Temporal evolution of veg. parameters by rangos (Spanish)
Antonio J. Pérez-Luque
2022-01-17
Last updated: 2022-01-21
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Knit directory: veg_alcontar/
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Introdución y Objetivos
Analizar la evolución de los parámetros de vegetación a lo largo del tiempo, entre dos tratamientos: Herbivorísmo pírico (HP) y Quemas Prescritas (QP), considerando los diferentes rangos
Usamos solamente datos de la quema de Otoño
Diseño:
- tratamiento: HP y QP
- rangos
- 6 fechas de muestreo
- 32 plots por tratamiento
rango
treat Matorral claro Matorral medio Matorral denso Espartal denso
HP 48 48 48 48
QP 48 48 48 48Análisis estadístico
- Se utilizaron Modelos Mixtos Aditivos Generalizados (GAMM) para evaluar los efectos del tratamiento (Quemas Prescritos vs. Herbivorismo Pírico) sobre la evolución de la cobertura vegetal, el fitovolumen y los índices de diversidad (Shannon, Riqueza).
Riqueza
Modelo
f <- as.formula(
riq ~
s(meses, by = interaction(treat, rango), k = 5, bs = "cs") +
s(meses, by = treat, k = 5, bs = "cs") +
s(meses, by = rango, k = 5, bs = "cs") +
treat * rango
)
fi <- as.formula(
riq ~
s(meses, by = interaction(treat, rango), k = 5, bs = "cs") +
treat * rango
)
fni <- as.formula(
riq ~
s(meses, by = treat, k = 5, bs = "cs") +
s(meses, by = rango, k = 5, bs = "cs") +
treat * rango
)
mfull <- gamm(f,
random = list(quadrat = ~1),
data = veg,
family = poisson,
method = "ML")
Maximum number of PQL iterations: 20 mi <- gamm(fi,
random = list(quadrat = ~1),
data = veg,
family = poisson,
method = "ML")
Maximum number of PQL iterations: 20 mni <- gamm(fni,
random = list(quadrat = ~1),
data = veg,
family = poisson,
method = "ML")
Maximum number of PQL iterations: 20 - Seleccionamos los suavizados, y elegimos el modelo con menor AIC
| model | df | AIC |
|---|---|---|
| mni | 15 | 319.8728 |
| mfull | 23 | 334.4628 |
| mi | 17 | 336.0228 |
# Distribution of Model Family
Predicted Distribution of Residuals
Distribution Probability
normal 66%
tweedie 34%
Predicted Distribution of Response
Distribution Probability
beta-binomial 59%
negative binomial 12%
neg. binomial (zero-infl.) 9%Model validation
| A. parametric coefficients | Estimate | Std. Error | t-value | p-value |
| (Intercept) | 2.3946 | 0.0854 | 28.0294 | < 0.0001 |
| treatQP | -0.2895 | 0.1234 | -2.3454 | 0.0195 |
| rangoMatorral medio | -0.0714 | 0.1214 | -0.5883 | 0.5567 |
| rangoMatorral denso | -0.0806 | 0.1214 | -0.6637 | 0.5073 |
| rangoEspartal denso | -0.2813 | 0.1236 | -2.2766 | 0.0234 |
| treatQP:rangoMatorral medio | 0.0729 | 0.1749 | 0.4169 | 0.6770 |
| treatQP:rangoMatorral denso | 0.0457 | 0.1752 | 0.2607 | 0.7945 |
| treatQP:rangoEspartal denso | -0.0214 | 0.1781 | -0.1201 | 0.9045 |
| B. smooth terms | edf | Ref.df | F-value | p-value |
| s(meses):treatHP | 1.6005 | 4.0000 | 4.1012 | 0.0001 |
| s(meses):treatQP | 3.4564 | 4.0000 | 6.4054 | < 0.0001 |
| s(meses):rangoMatorral claro | 0.0000 | 4.0000 | 0.0000 | 0.9918 |
| s(meses):rangoMatorral medio | 0.0000 | 4.0000 | 0.0000 | 0.7363 |
