Last updated: 2023-04-14

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Rmd	97c5d1c	ajpelu	2023-04-14	change a la limón
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Rmd	aab8ee0	ajpelu	2023-04-13	add analysis congreso pastos 2023

1 Introduction

Read data

veg_raw <- readxl::read_excel(here::here("data/datos_sep2023_todos_juntos.xlsx")) |> 
  janitor::clean_names() 

autumn_fire <- lubridate::ymd("2018-12-18")
spring_fire <- lubridate::ymd("2019-05-07")

veg <- veg_raw |> 
  filter(zona != "Quemado sin pastoreo") |> 
  rename(fecha = fecha_muestreos) |> 
  mutate(treat = as.factor(case_when(
    zona == "Quemado con pastoreo" ~ "Ot", 
    TRUE ~ "Pr"
  ))) |> 
  mutate(meses = ifelse(treat == "Ot",
                        as.numeric(as.character(lubridate::interval(autumn_fire, lubridate::ymd(fecha)) %/% months(1))),
                        as.numeric(as.character(lubridate::interval(spring_fire, lubridate::ymd(fecha)) %/% months(1))))) |> 
  mutate(quadrat = as.factor(quadrat)) |> 
  mutate(meses = case_when(
    meses == 0 ~ -1, 
    TRUE ~ meses
  ))

veg_total <- veg |> filter(tipo == "total")
veg_mt <- veg |> filter(tipo == "Macrochloa tenacissima") 
veg_gs <- veg |> filter(tipo == "Genista scorpius") 

# Subset of data (pre/post) 
ad_total <- veg_total |> filter(meses %in% c(-1, 22, 24)) 
ad_mt <-  veg_mt |> filter(meses %in% c(-1, 22, 24)) 
ad_gs <- veg_gs |> filter(meses %in% c(-1, 22, 24))

2 All sps

2.1 Total Cover

2.1.1 Model

f <- as.formula(cob_total ~ s(meses, k=5, bs="cs") + treat)

m <- gamm(f, 
          random = list(quadrat = ~1), 
          data = veg_total, 
          family = nb)


 Maximum number of PQL iterations:  20

# Distribution of Model Family

Predicted Distribution of Residuals

 Distribution Probability
       normal         47%
      tweedie         34%
       cauchy          9%

Predicted Distribution of Response

               Distribution Probability
 neg. binomial (zero-infl.)         62%
              beta-binomial         25%
                exponential          3%

2.1.2 Model validation

Version	Author	Date
5591608	ajpelu	2023-04-13

2.1.3 Coefficients

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	3.1363	0.1006	31.1906	< 0.0001
treatPr	-0.1774	0.1436	-1.2356	0.2175
B. smooth terms	edf	Ref.df	F-value	p-value
s(meses)	3.9313	4.0000	96.0674	< 0.0001

2.1.4 Visualizing effects

Version	Author	Date
5591608	ajpelu	2023-04-13

Version	Author	Date
5591608	ajpelu	2023-04-13

2.1.5 Pre-post (autumn)

Version	Author	Date
5591608	ajpelu	2023-04-13

term	df	sumsq	meansq	statistic	p.value
meses	1	29238.49	29238.49	42.98	0.0000
treat	1	0.44	0.44	0.00	0.9797
meses:treat	1	555.63	555.63	0.82	0.3679
Residuals	124	84364.31	680.36

2.2 Phytovol

2.2.1 Model

f <- as.formula(fitovol ~ s(meses, k=5, bs="cs") + treat)
m <- gamm(f,
          random = list(quadrat = ~1), 
          data = veg_total, 
          family = tw)


 Maximum number of PQL iterations:  20

# Distribution of Model Family

Predicted Distribution of Residuals

 Distribution Probability
       cauchy         41%
      tweedie         31%
       normal         25%

Predicted Distribution of Response

 Distribution Probability
    lognormal         72%
      tweedie         12%
            F          9%

2.2.2 Model validation

Version	Author	Date
5591608	ajpelu	2023-04-13

2.2.3 Coefficients

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	6.0647	0.1952	31.0758	< 0.0001
treatPr	-0.2432	0.2774	-0.8767	0.3813
B. smooth terms	edf	Ref.df	F-value	p-value
s(meses)	3.9171	4.0000	231.9889	< 0.0001

