + 
  ylab("Percent of stands with defoliation >= 3") + 
  theme(panel.grid = element_blank(),
        strip.background = element_blank()) +
  stat_cor(aes(label = ..r.label..), color = "black", geom = "text") + facet_grid(fct_relevel(especie,"All", "P. pinea", 
         "P. pinaster", "P. halepensis",
         "P. nigra", "P. sylvestris")~nao_index, 
         labeller = labeller(nao_index = toupper))

null device 

Explorar patron temporal

df_plot_ts <- nao_ppm %>% 
  dplyr::select(year, nao, pct_high, especie) 

df_plot_ts$especie <- factor(df_plot_ts$especie, 
                             levels = c("NA","All","P. sylvestris", "P. nigra",
                                         "P. pinaster","P. halepensis"))

ylim.prim <- c(0,40)
ylim.sec <- c(-5,5)
b <- diff(ylim.prim)/diff(ylim.sec)
a <-  ylim.prim[1] - b*ylim.sec[1]

patron_nao_ppm <- df_plot_ts %>%  
  filter(! %>% 
  filter(especie != "All") %>%
  ggplot(aes(x=year, y=pct_high)) + 
  geom_hline(yintercept = a, color="gray") + 
  geom_point() + 
  facet_wrap(~especie, nrow=4) + 
  geom_line(stat="identity") + 
  geom_line(data = (
    df_plot_ts %>% select(year, nao)
  ), aes(x=year, y= a + nao*b), color="red") +
    scale_y_continuous(sec.axis = sec_axis(name="NAO", ~(.-a)/b)) + 

                     breaks = seq(1989,2019, by=1)) +
    theme_bw() + 
  theme(panel.grid = element_blank(), 
        axis.text.x = element_text(size=8, angle=90, vjust = 0.5),
        axis.title.y.right = element_text(color = "red"),
        strip.background = element_blank()) +
   xlab("") + 
  ylab("Percent of stands with defoliation >= 3") 

null device 


Hódar, José A., Regino Zamora, and Luis Cayuela. 2012. “Climate Change and the Incidence of a Forest Pest in Mediterranean Ecosystems: Can the North Atlantic Oscillation Be Used as a Predictor?” Climatic Change 113 (3-4): 699–711.

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

[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] ggpubr_0.4.0    DT_0.17         flextable_0.6.3 here_1.0.1     
 [5] forcats_0.5.1   stringr_1.4.0   dplyr_1.0.4     purrr_0.3.4    
 [9] readr_1.4.0     tidyr_1.1.2     tibble_3.0.6    ggplot2_3.3.3  
[13] tidyverse_1.3.0 workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] nlme_3.1-152      fs_1.5.0          lubridate_1.7.10  httr_1.4.2       
 [5] rprojroot_2.0.2   tools_4.0.2       backports_1.2.1   bslib_0.2.4      
 [9] R6_2.5.0          DBI_1.1.1         mgcv_1.8-33       colorspace_2.0-0 
[13] withr_2.4.1       tidyselect_1.1.0  curl_4.3          compiler_4.0.2   
[17] git2r_0.28.0      cli_2.3.0         rvest_0.3.6       xml2_1.3.2       
[21] officer_0.3.16    labeling_0.4.2    sass_0.3.1        scales_1.1.1     
[25] systemfonts_1.0.0 digest_0.6.27     foreign_0.8-81    rmarkdown_2.6.6  
[29] rio_0.5.16        base64enc_0.1-3   pkgconfig_2.0.3   htmltools_0.5.1.1
[33] highr_0.8         dbplyr_2.1.0      htmlwidgets_1.5.3 rlang_0.4.10     
[37] readxl_1.3.1      rstudioapi_0.13   farver_2.0.3      jquerylib_0.1.3  
[41] generics_0.1.0    jsonlite_1.7.2    zip_2.1.1         car_3.0-10       
[45] magrittr_2.0.1    Matrix_1.3-2      Rcpp_1.0.6        munsell_0.5.0    
[49] abind_1.4-5       gdtools_0.2.3     lifecycle_1.0.0   stringi_1.5.3    
[53] whisker_0.4       yaml_2.2.1        carData_3.0-4     grid_4.0.2       
[57] promises_1.2.0.1  crayon_1.4.1      lattice_0.20-41   haven_2.3.1      
[61] splines_4.0.2     hms_1.0.0         knitr_1.31        pillar_1.4.7     
[65] uuid_0.1-4        ggsignif_0.6.0    reprex_1.0.0      glue_1.4.2       
[69] evaluate_0.14     data.table_1.13.6 modelr_0.1.8      vctrs_0.3.6      
[73] httpuv_1.5.5      cellranger_1.1.0  gtable_0.3.0      assertthat_0.2.1 
[77] xfun_0.20         openxlsx_4.2.3    broom_0.7.4       rstatix_0.6.0    
[81] later_1.1.0.1     ellipsis_0.3.1