| s(meses):rangoMatorral denso | 0.0000 | 4.0000 | 0.0000 | 0.5012 |
| s(meses):rangoEspartal denso | 2.7565 | 4.0000 | 7.5048 | < 0.0001 |
Diversidad
Modelo
f <- as.formula(
shan ~
s(meses, by = interaction(treat, rango), k = 5, bs = "cs") +
s(meses, by = treat, k = 5, bs = "cs") +
s(meses, by = rango, k = 5, bs = "cs") +
treat * rango
)
fi <- as.formula(
shan ~
s(meses, by = interaction(treat, rango), k = 5, bs = "cs") +
treat * rango
)
fni <- as.formula(
shan ~
s(meses, by = treat, k = 5, bs = "cs") +
s(meses, by = rango, k = 5, bs = "cs") +
treat * rango
)
mfull <- gamm(f,
random = list(quadrat = ~1),
data = veg,
family = tw,
method = "ML")
Maximum number of PQL iterations: 20 mi <- gamm(fi,
random = list(quadrat = ~1),
data = veg,
family = tw,
method = "ML")
Maximum number of PQL iterations: 20 mni <- gamm(fni,
random = list(quadrat = ~1),
data = veg,
family = tw,
method = "ML")
Maximum number of PQL iterations: 20 - Seleccionamos los suavizados, y elegimos el modelo con menor AIC
| model | df | AIC |
|---|---|---|
| mni | 16 | 312.3255 |
| mi | 18 | 326.0420 |
| mfull | 24 | 329.8898 |
# Distribution of Model Family
Predicted Distribution of Residuals
Distribution Probability
normal 62%
tweedie 19%
beta 12%
Predicted Distribution of Response
Distribution Probability
tweedie 47%
weibull 25%
beta 12%Model validation
| A. parametric coefficients | Estimate | Std. Error | t-value | p-value |
| (Intercept) | 0.5287 | 0.1110 | 4.7639 | < 0.0001 |
| treatQP | -0.1583 | 0.1573 | -1.0059 | 0.3151 |
| rangoMatorral medio | -0.2605 | 0.1576 | -1.6529 | 0.0992 |
| rangoMatorral denso | -0.2817 | 0.1576 | -1.7872 | 0.0747 |
| rangoEspartal denso | -0.7181 | 0.1590 | -4.5167 | < 0.0001 |
| treatQP:rangoMatorral medio | 0.2017 | 0.2231 | 0.9042 | 0.3665 |
| treatQP:rangoMatorral denso | 0.1236 | 0.2233 | 0.5535 | 0.5803 |
| treatQP:rangoEspartal denso | -0.2180 | 0.2258 | -0.9652 | 0.3351 |
| B. smooth terms | edf | Ref.df | F-value | p-value |
| s(meses):treatHP | 2.3165 | 4.0000 | 2.1678 | 0.0131 |
| s(meses):treatQP | 2.2922 | 4.0000 | 3.6264 | 0.0006 |
| s(meses):rangoMatorral claro | 0.0000 | 4.0000 | 0.0000 | 0.5747 |
| s(meses):rangoMatorral medio | 0.0000 | 4.0000 | 0.0000 | 0.5277 |
| s(meses):rangoMatorral denso | 1.8420 | 4.0000 | 2.6999 | 0.0022 |
| s(meses):rangoEspartal denso | 3.0818 | 4.0000 | 20.0725 | < 0.0001 |
Post-hoc
Wald test
-----
mod_full: shan ~ s(meses, by = treat, k = 5, bs = "cs") + s(meses, by = rango,
k = 5, bs = "cs") + treat * rango
Parametric effects:
(Intercept) treatQP
1.72097226 -0.22864459
rangoMatorral medio rangoMatorral denso
-0.35503370 -0.38829596
rangoEspartal denso treatQP:rangoMatorral medio
-0.78153985 0.27022479
treatQP:rangoMatorral denso treatQP:rangoEspartal denso
0.20484514 -0.02517138
Null hypothesis = 0
Comparing HP.Matorral claro with QP.Espartal denso:
X2(1.000) = 139.048, p < 2e-16 ***
Comparing QP.Matorral claro with QP.Espartal denso:
X2(1.000) = 84.416, p < 2e-16 ***
Comparing HP.Matorral claro with HP.Espartal denso:
X2(1.000) = 79.230, p < 2e-16 ***
Comparing QP.Matorral medio with QP.Espartal denso:
X2(1.000) = 67.599, p = 2.00e-16 ***
Comparing HP.Matorral medio with QP.Espartal denso:
X2(1.000) = 60.036, p = 9.31e-15 ***
Comparing HP.Matorral denso with QP.Espartal denso:
X2(1.000) = 54.309, p = 1.71e-13 ***
Comparing QP.Matorral denso with QP.Espartal denso:
X2(1.000) = 50.388, p = 1.26e-12 ***
Comparing QP.Matorral claro with HP.Espartal denso:
X2(1.000) = 39.653, p = 3.03e-10 ***
Comparing QP.Matorral medio with HP.Espartal denso:
X2(1.000) = 28.421, p = 9.76e-08 ***
Comparing HP.Matorral medio with HP.Espartal denso:
X2(1.000) = 23.596, p = 1.19e-06 ***
Comparing HP.Matorral claro with QP.Matorral denso:
X2(1.000) = 22.028, p = 2.69e-06 ***
Comparing HP.Matorral denso with HP.Espartal denso:
X2(1.000) = 20.059, p = 7.51e-06 ***
Comparing HP.Matorral claro with HP.Matorral denso:
X2(1.000) = 19.557, p = 9.76e-06 ***
Comparing QP.Matorral denso with HP.Espartal denso:
X2(1.000) = 17.705, p = 2.58e-05 ***
Comparing HP.Matorral claro with HP.Matorral medio:
X2(1.000) = 16.350, p = 5.26e-05 ***
Comparing HP.Matorral claro with QP.Matorral medio:
X2(1.000) = 12.745, p = 3.57e-04 ***
Comparing HP.Espartal denso with QP.Espartal denso:
X2(1.000) = 8.356, p = 0.004 **
Comparing HP.Matorral claro with QP.Matorral claro:
X2(1.000) = 6.781, p = 0.009 **
Comparing QP.Matorral claro with QP.Matorral denso:
X2(1.000) = 4.365, p = 0.037 *
Comparing QP.Matorral claro with HP.Matorral denso:
X2(1.000) = 3.306, p = 0.069 .
Comparing QP.Matorral claro with HP.Matorral medio:
X2(1.000) = 2.072, p = 0.150
Comparing QP.Matorral medio with QP.Matorral denso:
X2(1.000) = 1.262, p = 0.261
Comparing QP.Matorral claro with QP.Matorral medio:
X2(1.000) = 0.933, p = 0.334
Comparing QP.Matorral medio with HP.Matorral denso:
X2(1.000) = 0.727, p = 0.394
Comparing HP.Matorral medio with QP.Matorral denso:
X2(1.000) = 0.422, p = 0.516
Comparing HP.Matorral medio with HP.Matorral denso:
X2(1.000) = 0.144, p = 0.705
Comparing HP.Matorral denso with QP.Matorral denso:
X2(1.000) = 0.073, p = 0.786
Comparing HP.Matorral medio with QP.Matorral medio:
X2(1.000) = 0.224, p = 0.636
Wald test
-----
mod_rangos: shan ~ s(meses, by = treat, k = 5, bs = "cs") + s(meses, by = rango,
k = 5, bs = "cs") + rango
Parametric effects:
(Intercept) rangoMatorral medio rangoMatorral denso rangoEspartal denso
1.6066500 -0.2199213 -0.2858734 -0.7941255
Null hypothesis = 0
Comparing Matorral claro with Espartal denso:
X2(1.000) = 158.705, p < 2e-16 ***
Comparing Matorral medio with Espartal denso:
X2(1.000) = 82.974, p < 2e-16 ***
Comparing Matorral denso with Espartal denso:
X2(1.000) = 65.008, p = 7.46e-16 ***
Comparing Matorral claro with Matorral denso:
X2(1.000) = 20.566, p = 5.76e-06 ***
Comparing Matorral claro with Matorral medio:
X2(1.000) = 12.172, p = 4.85e-04 ***
Comparing Matorral medio with Matorral denso:
X2(1.000) = 1.095, p = 0.295 Plot
Cobertura
Modelo
f <- as.formula(
cob ~
s(meses, by = interaction(treat, rango), k = 5, bs = "cs") +
s(meses, by = treat, k = 5, bs = "cs") +
s(meses, by = rango, k = 5, bs = "cs") +
treat * rango
)
fi <- as.formula(
cob ~