2.2.4 Visualizing effects

Version	Author	Date
5591608	ajpelu	2023-04-13

Version	Author	Date
5591608	ajpelu	2023-04-13

2.2.5 Pre-post (autumn)

Version	Author	Date
5591608	ajpelu	2023-04-13

term	df	sumsq	meansq	statistic	p.value
meses	1	239540101	239540101	43.29	0.0000
treat	1	3904958	3904958	0.71	0.4025
meses:treat	1	867093	867093	0.16	0.6929
Residuals	124	686064856	5532781

3 Macrochloa tenacissima

3.1 Total Cover

3.1.1 Model

f <- as.formula(cob_total ~ s(meses, k=5, bs="cs") + treat)

m <- gamm(f, 
          random = list(quadrat = ~1), 
          data = veg_mt, 
          family = tw)


 Maximum number of PQL iterations:  20

# Distribution of Model Family

Predicted Distribution of Residuals

 Distribution Probability
       normal         53%
      tweedie         28%
       cauchy          9%

Predicted Distribution of Response

               Distribution Probability
                    tweedie         34%
                      gamma         19%
 neg. binomial (zero-infl.)         16%

3.1.2 Model validation

Version	Author	Date
5591608	ajpelu	2023-04-13

3.1.3 Coefficients

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	2.2188	0.4782	4.6397	< 0.0001
treatPr	0.2735	0.6524	0.4192	0.6759
B. smooth terms	edf	Ref.df	F-value	p-value
s(meses)	3.6961	4.0000	33.0268	< 0.0001

3.1.4 Visualizing effects

Version	Author	Date
5591608	ajpelu	2023-04-13

Version	Author	Date
5591608	ajpelu	2023-04-13

3.1.5 Pre-post (autumn)

Version	Author	Date
5591608	ajpelu	2023-04-13

term	df	sumsq	meansq	statistic	p.value
meses	1	9102.97	9102.97	8.12	0.0068
treat	1	344.67	344.67	0.31	0.5822
meses:treat	1	908.20	908.20	0.81	0.3733
Residuals	41	45950.50	1120.74

3.2 Phytovol

3.2.1 Model

f <- as.formula(fitovol ~ s(meses, k=5, bs="cs") + treat)
m <- gamm(f,
          random = list(quadrat = ~1), 
          data = veg_mt, 
          family = tw)


 Maximum number of PQL iterations:  20

# Distribution of Model Family

Predicted Distribution of Residuals

 Distribution Probability
       normal         50%
      tweedie         25%
       cauchy         12%

Predicted Distribution of Response

 Distribution Probability
    lognormal         59%
      tweedie         12%
            F          9%

3.2.2 Model validation

Version	Author	Date
5591608	ajpelu	2023-04-13

3.2.3 Coefficients

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	5.7937	0.5415	10.6995	< 0.0001
treatPr	0.3420	0.7379	0.4635	0.6439
B. smooth terms	edf	Ref.df	F-value	p-value
s(meses)	3.7978	4.0000	75.5143	< 0.0001

3.2.4 Visualizing effects

Version	Author	Date
5591608	ajpelu	2023-04-13

Version	Author	Date
5591608	ajpelu	2023-04-13

3.2.5 Pre-post (autumn)

Version	Author	Date
5591608	ajpelu	2023-04-13

term	df	sumsq	meansq	statistic	p.value
meses	1	137417699	137417699	17.86	0.0001
treat	1	9546071	9546071	1.24	0.2719
meses:treat	1	1792256	1792256	0.23	0.6320
Residuals	41	315547113	7696271

4 Genista scorpius

4.1 Total Cover

4.1.1 Model

f <- as.formula(cob_total ~ s(meses, k=5, bs="cs") + treat)

m <- gamm(f, 
          random = list(quadrat = ~1), 
          data = veg_gs, 
          family = tw)


 Maximum number of PQL iterations:  20

# Distribution of Model Family

Predicted Distribution of Residuals

 Distribution Probability
       normal         38%
      tweedie         34%
       cauchy         28%

