Last updated: 2021-07-01

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Rmd a2df5c6 Antonio J Perez-Luque 2021-07-01 add exploratory analysis

Introduction

  • Evaluar la variación de parámetros relacionados con vegetación tras la realización de quemas prescritas en parcelas con diferentes tratamientos de pastoreo.

  • Analizar si la fecha de la quema afecta a la velocidad de recuperación. Existen algunos estudios que señalan que la recuperación de la vegetación en las zonas afectadas por fuegos de primavera es rápida (ver referencias en Pereira et al. 2016)

library(here)
library(tidyverse)
library(readxl)
library(plotrix)
library(DT)

Preparar los datos

Recodifico los datos para facilidad de manejo.

  • ZONA. Se convierte a factor (zonaCod):
    • Quemado con pastoreo ~ OP
    • Quemado sin pastoreo ~ ONP
    • Quemado primavera ~ PP
  • RANGO_INFOCA. Se convierte a factor (rango. Se añade “R” delante de cada rango (i.e. RANGO_INFOCA = 1 ~ R1)

Se añade una variable de tiempo tras el fuego (time). Para ello, previamente establecemos la fecha de fuego en otoño (2018-12-18) y en primavera (2019-05-07). Seguidamente computamos el número de meses tras el fuego. Los muestreos previos al fuego se codifican como -1 en la variable time

Ver código! 
df <- read_excel(path=here::here("data/Cobertura.xlsx"))

quema_oto <- as.Date("2018-12-18")
quema_pri <- as.Date("2019-05-07")

cobertura <- df %>% 
  mutate(zonaCod = 
           as.factor(
             case_when(
             ZONA == "Quemado con pastoreo" ~ "OP",
             ZONA == "Quemado sin pastoreo" ~ "ONP",
             ZONA == "Quemado primavera" ~ "PP")),
         rango = 
           as.factor(
             case_when(
             RANGO_INFOCA == 1 ~ "R1",
             RANGO_INFOCA == 2 ~ "R2",
             RANGO_INFOCA == 3 ~ "R3",
             RANGO_INFOCA == 4 ~ "R4"))) %>% 
  mutate(time = 
           case_when(
    ZONA == "Quemado primavera" ~ (lubridate::interval(quema_pri, FECHA_MUESTREOS)) %/% months(1),
    TRUE ~ (lubridate::interval(quema_oto, FECHA_MUESTREOS)) %/% months(1),
    )) %>% 
  mutate(time = 
           case_when(time == 0 ~ -1,
                     TRUE ~ time),
         season = getSeason(FECHA_MUESTREOS),
         year = as.factor(lubridate::year(FECHA_MUESTREOS))) %>% 
  unite("timeSeason", year, season, sep="_", remove=FALSE) %>% 
  filter(FECHA_MUESTREOS != as.Date("2020-12-21"))

Análisis exploratorio

  • Evolución temporal de la cobertura agrupada por zonas (OP, ONP, PP) para cada uno de los rangos.

  • Calculamos el promedio de cobertura (group_by zonaCod y RANGO)

  • Se añaden las fechas de las quemas

Ver código! 
cob <- cobertura %>% 
  group_by(zonaCod, time, rango) %>% 
  summarise(mean = mean(COB_TOTAL, na.rm=TRUE),
            sd = sd(COB_TOTAL, na.rm=TRUE),
            se = plotrix::std.error(COB_TOTAL, na.rm=TRUE),
          n = length(COB_TOTAL)) %>% 
  mutate(pastoreo = 
           case_when(
             zonaCod == "OP" ~ "pastoreo",
             zonaCod == "ONP" ~ "no pastoreo",
             zonaCod == "PP" ~ "pastoreo")
  )

cob_season <- cobertura %>% 
  group_by(zonaCod, timeSeason, rango, FECHA_MUESTREOS) %>% 
  summarise(mean = mean(COB_TOTAL, na.rm=TRUE),
            sd = sd(COB_TOTAL, na.rm=TRUE),
            se = plotrix::std.error(COB_TOTAL, na.rm=TRUE),
          n = length(COB_TOTAL)) %>% 
  mutate(pastoreo = 
           case_when(
             zonaCod == "OP" ~ "pastoreo",
             zonaCod == "ONP" ~ "no pastoreo",
             zonaCod == "PP" ~ "pastoreo")
  ) %>% 
  mutate(timeSeason = factor(timeSeason, 
                             levels = c("2018_Autumn", 
                                        "2019_Spring","2019_Autumn",
                                        "2020_Spring","2020_Autumn",
                                        "2021_Spring"))) %>% 
  ungroup()
datatable(cob_season) %>% formatRound(c("mean","sd","se"), 2)
Ver código! 
p <- position_dodge(0.9)
plot_vegcob <- cob_season %>% ggplot(aes(x=FECHA_MUESTREOS, y=mean, colour=zonaCod,
                          group=zonaCod)) +
  geom_line(position = p) +  
  geom_point(position = p, 
             aes(shape=pastoreo), 
             size = 3) + 
  geom_errorbar(aes(ymin = mean-se, 
                    ymax = mean+se), 
                position = p) + 
  facet_wrap(~rango, ncol=1, scales = "free_y") + 
  theme_bw() + 
  theme(panel.grid = element_blank(), 
        strip.background = element_rect(fill="white")) + 
  ylab("veg. cover (%)") + 
  xlab("Year") + 
  scale_shape_manual(values=c(15,16)) + 
  geom_vline(xintercept = as.POSIXct(quema_oto), linetype="dotted", size = 1) +
  geom_vline(xintercept = as.POSIXct(quema_pri), linetype="dotted", colour="#00BFC4", size=1)
plot_vegcob
Evolución de la cobertura vegetal (%) tras las quemas prescritas en las tres parcelas de estudio. Se muestran valores medios y error estándar. Los cuadrados corresponden a las parcelas de no pastoreo, los circulos a las parcelas con pastoreo. Las líneas de puntos verticales indican el momento en el que se realizaron las quemas

