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Introduction

  • Analysis of temporal evolution of soil parameters along time.

  • 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") %>% filter(fecha != "2018-12-11") %>% 
    mutate(zona = as.factor(zona)) %>% mutate(meses = as.factor(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
   0        24          24
   22       25          25
   29       24          24
# sss <- soil %>% dplyr::select(meses, pastoreo, ca_percent)

Modelize

  • For each response variable, the approach modelling is

\(Y \sim pastoreo (Browsing|NoBrowsing)+ Fecha(0|22|29) + zona \times Fecha\)

  • using the “(1|pastoreo:geo_parcela_nombre)” as nested random effects

Humedad

humedad ~ pastoreo * meses + (1 | pastoreo:geo_parcela_nombre)
Type III Analysis of Variance Table with Satterthwaite's method
               Sum Sq Mean Sq NumDF   DenDF F value    Pr(>F)    
pastoreo         4.60   4.597     1   6.005  0.6233 0.4598622    
meses          362.22 181.110     2 133.044 24.5534 8.395e-10 ***
pastoreo:meses 132.90  66.452     2 133.044  9.0090 0.0002141 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Post-hoc

meses estimate SE df t.ratio p.value
0 - 22 3.6611 0.5490 133.0276 6.6684 0.0000
0 - 29 0.8268 0.5575 133.0164 1.4830 0.3023
22 - 29 -2.8343 0.5524 133.0747 -5.1307 0.0000
meses pastoreo contrast estimate SE df t.ratio p.value
0 . No Browsing - Browsing 1.4692 1.4030 9.5543 1.0472 0.9333
22 . No Browsing - Browsing -3.1741 1.3946 9.3270 -2.2760 0.2908
29 . No Browsing - Browsing -1.2509 1.4080 9.6864 -0.8884 0.9706
. Browsing 22 - 0 -1.3395 0.7764 133.0287 -1.7251 0.4705
. Browsing 29 - 22 1.8727 0.7860 133.1214 2.3826 0.1232
. No Browsing 22 - 0 -5.9827 0.7764 133.0266 -7.7054 0.0000
. No Browsing 29 - 22 3.7959 0.7764 133.0266 4.8889 0.0000

CIC

cic ~ pastoreo * meses + (1 | pastoreo:geo_parcela_nombre)
Type III Analysis of Variance Table with Satterthwaite's method
               Sum Sq Mean Sq NumDF   DenDF F value Pr(>F)    
pastoreo         4.21   4.210     1   5.979  1.3273 0.2933    
meses          435.43 217.716     2 133.999 68.6311 <2e-16 ***
pastoreo:meses   2.97   1.485     2 133.999  0.4682 0.6271    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Post-hoc

meses estimate SE df t.ratio p.value
0 - 22 0.325 0.3600 134.0292 0.9028 0.6395
0 - 29 -3.500 0.3636 134.0000 -9.6269 0.0000
22 - 29 -3.825 0.3600 134.0292 -10.6238 0.0000
meses pastoreo contrast estimate SE df t.ratio p.value
0 . No Browsing - Browsing -0.5000 0.8904 9.9201 -0.5616 0.9979
22 . No Browsing - Browsing -1.1280 0.8846 9.6682 -1.2751 0.8425
29 . No Browsing - Browsing -1.0833 0.8904 9.9201 -1.2167 0.8688
. Browsing 22 - 0 -0.0111 0.5092 134.0292 -0.0217 1.0000
. Browsing 29 - 22 3.8027 0.5092 134.0292 7.4683 0.0000
. No Browsing 22 - 0 -0.6390 0.5092 134.0292 -1.2550 0.8107
. No Browsing 29 - 22 3.8474 0.5092 134.0292 7.5560 0.0000