Predicted Distribution of Response

 Distribution Probability
      tweedie         41%
  half-cauchy         25%
    lognormal         12%

4.1.2 Model validation

Version	Author	Date
5591608	ajpelu	2023-04-13

4.1.3 Coefficients

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	1.7469	0.2615	6.6809	< 0.0001
treatPr	-0.4106	0.3856	-1.0649	0.2890
B. smooth terms	edf	Ref.df	F-value	p-value
s(meses)	3.8165	4.0000	64.7466	< 0.0001

4.1.4 Visualizing effects

Version	Author	Date
5591608	ajpelu	2023-04-13

Version	Author	Date
5591608	ajpelu	2023-04-13

4.1.5 Pre-post (autumn)

Version	Author	Date
5591608	ajpelu	2023-04-13

term	df	sumsq	meansq	statistic	p.value
meses	1	18954.52	18954.52	46.15	0.0000
treat	1	419.05	419.05	1.02	0.3181
meses:treat	1	335.16	335.16	0.82	0.3714
Residuals	43	17662.40	410.75

4.2 Phytovol

4.2.1 Model

f <- as.formula(fitovol ~ s(meses, k=5, bs="cs") + treat)
m <- gamm(f,
          random = list(quadrat = ~1), 
          data = veg_gs, 
          family = tw)


 Maximum number of PQL iterations:  20

# Distribution of Model Family

Predicted Distribution of Residuals

 Distribution Probability
       cauchy         34%
      tweedie         34%
       normal         31%

Predicted Distribution of Response

 Distribution Probability
    lognormal         59%
      tweedie         12%
            F          9%

4.2.2 Model validation

Version	Author	Date
5591608	ajpelu	2023-04-13

4.2.3 Coefficients

A. parametric coefficients	Estimate	Std. Error	t-value	p-value
(Intercept)	5.1540	0.2480	20.7843	< 0.0001
treatPr	-0.4222	0.3586	-1.1774	0.2413
B. smooth terms	edf	Ref.df	F-value	p-value
s(meses)	3.7404	4.0000	72.7425	< 0.0001

4.2.4 Visualizing effects

Version	Author	Date
5591608	ajpelu	2023-04-13

Version	Author	Date
5591608	ajpelu	2023-04-13

4.2.5 Pre-post (autumn)

Version	Author	Date
5591608	ajpelu	2023-04-13

term	df	sumsq	meansq	statistic	p.value
meses	1	119276185.2	119276185.2	59.21	0.0000
treat	1	155552.4	155552.4	0.08	0.7824
meses:treat	1	641482.1	641482.1	0.32	0.5755
Residuals	43	86619094.1	2014397.5

5 Summary

Parametric terms

Tipo	Variable	term	Estimate	SE	F	p
All species	Total cover	treatOt	3.136	0.101	31.19	< 0.0001
All species	Total cover	treatPr	-0.177	0.144	-1.24	0.2175
All species	Fitovol	treatOt	6.065	0.195	31.08	< 0.0001
All species	Fitovol	treatPr	-0.243	0.277	-0.88	0.3813
Macrochloa tenacissima	Total cover	treatOt	2.219	0.478	4.64	< 0.0001
Macrochloa tenacissima	Total cover	treatPr	0.273	0.652	0.42	0.6759
Macrochloa tenacissima	Fitovol	treatOt	5.794	0.541	10.70	< 0.0001
Macrochloa tenacissima	Fitovol	treatPr	0.342	0.738	0.46	0.6439
Genista scorpius	Total cover	treatOt	1.747	0.261	6.68	< 0.0001
Genista scorpius	Total cover	treatPr	-0.411	0.386	-1.06	0.289
Genista scorpius	Fitovol	treatOt	5.154	0.248	20.78	< 0.0001
Genista scorpius	Fitovol	treatPr	-0.422	0.359	-1.18	0.2413