s(meses, by = interaction(treat, rango), k = 5, bs = "cs") +
treat * rango
)
fni <- as.formula(
cob ~
s(meses, by = treat, k = 5, bs = "cs") +
s(meses, by = rango, k = 5, bs = "cs") +
treat * rango
)
mfull <- gamm(f,
random = list(quadrat = ~1),
data = veg,
family = nb,
method = "ML")
Maximum number of PQL iterations: 20 mi <- gamm(fi,
random = list(quadrat = ~1),
data = veg,
family = nb,
method = "ML")
Maximum number of PQL iterations: 20 mni <- gamm(fni,
random = list(quadrat = ~1),
data = veg,
family = nb,
method = "ML")
Maximum number of PQL iterations: 20 - Seleccionamos los suavizados, y elegimos el modelo con menor AIC
| model | df | AIC |
|---|---|---|
| mni | 16 | 553.3551 |
| mfull | 24 | 569.3552 |
| mi | 18 | 604.5710 |
# Distribution of Model Family
Predicted Distribution of Residuals
Distribution Probability
normal 69%
tweedie 28%
beta 3%
Predicted Distribution of Response
Distribution Probability
neg. binomial (zero-infl.) 91%
beta-binomial 9%Model validation
| A. parametric coefficients | Estimate | Std. Error | t-value | p-value |
| (Intercept) | 2.6987 | 0.1387 | 19.4558 | < 0.0001 |
| treatQP | 0.0166 | 0.1962 | 0.0846 | 0.9326 |
| rangoMatorral medio | 0.5852 | 0.1960 | 2.9861 | 0.0030 |
| rangoMatorral denso | 0.5418 | 0.1960 | 2.7644 | 0.0060 |
| rangoEspartal denso | 0.6656 | 0.1960 | 3.3959 | 0.0008 |
| treatQP:rangoMatorral medio | -0.0813 | 0.2772 | -0.2934 | 0.7694 |
| treatQP:rangoMatorral denso | -0.1166 | 0.2773 | -0.4206 | 0.6743 |
| treatQP:rangoEspartal denso | -0.0529 | 0.2772 | -0.1909 | 0.8487 |
| B. smooth terms | edf | Ref.df | F-value | p-value |
| s(meses):treatHP | 3.2434 | 4.0000 | 5.4764 | < 0.0001 |
| s(meses):treatQP | 3.7914 | 4.0000 | 21.8033 | < 0.0001 |
| s(meses):rangoMatorral claro | 0.0000 | 4.0000 | 0.0000 | 0.1724 |
| s(meses):rangoMatorral medio | 1.9142 | 4.0000 | 1.1067 | 0.0882 |
| s(meses):rangoMatorral denso | 3.8000 | 4.0000 | 25.9436 | < 0.0001 |
| s(meses):rangoEspartal denso | 3.8968 | 4.0000 | 33.2093 | < 0.0001 |
Post-hoc
Wald test
-----
mod_full: cob ~ s(meses, by = treat, k = 5, bs = "cs") + s(meses, by = rango,
k = 5, bs = "cs") + treat * rango
Parametric effects:
(Intercept) treatQP
15.2916667 0.8958333
rangoMatorral medio rangoMatorral denso
15.3125000 17.2708333
rangoEspartal denso treatQP:rangoMatorral medio
25.0833333 -3.8541667
treatQP:rangoMatorral denso treatQP:rangoEspartal denso
-1.1875000 -1.2916667
Null hypothesis = 0
Comparing HP.Matorral medio with QP.Matorral medio:
X2(1.000) = 1.313, p = 0.252
Comparing HP.Espartal denso with QP.Espartal denso:
X2(1.000) = 0.024, p = 0.878
Comparing HP.Matorral denso with QP.Matorral denso:
X2(1.000) = 0.013, p = 0.910
Comparing HP.Matorral claro with QP.Matorral claro:
X2(1.000) = 0.120, p = 0.729
Comparing HP.Matorral medio with QP.Matorral denso:
X2(1.000) = 0.417, p = 0.519
Comparing HP.Matorral medio with HP.Matorral denso:
X2(1.000) = 0.575, p = 0.448
Comparing QP.Matorral medio with QP.Matorral denso:
X2(1.000) = 3.208, p = 0.073 .
Comparing QP.Matorral medio with HP.Matorral denso:
X2(1.000) = 3.626, p = 0.057 .