C

c_percent ~ pastoreo * meses + (1 | pastoreo:geo_parcela_nombre)
Type III Analysis of Variance Table with Satterthwaite's method
                Sum Sq Mean Sq NumDF   DenDF F value    Pr(>F)    
pastoreo        2.9927  2.9927     1   5.995  1.5033 0.2661352    
meses          30.1228 15.0614     2 134.001  7.5658 0.0007706 ***
pastoreo:meses  0.5280  0.2640     2 134.001  0.1326 0.8759091    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Post-hoc

meses estimate SE df t.ratio p.value
0 - 22 1.0925 0.2852 134.0095 3.8303 0.0006
0 - 29 0.7256 0.2880 134.0000 2.5194 0.0343
22 - 29 -0.3669 0.2852 134.0095 -1.2863 0.4053
meses pastoreo contrast estimate SE df t.ratio p.value
0 . No Browsing - Browsing -1.4583 1.1364 7.1824 -1.2833 0.8525
22 . No Browsing - Browsing -1.1712 1.1336 7.1116 -1.0332 0.9427
29 . No Browsing - Browsing -1.3662 1.1364 7.1824 -1.2022 0.8868
. Browsing 22 - 0 -1.2360 0.4034 134.0095 -3.0643 0.0183
. Browsing 29 - 22 0.4644 0.4034 134.0095 1.1513 0.8686
. No Browsing 22 - 0 -0.9489 0.4034 134.0095 -2.3525 0.1325
. No Browsing 29 - 22 0.2694 0.4034 134.0095 0.6679 0.9928

Fe

fe_percent ~ pastoreo * meses + (1 | pastoreo:geo_parcela_nombre)
Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]
Mixed Model Anova Table (Type 3 tests, KR-method)

Model: fe_percent ~ pastoreo * meses + (1 | pastoreo:geo_parcela_nombre)
Data: df_model
               num Df den Df        F  Pr(>F)    
pastoreo            1      6   0.3182 0.59314    
meses               2    134 195.2049 < 2e-16 ***
pastoreo:meses      2    134   3.3444 0.03825 *  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Post-hoc

meses estimate SE df z.ratio p.value
0 - 22 0.0451 0.0103 Inf 4.3856 0
0 - 29 0.1858 0.0092 Inf 20.2649 0
22 - 29 0.1406 0.0085 Inf 16.5696 0
meses pastoreo contrast estimate SE df z.ratio p.value
0 . No Browsing - Browsing -0.0998 0.0830 Inf -1.2019 0.8386
22 . No Browsing - Browsing -0.0552 0.0828 Inf -0.6671 0.9927
29 . No Browsing - Browsing -0.0941 0.0822 Inf -1.1443 0.8696
. Browsing 22 - 0 -0.0674 0.0148 Inf -4.5577 0.0000
. Browsing 29 - 22 -0.1212 0.0121 Inf -10.0407 0.0000
. No Browsing 22 - 0 -0.0228 0.0143 Inf -1.5959 0.5594
. No Browsing 29 - 22 -0.1601 0.0119 Inf -13.4113 0.0000

K

k_percent ~ pastoreo * meses + (1 | pastoreo:geo_parcela_nombre)
Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]
Mixed Model Anova Table (Type 3 tests, KR-method)

Model: k_percent ~ pastoreo * meses + (1 | pastoreo:geo_parcela_nombre)
Data: df_model
               num Df den Df        F    Pr(>F)    
pastoreo            1   6.00   3.5557  0.108303    
meses               2 134.01 477.4712 < 2.2e-16 ***
pastoreo:meses      2 134.01   6.3832  0.002249 ** 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Post-hoc

meses estimate SE df z.ratio p.value
0 - 22 0.5391 0.1111 Inf 4.8522 0
0 - 29 1.7591 0.0935 Inf 18.8131 0
22 - 29 1.2200 0.0725 Inf 16.8366 0
meses pastoreo contrast estimate SE df z.ratio p.value
0 . No Browsing - Browsing -1.6632 0.2534 Inf -6.5632 0.0000
22 . No Browsing - Browsing -0.3766 0.2246 Inf -1.6770 0.4972
29 . No Browsing - Browsing -0.1544 0.1900 Inf -0.8125 0.9770
. Browsing 22 - 0 -1.1824 0.1867 Inf -6.3323 0.0000
. Browsing 29 - 22 -1.3311 0.1096 Inf -12.1412 0.0000
. No Browsing 22 - 0 0.1042 0.1204 Inf 0.8654 0.9674
. No Browsing 29 - 22 -1.1089 0.0947 Inf -11.7063 0.0000