Smooth terms

tipo	Variable	term	edf	ref.df	F	p
All species	Total cover	s(meses)	3.931	4	96.07	< 0.0001
All species	Fitovol	s(meses)	3.917	4	231.99	< 0.0001
Macrochloa tenacissima	Total cover	s(meses)	3.696	4	33.03	< 0.0001
Macrochloa tenacissima	Fitovol	s(meses)	3.798	4	75.51	< 0.0001
Genista scorpius	Total cover	s(meses)	3.817	4	64.75	< 0.0001
Genista scorpius	Fitovol	s(meses)	3.740	4	72.74	< 0.0001

6 All plots

Version	Author	Date
5591608	ajpelu	2023-04-13

7 Summary table

Table 7.1: **All species**
Characteristic	Ot						Pr
Characteristic	-1, N = 32¹	5, N = 32¹	12, N = 32¹	17, N = 32¹	22, N = 32¹	29, N = 32¹	-1, N = 32¹	7, N = 32¹	12, N = 32¹	18, N = 32¹	24, N = 32¹
cob_total	59.22 ± 5.42	16.22 ± 2.26	16.78 ± 2.41	28.06 ± 3.24	25.97 ± 3.25	32.00 ± 3.54	54.94 ± 5.68	8.47 ± 2.00	21.94 ± 2.58	18.41 ± 2.79	27.50 ± 3.58
fitovol	3,415.15 ± 502.49	261.85 ± 57.16	331.35 ± 76.23	600.53 ± 152.12	612.47 ± 135.99	793.42 ± 204.37	3,600.32 ± 600.10	196.49 ± 63.90	351.24 ± 80.13	438.44 ± 116.67	897.76 ± 245.94
¹ Mean ± std.error

Table 7.2: *Macrochloa tenacissima*
Characteristic	Ot						Pr
Characteristic	-1, N = 11¹	5, N = 9¹	12, N = 10¹	17, N = 10¹	22, N = 10¹	29, N = 10¹	-1, N = 12¹	7, N = 10¹	12, N = 11¹	18, N = 13¹	24, N = 12¹
cob_total	62.37 ± 12.17	8.28 ± 3.09	13.50 ± 3.68	21.60 ± 7.50	24.93 ± 7.93	27.72 ± 8.75	59.17 ± 10.81	17.93 ± 4.59	26.28 ± 5.77	25.25 ± 6.11	37.34 ± 8.06
fitovol	4,811.34 ± 991.38	238.56 ± 125.84	468.60 ± 181.18	921.65 ± 414.61	979.45 ± 347.58	1,389.46 ± 557.97	5,347.08 ± 1,112.59	492.69 ± 139.59	733.13 ± 164.78	904.74 ± 229.29	2,020.42 ± 501.25
¹ Mean ± std.error

Table 7.3: *Genista scorpius*
Characteristic	Ot						Pr
Characteristic	-1, N = 10¹	5, N = 11¹	12, N = 11¹	17, N = 13¹	22, N = 14¹	29, N = 14¹	-1, N = 11¹	7, N = 12¹	12, N = 12¹	18, N = 11¹	24, N = 12¹
cob_total	53.20 ± 9.21	2.75 ± 0.76	5.93 ± 1.65	11.36 ± 3.83	8.79 ± 3.04	13.26 ± 4.16	41.27 ± 8.54	1.95 ± 1.30	1.73 ± 0.57	2.63 ± 0.78	4.24 ± 1.54
fitovol	3,560.00 ± 650.98	19.41 ± 6.53	190.33 ± 61.81	263.38 ± 119.28	240.46 ± 107.63	332.91 ± 174.09	3,184.45 ± 648.48	36.51 ± 33.09	13.77 ± 5.74	32.35 ± 12.24	67.89 ± 29.23
¹ Mean ± std.error

R version 4.2.1 (2022-06-23)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Monterey 12.3.1