Comparing HP.Matorral denso with QP.Espartal denso:
X2(1.000) = 8.251, p = 0.004 **
Comparing QP.Matorral denso with QP.Espartal denso:
X2(1.000) = 8.912, p = 0.003 **
Comparing HP.Matorral denso with HP.Espartal denso:
X2(1.000) = 9.155, p = 0.002 **
Comparing QP.Matorral denso with HP.Espartal denso:
X2(1.000) = 9.851, p = 0.002 **
Comparing HP.Matorral medio with QP.Espartal denso:
X2(1.000) = 13.183, p = 2.83e-04 ***
Comparing HP.Matorral medio with HP.Espartal denso:
X2(1.000) = 14.320, p = 1.54e-04 ***
Comparing QP.Matorral claro with QP.Matorral medio:
X2(1.000) = 19.693, p = 9.09e-06 ***
Comparing QP.Matorral medio with QP.Espartal denso:
X2(1.000) = 22.816, p = 1.78e-06 ***
Comparing HP.Matorral claro with QP.Matorral medio:
X2(1.000) = 22.893, p = 1.71e-06 ***
Comparing QP.Matorral medio with HP.Espartal denso:
X2(1.000) = 24.304, p = 8.23e-07 ***
Comparing QP.Matorral claro with HP.Matorral medio:
X2(1.000) = 31.175, p = 2.36e-08 ***
Comparing HP.Matorral claro with HP.Matorral medio:
X2(1.000) = 35.169, p = 3.02e-09 ***
Comparing QP.Matorral claro with QP.Matorral denso:
X2(1.000) = 38.799, p = 4.70e-10 ***
Comparing QP.Matorral claro with HP.Matorral denso:
X2(1.000) = 40.219, p = 2.27e-10 ***
Comparing HP.Matorral claro with QP.Matorral denso:
X2(1.000) = 43.242, p = 4.84e-11 ***
Comparing HP.Matorral claro with HP.Matorral denso:
X2(1.000) = 44.740, p = 2.25e-11 ***
Comparing QP.Matorral claro with QP.Espartal denso:
X2(1.000) = 84.902, p < 2e-16 ***
Comparing QP.Matorral claro with HP.Espartal denso:
X2(1.000) = 87.751, p < 2e-16 ***
Comparing HP.Matorral claro with QP.Espartal denso:
X2(1.000) = 91.417, p < 2e-16 ***
Comparing HP.Matorral claro with HP.Espartal denso:
X2(1.000) = 94.372, p < 2e-16 ***
Wald test
-----
mod_rangos: cob ~ s(meses, by = treat, k = 5, bs = "cs") + s(meses, by = rango,
k = 5, bs = "cs") + rango
Parametric effects:
(Intercept) rangoMatorral medio rangoMatorral denso rangoEspartal denso
15.73958 13.38542 16.67708 24.43750
Null hypothesis = 0
Comparing Matorral medio with Matorral denso:
X2(1.000) = 3.186, p = 0.074 .
Comparing Matorral denso with Espartal denso:
X2(1.000) = 17.707, p = 2.58e-05 ***
Comparing Matorral medio with Espartal denso:
X2(1.000) = 35.914, p = 2.06e-09 ***
Comparing Matorral claro with Matorral medio:
X2(1.000) = 52.679, p = 3.93e-13 ***
Comparing Matorral claro with Matorral denso:
X2(1.000) = 81.774, p < 2e-16 ***
Comparing Matorral claro with Espartal denso:
X2(1.000) = 175.585, p < 2e-16 ***Plot
Fitovolumen
Modelo
f <- as.formula(
fitovol ~
s(meses, by = interaction(treat, rango), k = 5, bs = "cs") +
s(meses, by = treat, k = 5, bs = "cs") +
s(meses, by = rango, k = 5, bs = "cs") +
treat * rango
)
fi <- as.formula(
fitovol ~
s(meses, by = interaction(treat, rango), k = 5, bs = "cs") +
treat * rango
)
fni <- as.formula(
fitovol ~
s(meses, by = treat, k = 5, bs = "cs") +
s(meses, by = rango, k = 5, bs = "cs") +
treat * rango
)
mfull <- gamm(f,
random = list(quadrat = ~1),
data = veg,
family = tw,
method = "ML")
Maximum number of PQL iterations: 20 mi <- gamm(fi,
random = list(quadrat = ~1),
data = veg,
family = tw,
method = "ML")