Mg

mg_percent ~ pastoreo * meses + (1 | pastoreo:geo_parcela_nombre)
Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]
Mixed Model Anova Table (Type 3 tests, KR-method)

Model: mg_percent ~ pastoreo * meses + (1 | pastoreo:geo_parcela_nombre)
Data: df_model
               num Df den Df      F   Pr(>F)   
pastoreo            1      6 0.7514 0.419348   
meses               2    134 3.1375 0.046594 * 
pastoreo:meses      2    134 4.7869 0.009817 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Post-hoc

meses estimate SE df z.ratio p.value
0 - 22 -0.0295 0.0403 Inf -0.7309 0.7451
0 - 29 0.0742 0.0376 Inf 1.9735 0.1188
22 - 29 0.1037 0.0377 Inf 2.7478 0.0165
meses pastoreo contrast estimate SE df z.ratio p.value
0 . No Browsing - Browsing -0.3266 0.2138 Inf -1.5275 0.6124
22 . No Browsing - Browsing -0.1644 0.2139 Inf -0.7685 0.9832
29 . No Browsing - Browsing -0.0761 0.2120 Inf -0.3591 0.9999
. Browsing 22 - 0 -0.0516 0.0659 Inf -0.7831 0.9813
. Browsing 29 - 22 -0.1478 0.0576 Inf -2.5634 0.0703
. No Browsing 22 - 0 0.1106 0.0465 Inf 2.3798 0.1151
. No Browsing 29 - 22 -0.0596 0.0487 Inf -1.2234 0.8262

C/N

c_n ~ pastoreo * meses + (1 | pastoreo:geo_parcela_nombre)
Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]
Mixed Model Anova Table (Type 3 tests, KR-method)

Model: c_n ~ pastoreo * meses + (1 | pastoreo:geo_parcela_nombre)
Data: df_model
               num Df   den Df       F Pr(>F)    
pastoreo            1   6.0002  2.1619 0.1919    
meses               2 133.0248 61.8754 <2e-16 ***
pastoreo:meses      2 133.0248  0.9364 0.3946    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Post-hoc

meses estimate SE df z.ratio p.value
0 - 22 0.0029 0.0030 Inf 0.9680 0.5972
0 - 29 -0.0401 0.0040 Inf -10.0033 0.0000
22 - 29 -0.0431 0.0039 Inf -10.9013 0.0000
meses pastoreo contrast estimate SE df z.ratio p.value
0 . No Browsing - Browsing 0.0088 0.0085 Inf 1.0277 0.9210
22 . No Browsing - Browsing 0.0109 0.0084 Inf 1.2943 0.7820
29 . No Browsing - Browsing 0.0119 0.0099 Inf 1.1917 0.8444
. Browsing 22 - 0 -0.0040 0.0039 Inf -1.0175 0.9247
. Browsing 29 - 22 0.0426 0.0052 Inf 8.2492 0.0000
. No Browsing 22 - 0 -0.0019 0.0046 Inf -0.4045 0.9997
. No Browsing 29 - 22 0.0435 0.0060 Inf 7.2804 0.0000

MO

mo ~ pastoreo * meses + (1 | pastoreo:geo_parcela_nombre)
Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]
Mixed Model Anova Table (Type 3 tests, KR-method)

