AnĂ¡lisis de la Productividad Primaria
D. S. Fernandez del Viso
4/28/2020
Datos
Los datos se encuentran en el archivo ‘ProdPrim.csv’, y se cargan a R usando la funciĂ³n read_csv.
library(readr)
ProdPrim <- read_csv("ProdPrim.csv", na = "NA")
head(ProdPrim)## # A tibble: 6 x 28
## site_name site_id country landmark state lat long elevation map_mm
## <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Badkhyz BDK Turkme… Serheta… Mary 35.7 62 700 292.
## 2 Beacon H… BCN UK Portsmo… West… 50.9 -0.85 205 858.
## 3 Bridger BRD USA Bozeman Mont… 45.8 -111. 2340 382
## 4 Calabozo CLB Venezu… Calabozo GuĂ¡r… 8.93 -67.4 98 1257
## 5 Canas CNS Costa … Canas Guan… 10.4 -85.1 45 1539.
## 6 Charlevi… CHR1 Austra… Charlev… Quee… -26.4 146. 304 483.
## # … with 19 more variables: meantmin_C <dbl>, meantmax_C <dbl>,
## # biome <chr>, soil_type <chr>, ecoregion <chr>, species <chr>,
## # vegetation <chr>, plot_mgmt <chr>, mgmt_hist <chr>, agb_gm2 <dbl>,
## # bgb_gm2 <dbl>, ANPP_gm2yr <dbl>, BNPP_gm2yr <dbl>, start_date <dbl>,
## # end_date <dbl>, s_interval <chr>, s_period <chr>, m_periods <chr>,
## # reference <chr>GrĂ¡ficas de relaciĂ³n de productividad primaria con atributos geogrĂ¡ficos
GrĂ¡ficas de ANPP versus latitud y elevaciĂ³n.
Latitud
library(ggplot2)
#grĂ¡fica de puntos
NPP_lat <- ggplot(data=ProdPrim, aes(x=lat, y=ANPP_gm2yr)) +
geom_point(pch=19, color="green", size=2) +
labs(x="Latitud", y="ANPP, g/m^2/yr")
NPP_lat
#grĂ¡fica con tendencia
NPP_lat <- ggplot(data=ProdPrim, aes(x=lat, y=ANPP_gm2yr)) +
geom_point(pch=19, color="green", size=2) +
geom_smooth(stat = "smooth", method = "auto") +
labs(x="Latitud", y="ANPP, g/m^2/yr")
NPP_lat
Prueba de hipĂ³tesis - RegresiĂ³n polinomial
library(sjPlot)## Learn more about sjPlot with 'browseVignettes("sjPlot")'.regpoly <- lm(ANPP_gm2yr ~ lat + I(lat^2), data=ProdPrim)
tab_model(regpoly)| Â | ANPP gm 2 yr | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 989.40 | 867.34 – 1111.45 | <0.001 |
| lat | 6.84 | 2.00 – 11.67 | 0.006 |
| lat^2 | -0.31 | -0.41 – -0.22 | <0.001 |
| Observations | 122 | ||
| R2 / R2 adjusted | 0.303 / 0.291 | ||
ElevaciĂ³n
library(ggplot2)
#grĂ¡fica de puntos
NPP_elev <- ggplot(data=ProdPrim, aes(x=elevation, y=ANPP_gm2yr)) +
geom_point(pch=19, color="green", size=2) +
labs(x="ElevaciĂ³n, m.s.n.m.", y="ANPP, g/m^2/yr")
NPP_elev
#grĂ¡fica con tendencia
NPP_elev <- ggplot(data=ProdPrim, aes(x=elevation, y=ANPP_gm2yr)) +
geom_point(pch=19, color="green", size=2) +
geom_smooth(stat = "smooth", method = "auto") +
labs(x="ElevaciĂ³n, m.s.n.m.", y="ANPP, g/m^2/yr")
NPP_elev
Prueba de hipĂ³tesis - RegresiĂ³n
regelev <- lm(ANPP_gm2yr ~ elevation, data=ProdPrim)
tab_model(regelev)| Â | ANPP gm 2 yr | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 862.18 | 717.29 – 1007.07 | <0.001 |
| elevation | -0.17 | -0.34 – -0.00 | 0.044 |
| Observations | 122 | ||
| R2 / R2 adjusted | 0.033 / 0.025 | ||
GrĂ¡ficas de relaciĂ³n de productividad primaria aĂ©rea con variables climĂ¡ticas
GrĂ¡ficas de ANPP versus temperatura (mĂnima, mĂ¡xima, promedio) y precipitaciĂ³n
Temperatura mĂnima
library(ggplot2)
#grĂ¡fica de puntos
NPP_tmin <- ggplot(data=ProdPrim, aes(x=meantmin_C, y=ANPP_gm2yr)) +
geom_point(pch=19, color="blue", size=2) +
labs(x="Temperatura mĂnima media, C", y="ANPP, g/m^2/yr")
NPP_tmin
#grĂ¡fica con tendencia
NPP_tmin <- ggplot(data=ProdPrim, aes(x=meantmin_C, y=ANPP_gm2yr)) +
geom_point(pch=19, color="blue", size=2) +
geom_smooth(stat = "smooth", method = "auto") +