Maximum number of PQL iterations: 20 mni <- gamm(fni,
random = list(quadrat = ~1),
data = veg,
family = tw,
method = "ML")
Maximum number of PQL iterations: 20 - Seleccionamos los suavizados, y elegimos el modelo con menor AIC
| model | df | AIC |
|---|---|---|
| mni | 16 | 903.7188 |
| mfull | 24 | 919.8060 |
| mi | 18 | 958.6921 |
# Distribution of Model Family
Predicted Distribution of Residuals
Distribution Probability
normal 53%
tweedie 47%
Predicted Distribution of Response
Distribution Probability
lognormal 31%
tweedie 22%
F 16%Model validation
| A. parametric coefficients | Estimate | Std. Error | t-value | p-value |
| (Intercept) | 5.0453 | 0.1969 | 25.6224 | < 0.0001 |
| treatQP | 0.0576 | 0.2773 | 0.2075 | 0.8357 |
| rangoMatorral medio | 1.1151 | 0.2685 | 4.1532 | < 0.0001 |
| rangoMatorral denso | 1.2420 | 0.2682 | 4.6310 | < 0.0001 |
| rangoEspartal denso | 1.8986 | 0.2648 | 7.1699 | < 0.0001 |
| treatQP:rangoMatorral medio | -0.2065 | 0.3794 | -0.5443 | 0.5866 |
| treatQP:rangoMatorral denso | -0.2339 | 0.3765 | -0.6212 | 0.5348 |
| treatQP:rangoEspartal denso | -0.2753 | 0.3724 | -0.7391 | 0.4603 |
| B. smooth terms | edf | Ref.df | F-value | p-value |
| s(meses):treatHP | 3.5747 | 4.0000 | 29.5108 | < 0.0001 |
| s(meses):treatQP | 3.7773 | 4.0000 | 42.6866 | < 0.0001 |
| s(meses):rangoMatorral claro | 0.0000 | 4.0000 | 0.0000 | 0.7831 |
| s(meses):rangoMatorral medio | 0.0000 | 4.0000 | 0.0000 | 0.7361 |
| s(meses):rangoMatorral denso | 3.7580 | 4.0000 | 22.1492 | < 0.0001 |
| s(meses):rangoEspartal denso | 3.7961 | 4.0000 | 19.4941 | < 0.0001 |
Post-hoc
Wald test
-----
mod_full: fitovol ~ s(meses, by = treat, k = 5, bs = "cs") + s(meses, by = rango,
k = 5, bs = "cs") + treat * rango
Parametric effects:
(Intercept) treatQP
187.637708 23.487708
rangoMatorral medio rangoMatorral denso
446.031563 963.257500
rangoEspartal denso treatQP:rangoMatorral medio
1850.015833 -82.336771
treatQP:rangoMatorral denso treatQP:rangoEspartal denso
2.029917 -274.912250
Null hypothesis = 0
Comparing HP.Espartal denso with QP.Espartal denso:
X2(1.000) = 3.226, p = 0.072 .
Comparing HP.Matorral medio with QP.Matorral medio:
X2(1.000) = 0.177, p = 0.674
Comparing HP.Matorral claro with QP.Matorral claro:
X2(1.000) = 0.028, p = 0.867
Comparing HP.Matorral denso with QP.Matorral denso:
X2(1.000) = 0.033, p = 0.855
Comparing QP.Matorral claro with QP.Matorral medio:
X2(1.000) = 6.750, p = 0.009 **
Comparing HP.Matorral claro with QP.Matorral medio:
X2(1.000) = 7.650, p = 0.006 **
Comparing QP.Matorral claro with HP.Matorral medio:
X2(1.000) = 9.111, p = 0.003 **
Comparing HP.Matorral claro with HP.Matorral medio:
X2(1.000) = 10.152, p = 0.001 **
Comparing HP.Matorral medio with HP.Matorral denso:
X2(1.000) = 13.651, p = 2.20e-04 ***
Comparing HP.Matorral medio with QP.Matorral denso:
X2(1.000) = 15.031, p = 1.06e-04 ***
Comparing QP.Matorral medio with HP.Matorral denso:
X2(1.000) = 16.934, p = 3.87e-05 ***
Comparing QP.Matorral medio with QP.Matorral denso:
X2(1.000) = 18.468, p = 1.73e-05 ***
Comparing QP.Matorral denso with QP.Espartal denso:
X2(1.000) = 18.976, p = 1.32e-05 ***