Model: mo ~ pastoreo * meses + (1 | pastoreo:geo_parcela_nombre)
Data: df_model
               num Df   den Df       F    Pr(>F)    
pastoreo            1   5.9992  0.4239    0.5391    
meses               2 134.0502 20.5449 1.649e-08 ***
pastoreo:meses      2 134.0502  0.0116    0.9885    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Post-hoc

meses estimate SE df z.ratio p.value
0 - 22 -0.0558 0.0162 Inf -3.4368 0.0017
0 - 29 -0.1241 0.0199 Inf -6.2501 0.0000
22 - 29 -0.0684 0.0216 Inf -3.1659 0.0044
meses pastoreo contrast estimate SE df z.ratio p.value
0 . No Browsing - Browsing 0.0148 0.0337 Inf 0.4410 0.9995
22 . No Browsing - Browsing 0.0262 0.0376 Inf 0.6964 0.9905
29 . No Browsing - Browsing 0.0350 0.0441 Inf 0.7930 0.9799
. Browsing 22 - 0 0.0501 0.0221 Inf 2.2684 0.1522
. Browsing 29 - 22 0.0640 0.0290 Inf 2.2053 0.1769
. No Browsing 22 - 0 0.0614 0.0238 Inf 2.5847 0.0663
. No Browsing 29 - 22 0.0727 0.0320 Inf 2.2739 0.1501

pH Agua

p_h_agua_eez ~ pastoreo * meses + (1 | pastoreo:geo_parcela_nombre)
Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]
Mixed Model Anova Table (Type 3 tests, KR-method)

Model: p_h_agua_eez ~ pastoreo * meses + (1 | pastoreo:geo_parcela_nombre)
Data: df_model
               num Df   den Df       F    Pr(>F)    
pastoreo            1   5.9999  0.8325   0.39674    
meses               2 134.0153 23.1121 2.379e-09 ***
pastoreo:meses      2 134.0153  4.7246   0.01041 *  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Post-hoc

meses estimate SE df z.ratio p.value
0 - 22 0.0002 3e-04 Inf 0.7249 0.7487
0 - 29 -0.0020 3e-04 Inf -5.6670 0.0000
22 - 29 -0.0022 3e-04 Inf -6.4407 0.0000
meses pastoreo contrast estimate SE df z.ratio p.value
0 . No Browsing - Browsing 0.0001 0.0011 Inf 0.0564 1.0000
22 . No Browsing - Browsing -0.0004 0.0011 Inf -0.3918 0.9998
29 . No Browsing - Browsing -0.0021 0.0011 Inf -1.8180 0.3941
. Browsing 22 - 0 0.0000 0.0005 Inf 0.0143 1.0000
. Browsing 29 - 22 0.0031 0.0005 Inf 6.2022 0.0000
. No Browsing 22 - 0 -0.0005 0.0005 Inf -1.0401 0.9162
. No Browsing 29 - 22 0.0014 0.0005 Inf 2.8942 0.0263

pH KCl

p_h_k_cl ~ pastoreo * meses + (1 | pastoreo:geo_parcela_nombre)
Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]
Mixed Model Anova Table (Type 3 tests, KR-method)

Model: p_h_k_cl ~ pastoreo * meses + (1 | pastoreo:geo_parcela_nombre)
Data: df_model
               num Df   den Df       F    Pr(>F)    
pastoreo            1   5.9999  0.0072   0.93499    
meses               2 134.0178 17.3528 1.987e-07 ***
pastoreo:meses      2 134.0178  3.0542   0.05046 .  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Post-hoc

meses estimate SE df z.ratio p.value
0 - 22 -0.0005 4e-04 Inf -1.1937 0.4569
0 - 29 -0.0024 4e-04 Inf -5.7629 0.0000
22 - 29 -0.0019 4e-04 Inf -4.6216 0.0000
meses pastoreo contrast estimate SE df z.ratio p.value
0 . No Browsing - Browsing 0.0009 0.0013 Inf 0.6690 0.9926
22 . No Browsing - Browsing 0.0001 0.0013 Inf 0.0618 1.0000
29 . No Browsing - Browsing -0.0013 0.0013 Inf -0.9795 0.9377
. Browsing 22 - 0 0.0009 0.0006 Inf 1.5184 0.6194
. Browsing 29 - 22 0.0026 0.0006 Inf 4.4015 0.0001
. No Browsing 22 - 0 0.0001 0.0006 Inf 0.1738 1.0000
. No Browsing 29 - 22 0.0013 0.0006 Inf 2.1315 0.2096

NH4

  • No data
# A tibble: 1 x 3
# Groups:   meses, fecha [1]
  meses fecha          n
  <fct> <date>     <int>
1 0     2018-12-20    44