Evolución de la cobertura vegetal (%) tras las quemas prescritas en las tres parcelas de estudio. Se muestran valores medios y error estándar. Los cuadrados corresponden a las parcelas de no pastoreo, los circulos a las parcelas con pastoreo. Las líneas de puntos verticales indican el momento en el que se realizaron las quemas

Notas

  • Keeley et al. (2005) en un estudio sobre recuperación de la vegetación tras el fuego en matorrales mediterráneos de California, computaron índices de similaridad para la densidad (cobertura) de cada una de las especies antes y después de las quemas. De esta forma podían estimar el comportamiento de las especies tras el fuego. Además podían analizar las posibles relaciones de competencia que se producen tras el fuego entre las especies. Asímismo usando un índice de Jaccard, pudieron determinan cambios a nivel de comunidad.

  • Existen varios estudios interesantes que presentan un diseño experimental similar, ver Alcañiz et al. (2016) y Alcañiz et al. (2020)

  • Incluir otras covariables como la precipitación tras el fuego (nº de días sin lluvía, etc); la pendiente, etc. Ver el estudio de Pereira et al. (2016).

References

Alcañiz, M., Outeiro, L., Francos, M., Farguell, J. & Úbeda, X. (2016). Long-term dynamics of soil chemical properties after a prescribed fire in a mediterranean forest (montgrı́ massif, catalonia, spain). Science of The Total Environment, 572, 1329–1335.
Alcañiz, M., Úbeda, X. & Cerdà, A. (2020). A 13-year approach to understand the effect of prescribed fires and livestock grazing on soil chemical properties in tivissa, NE iberian peninsula. Forests, 11, 1013.
Keeley, J.E., Fotheringham, C.J. & Baer-Keeley, M. (2005). DETERMINANTS OF POSTFIRE RECOVERY AND SUCCESSION IN MEDITERRANEAN-CLIMATE SHRUBLANDS OF CALIFORNIA. Ecological Applications, 15, 1515–1534.
Pereira, P., Cerdà, A., Lopez, A.J., Zavala, L.M., Mataix-Solera, J., Arcenegui, V., et al. (2016). Short-term vegetation recovery after a grassland fire in lithuania: The effects of fire severity, slope position and aspect. Land Degradation & Development, 27, 1523–1534. 

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         plotrix_3.8-1   readxl_1.3.1    forcats_0.5.1  
 [5] stringr_1.4.0   dplyr_1.0.6     purrr_0.3.4     readr_1.4.0    
 [9] tidyr_1.1.3     tibble_3.1.2    ggplot2_3.3.3   tidyverse_1.3.1
[13] here_1.0.1      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_2.0.0      evaluate_0.14     highr_0.8        
[13] httr_1.4.2        pillar_1.6.1      rlang_0.4.10      rstudioapi_0.13  
[17] whisker_0.4       jquerylib_0.1.3   rmarkdown_2.8     labeling_0.4.2   
[21] htmlwidgets_1.5.3 munsell_0.5.0     broom_0.7.6       compiler_4.0.2   
[25] httpuv_1.5.5      modelr_0.1.8      xfun_0.23         pkgconfig_2.0.3  
[29] htmltools_0.5.1.1 tidyselect_1.1.0  fansi_0.4.2       crayon_1.4.1     
[33] dbplyr_2.1.1      withr_2.4.1       later_1.1.0.1     grid_4.0.2       
[37] jsonlite_1.7.2    gtable_0.3.0      lifecycle_1.0.0   DBI_1.1.1        
[41] git2r_0.28.0      magrittr_2.0.1    scales_1.1.1      cli_2.5.0        
[45] stringi_1.5.3     farver_2.0.3      fs_1.5.0          promises_1.2.0.1 
[49] xml2_1.3.2        bslib_0.2.4       ellipsis_0.3.2    generics_0.1.0   
[53] vctrs_0.3.8       tools_4.0.2       glue_1.4.2        crosstalk_1.1.1  
[57] hms_1.0.0         yaml_2.2.1        colorspace_2.0-0  rvest_1.0.0      
[61] knitr_1.31        haven_2.3.1       sass_0.3.1