Comparing HP.Matorral denso with QP.Espartal denso:
X2(1.000) = 20.597, p = 5.67e-06 ***
Comparing QP.Matorral denso with HP.Espartal denso:
X2(1.000) = 37.849, p = 7.64e-10 ***
Comparing HP.Matorral denso with HP.Espartal denso:
X2(1.000) = 40.125, p = 2.38e-10 ***
Comparing QP.Matorral claro with HP.Matorral denso:
X2(1.000) = 45.066, p = 1.90e-11 ***
Comparing HP.Matorral claro with HP.Matorral denso:
X2(1.000) = 47.347, p = 5.95e-12 ***
Comparing QP.Matorral claro with QP.Matorral denso:
X2(1.000) = 47.547, p = 5.37e-12 ***
Comparing HP.Matorral claro with QP.Matorral denso:
X2(1.000) = 49.889, p = 1.63e-12 ***
Comparing HP.Matorral medio with QP.Espartal denso:
X2(1.000) = 67.785, p < 2e-16 ***
Comparing QP.Matorral medio with QP.Espartal denso:
X2(1.000) = 74.884, p < 2e-16 ***
Comparing HP.Matorral medio with HP.Espartal denso:
X2(1.000) = 100.585, p < 2e-16 ***
Comparing QP.Matorral medio with HP.Espartal denso:
X2(1.000) = 109.194, p < 2e-16 ***
Comparing QP.Matorral claro with QP.Espartal denso:
X2(1.000) = 126.598, p < 2e-16 ***
Comparing HP.Matorral claro with QP.Espartal denso:
X2(1.000) = 130.402, p < 2e-16 ***
Comparing QP.Matorral claro with HP.Espartal denso:
X2(1.000) = 170.240, p < 2e-16 ***
Comparing HP.Matorral claro with HP.Espartal denso:
X2(1.000) = 174.647, p < 2e-16 ***
Wald test
-----
mod_rangos: fitovol ~ s(meses, by = treat, k = 5, bs = "cs") + s(meses, by = rango,
k = 5, bs = "cs") + rango
Parametric effects:
(Intercept) rangoMatorral medio rangoMatorral denso rangoEspartal denso
199.3816 404.8632 964.2725 1712.5597
Null hypothesis = 0
Comparing Matorral claro with Matorral medio:
X2(1.000) = 16.753, p = 4.26e-05 ***
Comparing Matorral medio with Matorral denso:
X2(1.000) = 31.984, p = 1.55e-08 ***
Comparing Matorral denso with Espartal denso:
X2(1.000) = 57.228, p = 3.88e-14 ***
Comparing Matorral claro with Matorral denso:
X2(1.000) = 95.033, p < 2e-16 ***
Comparing Matorral medio with Espartal denso:
X2(1.000) = 174.779, p < 2e-16 ***
Comparing Matorral claro with Espartal denso:
X2(1.000) = 299.755, p < 2e-16 ***Plot
Overall
- Smooth terms
Variable term edf ref.df F p Richness s(meses):treatHP 1.601 4 4.10 < 0.001 Richness s(meses):treatQP 3.456 4 6.40 < 0.001 Richness s(meses):rangoMatorral claro 0.000 4 0.00 0.992 Richness s(meses):rangoMatorral medio 0.000 4 0.00 0.736 Richness s(meses):rangoMatorral denso 0.000 4 0.00 0.501 Richness s(meses):rangoEspartal denso 2.756 4 7.50 < 0.001 Shannon s(meses):treatHP 2.317 4 2.17 0.013 Shannon s(meses):treatQP 2.292 4 3.63 0.001 Shannon s(meses):rangoMatorral claro 0.000 4 0.00 0.575 Shannon s(meses):rangoMatorral medio 0.000 4 0.00 0.528 Shannon s(meses):rangoMatorral denso 1.842 4 2.70 0.002 Shannon s(meses):rangoEspartal denso 3.082 4 20.07 < 0.001 Cobertura total s(meses):treatHP 3.243 4 5.48 < 0.001 Cobertura total s(meses):treatQP 3.791 4 21.80 < 0.001 Cobertura total s(meses):rangoMatorral claro 0.000 4 0.00 0.172 Cobertura total s(meses):rangoMatorral medio 1.914 4 1.11 0.088 Cobertura total s(meses):rangoMatorral denso 3.800 4 25.94 < 0.001 Cobertura total s(meses):rangoEspartal denso 3.897 4 33.21 < 0.001 Fitovolumen s(meses):treatHP 3.575 4 29.51 < 0.001 Fitovolumen s(meses):treatQP 3.777 4 42.69 < 0.001 Fitovolumen s(meses):rangoMatorral claro 0.000 4 0.00 0.783 Fitovolumen s(meses):rangoMatorral medio 0.000 4 0.00 0.736 Fitovolumen s(meses):rangoMatorral denso 3.758 4 22.15 < 0.001 Fitovolumen s(meses):rangoEspartal denso 3.796 4 19.49 < 0.001 - Parametrics terms