NO3

  • No data
# A tibble: 1 x 3
# Groups:   meses, fecha [1]
  meses fecha          n
  <fct> <date>     <int>
1 0     2018-12-20    47

P

p ~ pastoreo * meses + (1 | pastoreo:geo_parcela_nombre)
Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]
Mixed Model Anova Table (Type 3 tests, KR-method)

Model: p ~ pastoreo * meses + (1 | pastoreo:geo_parcela_nombre)
Data: df_model
               num Df   den Df       F    Pr(>F)    
pastoreo            1   5.9996  0.0232    0.8840    
meses               2 134.0368 12.0243 1.574e-05 ***
pastoreo:meses      2 134.0368  1.5180    0.2229    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Post-hoc

meses estimate SE df z.ratio p.value
0 - 22 0.1346 0.0954 Inf 1.4112 0.3350
0 - 29 0.5383 0.1077 Inf 4.9980 0.0000
22 - 29 0.4038 0.1095 Inf 3.6865 0.0007
meses pastoreo contrast estimate SE df z.ratio p.value
0 . No Browsing - Browsing 0.1041 0.1722 Inf 0.6045 0.9960
22 . No Browsing - Browsing 0.1040 0.1767 Inf 0.5889 0.9966
29 . No Browsing - Browsing -0.2714 0.2032 Inf -1.3357 0.7542
. Browsing 22 - 0 -0.1345 0.1383 Inf -0.9728 0.9398
. Browsing 29 - 22 -0.2161 0.1506 Inf -1.4343 0.6834
. No Browsing 22 - 0 -0.1346 0.1313 Inf -1.0250 0.9220
. No Browsing 29 - 22 -0.5915 0.1590 Inf -3.7194 0.0014

N

n_percent ~ pastoreo * meses + (1 | pastoreo:geo_parcela_nombre)
Analysis of Deviance Table (Type II Wald chisquare tests)

Response: n_percent
                 Chisq Df Pr(>Chisq)   
pastoreo        0.0361  1   0.849276   
meses          12.5842  2   0.001851 **
pastoreo:meses  0.4221  2   0.809750   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Post-hoc

meses estimate SE df t.ratio p.value
0 - 22 0.3931 0.1114 137 3.5284 0.0016
0 - 29 0.2217 0.1109 137 1.9989 0.1163
22 - 29 -0.1714 0.1144 137 -1.4980 0.2951
meses pastoreo contrast estimate SE df t.ratio p.value
0 . No Browsing - Browsing 0.0250 0.1612 137 0.1551 1.0000
22 . No Browsing - Browsing 0.0160 0.1711 137 0.0936 1.0000
29 . No Browsing - Browsing -0.1061 0.1697 137 -0.6255 0.9951
. Browsing 22 - 0 -0.3887 0.1578 137 -2.4635 0.1004
. Browsing 29 - 22 0.2325 0.1601 137 1.4518 0.6764
. No Browsing 22 - 0 -0.3976 0.1574 137 -2.5268 0.0852
. No Browsing 29 - 22 0.1103 0.1635 137 0.6748 0.9923

Na

na_percent ~ pastoreo * meses + (1 | pastoreo:geo_parcela_nombre)
Analysis of Deviance Table (Type II Wald chisquare tests)

Response: na_percent
                  Chisq Df Pr(>Chisq)    
pastoreo         3.3148  1  0.0686596 .  
meses          188.1372  2  < 2.2e-16 ***
pastoreo:meses  17.5761  2  0.0001525 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Post-hoc

meses estimate SE df t.ratio p.value
0 - 22 -0.3395 0.0750 138 -4.5284 0
0 - 29 -0.9219 0.0695 138 -13.2591 0
22 - 29 -0.5824 0.0616 138 -9.4576 0
meses pastoreo contrast estimate SE df t.ratio p.value
0 . No Browsing - Browsing 0.6720 0.1683 138 3.9932 0.0007
22 . No Browsing - Browsing 0.1388 0.1555 138 0.8923 0.9622
29 . No Browsing - Browsing 0.1200 0.1447 138 0.8295 0.9746
. Browsing 22 - 0 0.6061 0.1152 138 5.2589 0.0000
. Browsing 29 - 22 0.5918 0.0892 138 6.6373 0.0000
. No Browsing 22 - 0 0.0729 0.0959 138 0.7605 0.9844
. No Browsing 29 - 22 0.5730 0.0849 138 6.7521 0.0000