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.2-arm64/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] emmeans_1.8.1-1    plotrix_3.8-2      gtsummary_1.7.0    patchwork_1.1.2   
 [5] performance_0.10.3 broom_1.0.1        tidymv_3.3.2       kableExtra_1.3.4  
 [9] itsadug_2.4.1      plotfunctions_1.4  gratia_0.8.1       mgcv_1.8-42       
[13] nlme_3.1-157       janitor_2.1.0      here_1.0.1         forcats_0.5.2     
[17] stringr_1.4.1      dplyr_1.1.1        purrr_1.0.1        readr_2.1.3       
[21] tidyr_1.2.1        tibble_3.2.1       ggplot2_3.4.2      tidyverse_1.3.2   
[25] workflowr_1.7.0   

loaded via a namespace (and not attached):
  [1] readxl_1.4.2            backports_1.4.1         systemfonts_1.0.4      
  [4] plyr_1.8.7              splines_4.2.1           digest_0.6.31          
  [7] htmltools_0.5.4         lmerTest_3.1-3          fansi_1.0.4            
 [10] magrittr_2.0.3          paletteer_1.5.0         googlesheets4_1.0.1    
 [13] tzdb_0.3.0              modelr_0.1.9            svglite_2.1.0          
 [16] timechange_0.2.0        colorspace_2.0-3        ggrepel_0.9.3          
 [19] rvest_1.0.3             haven_2.5.1             xfun_0.35              
 [22] prismatic_1.1.1         callr_3.7.3             crayon_1.5.2           
 [25] jsonlite_1.8.4          lme4_1.1-30             zeallot_0.1.0          
 [28] glue_1.6.2              gtable_0.3.1            gargle_1.2.1           
 [31] MatrixModels_0.5-1      webshot_0.5.4           statsExpressions_1.5.0 
 [34] car_3.1-0               abind_1.4-5             scales_1.2.1           
 [37] mvtnorm_1.1-3           DBI_1.1.3               rstatix_0.7.0          
 [40] Rcpp_1.0.10             viridisLite_0.4.1       xtable_1.8-4           
 [43] tweedie_2.3.5           datawizard_0.7.1        httr_1.4.4             
 [46] RColorBrewer_1.1-3      ellipsis_0.3.2          pkgconfig_2.0.3        
 [49] farver_2.1.1            sass_0.4.5              dbplyr_2.2.1           
 [52] utf8_1.2.3              effectsize_0.8.3        reshape2_1.4.4         
 [55] tidyselect_1.2.0        labeling_0.4.2          rlang_1.1.0            
 [58] later_1.3.0             munsell_0.5.0           cellranger_1.1.0       
 [61] tools_4.2.1             cachem_1.0.6            cli_3.6.1              
 [64] generics_0.1.3          evaluate_0.18           fastmap_1.1.0          
 [67] yaml_2.3.7              rematch2_2.1.2          processx_3.8.0         
 [70] knitr_1.41              fs_1.6.1                randomForest_4.7-1.1   
 [73] pbapply_1.6-0           whisker_0.4.1           mvnfast_0.2.8          
 [76] xml2_1.3.3              correlation_0.8.4       compiler_4.2.1         
 [79] rstudioapi_0.14         ggsignif_0.6.3          gt_0.9.0               
 [82] reprex_2.0.2            broom.helpers_1.13.0    afex_1.2-0             
 [85] bslib_0.4.2             stringi_1.7.8           highr_0.9              
 [88] ps_1.7.4                parameters_0.20.3       lattice_0.20-45        
 [91] Matrix_1.5-1            commonmark_1.8.1        markdown_1.5           
 [94] nloptr_2.0.3            vctrs_0.6.1             pillar_1.9.0           
 [97] lifecycle_1.0.3         jquerylib_0.1.4         estimability_1.4.1     
[100] insight_0.19.1          httpuv_1.6.8            R6_2.5.1               
[103] bookdown_0.30           promises_1.2.0.1        BayesFactor_0.9.12-4.4 
[106] boot_1.3-28             MASS_7.3-58.3           assertthat_0.2.1       
[109] rprojroot_2.0.3         withr_2.5.0             parallel_4.2.1         
[112] bayestestR_0.13.0       hms_1.1.2               grid_4.2.1             
[115] coda_0.19-4             minqa_1.2.4             rmarkdown_2.18         
[118] snakecase_0.11.0        carData_3.0-5           googledrive_2.0.0      
[121] git2r_0.30.1            ggpubr_0.4.0            getPass_0.2-2          
[124] numDeriv_2016.8-1.1     lubridate_1.9.2         ggstatsplot_0.11.0.9000

Comparison fire epoch (Congreso Pastos 2023)