Resilience of Soil propierties
ajpelu
2021-09-08
Last updated: 2021-09-13
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Knit directory: soil_alcontar/
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Introduction
- Analysis of resilience of soil propierties.
- Only for Autumn treatment (i.e. zona == “P”; zona == “NP”)
- Interpret zona as “grazing effect”:
- zona == “P” corresponds to Browsing
- zona == “NP” corresponds to No Browsing
Prepare data
raw_soil <- readxl::read_excel(here::here("data/Resultados_Suelos_2018_2021_v2.xlsx"),
sheet = "SEGUIMIENTO_SUELOS_sin_ouliers") %>% janitor::clean_names() %>% mutate(treatment_name = case_when(str_detect(geo_parcela_nombre,
"NP_") ~ "Autumn Burning / No Browsing", str_detect(geo_parcela_nombre, "PR_") ~
"Spring Burning / Browsing", str_detect(geo_parcela_nombre, "P_") ~ "Autumn Burning / Browsing"),
zona = case_when(str_detect(geo_parcela_nombre, "NP_") ~ "QOt_NP", str_detect(geo_parcela_nombre,
"PR_") ~ "QPr_P", str_detect(geo_parcela_nombre, "P_") ~ "QOt_P"), fecha = lubridate::ymd(fecha),
pre_post_quema = case_when(pre_post_quema == "Prequema" ~ "0 preQuema", pre_post_quema ==
"Postquema" ~ "1 postQuema"))- Compute date as months after fire
autumn_fire <- lubridate::ymd("2018-12-18")
soil <- raw_soil %>% filter(zona != "QPr_P") %>% mutate(zona = as.factor(zona)) %>%
mutate(meses = as.factor(case_when(fecha == "2018-12-11" ~ as.character("-1"),
fecha != "2018-12-11" ~ as.character(lubridate::interval(autumn_fire, lubridate::ymd(fecha))%/%months(1))))) %>%
mutate(pastoreo = as.factor(case_when(zona == "QOt_P" ~ "Browsing", zona == "QOt_NP" ~
"No Browsing"))) %>% relocate(pastoreo, fecha, meses) %>% dplyr::select(-pre_post_quema,
-tratamiento)
xtabs(~meses + pastoreo, data = soil) pastoreo
meses Browsing No Browsing
-1 24 24
0 24 24
22 25 25
29 24 24Compute resilience
For each soil variable we compute the Resilience value according to the following equation (sensu LLoret et al. 2011): \[Resilience = preFire / postFire\] - We computed the resilience value for each time step: just after fire (month=0) and after 22 and 29 months.
The we groupped soil variables by treatment (Browsing vs No Browsing)
Finally we explored graphically the results.
computeResilience <- function(df, variable) {
df %>% mutate(replica = str_remove(geo_suelos_nombre, "Q|_Q")) %>% dplyr::select(one_of("pastoreo",
"meses", "geo_parcela_nombre", "replica", variable)) %>% pivot_wider(values_from = one_of(variable),
names_from = meses, names_prefix = "t") %>% filter(!(str_detect(replica,
"E3"))) %>% rename(control = `t-1`) %>% # resilience r = post/pre
mutate(res.0 = t0/control, res.22 = t22/control, res.29 = t29/control) %>% dplyr::select(pastoreo,
geo_parcela_nombre, res.0, res.22, res.29) %>% pivot_longer(res.0:res.29,
names_to = "resilience") %>% mutate(myvar = variable)
}
x <- bind_rows(computeResilience(soil, "humedad"), computeResilience(soil, "n_nh4"),
computeResilience(soil, "n_no3"), computeResilience(soil, "fe_percent"), computeResilience(soil,
"k_percent"), computeResilience(soil, "mg_percent"), computeResilience(soil,
"na_percent"), computeResilience(soil, "n_percent"), computeResilience(soil,
"c_percent"), computeResilience(soil, "c_n"), computeResilience(soil, "cic"),
computeResilience(soil, "p"), computeResilience(soil, "mo"), computeResilience(soil,
"p_h_agua_eez"), computeResilience(soil, "p_h_k_cl"))
x <- x %>% mutate(meses = str_remove(resilience, "res."))Plots
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] car_3.0-10 carData_3.0-4 glmmADMB_0.8.3.3 glmmTMB_1.0.2.1
[5] DHARMa_0.3.3.0 afex_0.28-1 performance_0.7.2 multcomp_1.4-16
[9] TH.data_1.0-10 mvtnorm_1.1-1 emmeans_1.5.4 lmerTest_3.1-3
[13] lme4_1.1-27.1 Matrix_1.3-2 fitdistrplus_1.1-3 survival_3.2-7
[17] MASS_7.3-53 ggpubr_0.4.0 janitor_2.1.0 here_1.0.1
[21] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.6 purrr_0.3.4
[25] readr_1.4.0 tidyr_1.1.3 tibble_3.1.2 ggplot2_3.3.5
[29] tidyverse_1.3.1 rmdformats_1.0.1 knitr_1.31 workflowr_1.6.2
loaded via a namespace (and not attached):
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[4] ellipsis_0.3.2 rio_0.5.16 rprojroot_2.0.2
[7] estimability_1.3 snakecase_0.11.0 fs_1.5.0
[10] rstudioapi_0.13 farver_2.0.3 fansi_0.4.2
[13] lubridate_1.7.10 xml2_1.3.2 codetools_0.2-18
[16] splines_4.0.2 jsonlite_1.7.2 nloptr_1.2.2.2
[19] broom_0.7.9 dbplyr_2.1.1 compiler_4.0.2
[22] httr_1.4.2 backports_1.2.1 assertthat_0.2.1
[25] fastmap_1.1.0 cli_2.5.0 formatR_1.8
[28] later_1.1.0.1 htmltools_0.5.2 tools_4.0.2
[31] coda_0.19-4 gtable_0.3.0 glue_1.4.2
[34] reshape2_1.4.4 Rcpp_1.0.7 cellranger_1.1.0
[37] jquerylib_0.1.3 vctrs_0.3.8 nlme_3.1-152
[40] iterators_1.0.13 insight_0.14.4 xfun_0.23
[43] openxlsx_4.2.3 rvest_1.0.0 lifecycle_1.0.0
[46] rstatix_0.6.0 zoo_1.8-8 scales_1.1.1
[49] hms_1.0.0 promises_1.2.0.1 parallel_4.0.2
[52] sandwich_3.0-0 TMB_1.7.19 yaml_2.2.1
[55] curl_4.3 sass_0.3.1 stringi_1.7.4
[58] highr_0.8 foreach_1.5.1 boot_1.3-26
[61] zip_2.1.1 R2admb_0.7.16.2 rlang_0.4.10
[64] pkgconfig_2.0.3 evaluate_0.14 lattice_0.20-41
[67] labeling_0.4.2 tidyselect_1.1.1 plyr_1.8.6
[70] magrittr_2.0.1 bookdown_0.21.6 R6_2.5.0
[73] generics_0.1.0 DBI_1.1.1 pillar_1.6.1
[76] haven_2.3.1 whisker_0.4 foreign_0.8-81
[79] withr_2.4.1 abind_1.4-5 modelr_0.1.8
[82] crayon_1.4.1 utf8_1.1.4 rmarkdown_2.8
[85] grid_4.0.2 readxl_1.3.1 data.table_1.14.0
[88] git2r_0.28.0 reprex_2.0.0 digest_0.6.27
[91] xtable_1.8-4 httpuv_1.5.5 numDeriv_2016.8-1.1
[94] munsell_0.5.0 bslib_0.2.4