General Overview

Mean + SE table

Characteristic Browsing No Browsing
0, N = 241 22, N = 251 29, N = 241 0, N = 241 22, N = 251 29, N = 241
humedad 11.39 (0.79) 10.07 (0.54) 11.91 (0.60) 12.86 (0.86) 6.94 (0.47) 10.67 (0.39)
fe_percent 1.72 (0.07) 1.98 (0.09) 2.61 (0.15) 1.89 (0.04) 1.97 (0.04) 2.89 (0.08)
k_percent 0.29 (0.02) 0.43 (0.02) 1.06 (0.05) 0.54 (0.03) 0.51 (0.03) 1.18 (0.04)
mg_percent 1.10 (0.07) 1.19 (0.08) 1.44 (0.10) 1.74 (0.21) 1.42 (0.12) 1.58 (0.16)
na_percent 0.03 (0.00) 0.05 (0.00) 0.08 (0.01) 0.05 (0.00) 0.05 (0.01) 0.09 (0.00)
n_percent 0.28 (0.03) 0.19 (0.01) 0.24 (0.02) 0.30 (0.04) 0.20 (0.02) 0.22 (0.02)
c_percent 8.73 (0.35) 7.46 (0.38) 7.96 (0.45) 7.27 (0.42) 6.31 (0.36) 6.59 (0.38)
c_n 13.59 (0.74) 14.34 (0.58) 8.87 (0.35) 11.88 (0.72) 12.12 (0.38) 7.94 (0.29)
cic 15.58 (0.47) 15.56 (0.35) 19.38 (0.30) 15.08 (0.36) 14.44 (0.42) 18.29 (0.50)
p 4.91 (0.35) 4.28 (0.47) 3.46 (0.38) 5.50 (0.33) 4.80 (0.80) 2.67 (0.25)
mo 6.15 (0.46) 4.68 (0.28) 3.60 (0.24) 5.86 (0.65) 4.24 (0.42) 3.25 (0.32)
p_h_k_cl 7.57 (0.03) 7.51 (0.02) 7.37 (0.03) 7.52 (0.03) 7.51 (0.03) 7.44 (0.03)
p_h_agua_eez 7.92 (0.03) 7.91 (0.03) 7.73 (0.03) 7.91 (0.02) 7.94 (0.02) 7.85 (0.03)

1 Mean (std.error)

Figures

Version Author Date
e063cee ajpelu 2021-09-14

Version Author Date
e063cee ajpelu 2021-09-14

Anovas table

zona
fecha
zona X fecha
Variables F p F p F p
c_n 2.162 0.192 61.875 0.000 0.936 0.395
cic 1.327 0.293 68.631 0.000 0.468 0.627
c_percent 1.503 0.266 7.566 0.001 0.133 0.876
k_percent 3.556 0.108 477.471 0.000 6.383 0.002
humedad 0.623 0.460 24.553 0.000 9.009 0.000
fe_percent 0.318 0.593 195.205 0.000 3.344 0.038
mg_percent 0.751 0.419 3.138 0.047 4.787 0.010
mo 0.424 0.539 20.545 0.000 0.012 0.989
p 0.023 0.884 12.024 0.000 1.518 0.223
p_h_agua_eez 0.832 0.397 23.112 0.000 4.725 0.010
p_h_k_cl 0.007 0.935 17.353 0.000 3.054 0.050
n_percent 0.036 0.849 12.584 0.002 0.422 0.810
na_percent 3.315 0.069 188.137 0.000 17.576 0.000