| Variable | param. terms | df | F | p.value |
|---|---|---|---|---|
| Richness | treat | 1 | 5.501 | 0.02 |
| Richness | rango | 3 | 1.880 | 0.132 |
| Richness | treat:rango | 3 | 0.116 | 0.951 |
| Shannon | treat | 1 | 1.012 | 0.315 |
| Shannon | rango | 3 | 6.971 | < 0.001 |
| Shannon | treat:rango | 3 | 1.302 | 0.273 |
| Cobertura total | treat | 1 | 0.007 | 0.933 |
| Cobertura total | rango | 3 | 4.781 | 0.003 |
| Cobertura total | treat:rango | 3 | 0.063 | 0.979 |
| Fitovolumen | treat | 1 | 0.043 | 0.836 |
| Fitovolumen | rango | 3 | 17.379 | < 0.001 |
| Fitovolumen | treat:rango | 3 | 0.207 | 0.892 |
| Variable | R2 | AIC | Model distribution |
|---|---|---|---|
| Richness | 0.262 | 319.87 | Poisson |
| Shannon | 0.384 | 312.33 | Tweedie |
| Cobertura total | 0.698 | 553.36 | Negative Binomial |
| Fitovol | 0.839 | 903.72 | Tweedie |
- Post-hoc
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] xlsx_0.6.5 emmeans_1.5.4 plotrix_3.8-1 gtsummary_1.4.2
[5] patchwork_1.1.1 performance_0.8.0 broom_0.7.9 tidymv_3.2.1
[9] kableExtra_1.3.1 itsadug_2.4 plotfunctions_1.4 gratia_0.6.0
[13] mgcv_1.8-33 nlme_3.1-152 janitor_2.1.0 here_1.0.1
[17] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.6 purrr_0.3.4
[21] readr_1.4.0 tidyr_1.1.3 tibble_3.1.2 ggplot2_3.3.5
[25] tidyverse_1.3.1 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] TH.data_1.0-10 colorspace_2.0-2 ellipsis_0.3.2
[4] rprojroot_2.0.2 estimability_1.3 snakecase_0.11.0
[7] fs_1.5.0 rstudioapi_0.13 farver_2.1.0
[10] fansi_0.4.2 mvtnorm_1.1-1 lubridate_1.7.10
[13] xml2_1.3.2 codetools_0.2-18 splines_4.0.2
[16] knitr_1.31 jsonlite_1.7.2 gt_0.3.0
[19] rJava_0.9-13 dbplyr_2.1.1 compiler_4.0.2
[22] httr_1.4.2 backports_1.2.1 assertthat_0.2.1
[25] Matrix_1.3-2 fastmap_1.1.0 cli_2.5.0
[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] Rcpp_1.0.7 cellranger_1.1.0 jquerylib_0.1.3
[37] vctrs_0.3.8 broom.helpers_1.4.0 insight_0.14.4
[40] xfun_0.23 ps_1.5.0 xlsxjars_0.6.1
[43] rvest_1.0.0 lifecycle_1.0.1 getPass_0.2-2
[46] MASS_7.3-53 zoo_1.8-8 scales_1.1.1.9000
[49] ragg_1.1.1 hms_1.0.0 promises_1.2.0.1
[52] sandwich_3.0-0 yaml_2.2.1 mvnfast_0.2.7
[55] sass_0.3.1 stringi_1.7.4 bayestestR_0.9.0
[58] highr_0.8 randomForest_4.6-14 systemfonts_1.0.0
[61] rlang_0.4.12 pkgconfig_2.0.3 evaluate_0.14
[64] lattice_0.20-41 labeling_0.4.2 processx_3.5.1
[67] tidyselect_1.1.1 magrittr_2.0.1 R6_2.5.1
[70] generics_0.1.0 multcomp_1.4-16 DBI_1.1.1
[73] pillar_1.6.1 haven_2.3.1 whisker_0.4
[76] withr_2.4.1 survival_3.2-7 modelr_0.1.8
[79] crayon_1.4.1 utf8_1.1.4 rmarkdown_2.8
[82] grid_4.0.2 readxl_1.3.1 callr_3.7.0
[85] git2r_0.28.0 reprex_2.0.0 digest_0.6.27
[88] webshot_0.5.2 xtable_1.8-4 httpuv_1.5.5
[91] textshaping_0.3.2 munsell_0.5.0 tweedie_2.3.3
[94] viridisLite_0.4.0 bslib_0.2.4