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] gtsummary_1.4.2    plotrix_3.8-1      kableExtra_1.3.1   car_3.0-10        
 [5] carData_3.0-4      glmmADMB_0.8.3.3   glmmTMB_1.0.2.1    DHARMa_0.3.3.0    
 [9] afex_0.28-1        performance_0.8.0  multcomp_1.4-16    TH.data_1.0-10    
[13] mvtnorm_1.1-1      emmeans_1.5.4      lmerTest_3.1-3     lme4_1.1-27.1     
[17] Matrix_1.3-2       fitdistrplus_1.1-3 survival_3.2-7     MASS_7.3-53       
[21] ggpubr_0.4.0       janitor_2.1.0      here_1.0.1         forcats_0.5.1     
[25] stringr_1.4.0      dplyr_1.0.6        purrr_0.3.4        readr_1.4.0       
[29] tidyr_1.1.3        tibble_3.1.2       ggplot2_3.3.5      tidyverse_1.3.1   
[33] rmdformats_1.0.1   knitr_1.31         workflowr_1.7.0   

loaded via a namespace (and not attached):
  [1] readxl_1.3.1        backports_1.2.1     plyr_1.8.6         
  [4] TMB_1.7.19          splines_4.0.2       digest_0.6.27      
  [7] foreach_1.5.1       htmltools_0.5.2     fansi_0.4.2        
 [10] magrittr_2.0.1      checkmate_2.0.0     openxlsx_4.2.3     
 [13] modelr_0.1.8        sandwich_3.0-0      colorspace_2.0-2   
 [16] rvest_1.0.0         haven_2.3.1         xfun_0.23          
 [19] callr_3.7.0         crayon_1.4.1        jsonlite_1.7.2     
 [22] zoo_1.8-8           iterators_1.0.13    glue_1.4.2         
 [25] gtable_0.3.0        R2admb_0.7.16.2     webshot_0.5.2      
 [28] abind_1.4-5         scales_1.1.1.9000   DBI_1.1.1          
 [31] rstatix_0.6.0       Rcpp_1.0.7          viridisLite_0.4.0  
 [34] xtable_1.8-4        foreign_0.8-81      httr_1.4.2         
 [37] ellipsis_0.3.2      farver_2.1.0        pkgconfig_2.0.3    
 [40] sass_0.3.1          dbplyr_2.1.1        utf8_1.1.4         
 [43] labeling_0.4.2      tidyselect_1.1.1    rlang_0.4.12       
 [46] reshape2_1.4.4      later_1.1.0.1       munsell_0.5.0      
 [49] cellranger_1.1.0    tools_4.0.2         cli_2.5.0          
 [52] generics_0.1.0      broom_0.7.9         evaluate_0.14      
 [55] fastmap_1.1.0       yaml_2.2.1          processx_3.5.1     
 [58] fs_1.5.0            zip_2.1.1           nlme_3.1-152       
 [61] whisker_0.4         formatR_1.8         xml2_1.3.2         
 [64] compiler_4.0.2      pbkrtest_0.5-0.1    rstudioapi_0.13    
 [67] curl_4.3            ggsignif_0.6.0      gt_0.3.0           
 [70] reprex_2.0.0        broom.helpers_1.4.0 bslib_0.2.4        
 [73] stringi_1.7.4       highr_0.8           ps_1.5.0           
 [76] lattice_0.20-41     commonmark_1.7      nloptr_1.2.2.2     
 [79] vctrs_0.3.8         pillar_1.6.1        lifecycle_1.0.1    
 [82] jquerylib_0.1.3     estimability_1.3    data.table_1.14.0  
 [85] insight_0.14.4      httpuv_1.5.5        R6_2.5.1           
 [88] bookdown_0.21.6     promises_1.2.0.1    rio_0.5.16         
 [91] codetools_0.2-18    boot_1.3-26         assertthat_0.2.1   
 [94] rprojroot_2.0.2     withr_2.4.1         parallel_4.0.2     
 [97] hms_1.0.0           grid_4.0.2          coda_0.19-4        
[100] minqa_1.2.4        
 [ reached getOption("max.print") -- omitted 6 entries ]