labs(x="Temperatura mĂnima media, C", y="ANPP, g/m^2/yr")
NPP_tmin
Prueba de hipĂ³tesis - RegresiĂ³n
regtmin <- lm(ANPP_gm2yr ~ meantmin_C, data=ProdPrim)
tab_model(regtmin)| Â | ANPP gm 2 yr | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 677.77 | 586.80 – 768.73 | <0.001 |
| meantmin_C | 20.97 | 15.70 – 26.24 | <0.001 |
| Observations | 102 | ||
| R2 / R2 adjusted | 0.384 / 0.378 | ||
Temperatura mĂ¡xima
library(ggplot2)
#grĂ¡fica de puntos
NPP_tmax <- ggplot(data=ProdPrim, aes(x=meantmax_C, y=ANPP_gm2yr)) +
geom_point(pch=19, color="red", size=2) +
labs(x="Temperatura mĂ¡xima media, C", y="ANPP, g/m^2/yr")
NPP_tmax
#grĂ¡fica con tendencia
NPP_tmax <- ggplot(data=ProdPrim, aes(x=meantmax_C, y=ANPP_gm2yr)) +
geom_point(pch=19, color="red", size=2) +
geom_smooth(stat = "smooth", method = "auto") +
labs(x="Temperatura mĂ¡xima media, C", y="ANPP, g/m^2/yr")
NPP_tmax
Prueba de hipĂ³tesis - RegresiĂ³n
regtmax <- lm(ANPP_gm2yr ~ meantmax_C, data=ProdPrim)
tab_model(regtmax)| Â | ANPP gm 2 yr | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 518.46 | 206.43 – 830.50 | 0.001 |
| meantmax_C | 9.85 | -1.91 – 21.61 | 0.100 |
| Observations | 103 | ||
| R2 / R2 adjusted | 0.027 / 0.017 | ||
Temperatura media
CĂ³mo no estĂ¡ reportada, vamos a usar el promedio entre la mĂnima y la mĂ¡xima:
#promedio tmin tmax
library(dplyr)
ProdPrim_tm <- mutate(ProdPrim, tmean=(meantmin_C + meantmax_C)/2)
head(ProdPrim_tm)## # A tibble: 6 x 29
## site_name site_id country landmark state lat long elevation map_mm
## <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Badkhyz BDK Turkme… Serheta… Mary 35.7 62 700 292.
## 2 Beacon H… BCN UK Portsmo… West… 50.9 -0.85 205 858.
## 3 Bridger BRD USA Bozeman Mont… 45.8 -111. 2340 382
## 4 Calabozo CLB Venezu… Calabozo GuĂ¡r… 8.93 -67.4 98 1257
## 5 Canas CNS Costa … Canas Guan… 10.4 -85.1 45 1539.
## 6 Charlevi… CHR1 Austra… Charlev… Quee… -26.4 146. 304 483.
## # … with 20 more variables: meantmin_C <dbl>, meantmax_C <dbl>,
## # biome <chr>, soil_type <chr>, ecoregion <chr>, species <chr>,
## # vegetation <chr>, plot_mgmt <chr>, mgmt_hist <chr>, agb_gm2 <dbl>,
## # bgb_gm2 <dbl>, ANPP_gm2yr <dbl>, BNPP_gm2yr <dbl>, start_date <dbl>,
## # end_date <dbl>, s_interval <chr>, s_period <chr>, m_periods <chr>,
## # reference <chr>, tmean <dbl>NPP vs temperatura media
#grĂ¡fica de puntos
NPP_tmean <- ggplot(data=ProdPrim_tm, aes(x=tmean, y=ANPP_gm2yr)) +
geom_point(pch=19, color="green", size=2) +
labs(x="Temperatura media anual, C", y="ANPP, g/m^2/yr")
NPP_tmean
#grĂ¡fica con tendencia
NPP_tmean <- ggplot(data=ProdPrim_tm, aes(x=tmean, y=ANPP_gm2yr)) +
geom_point(pch=19, color="green", size=2) +
geom_smooth(stat = "smooth", method = "auto") +
labs(x="Temperatura media anual, C", y="ANPP, g/m^2/yr")
NPP_tmean
Prueba de hipĂ³tesis - RegresiĂ³n
regtm <- lm(ANPP_gm2yr ~ tmean, data=ProdPrim_tm)
tab_model(regtm)| Â | ANPP gm 2 yr | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 325.97 | 169.25 – 482.69 | <0.001 |
| tmean | 30.14 | 21.37 – 38.91 | <0.001 |
| Observations | 102 | ||
| R2 / R2 adjusted | 0.318 / 0.311 | ||
PrecipitaciĂ³n
#grĂ¡fica de puntos
NPP_prec <- ggplot(data=ProdPrim, aes(x=map_mm, y=ANPP_gm2yr)) +
geom_point(pch=19, color="blue", size=2) +
labs(x="PrecipitaciĂ³n anual media, mm", y="ANPP, g/m^2/yr")
NPP_prec
#grĂ¡fica con tendencia
NPP_prec <- ggplot(data=ProdPrim, aes(x=map_mm, y=ANPP_gm2yr)) +
geom_point(pch=19, color="blue", size=2) +
geom_smooth(stat = "smooth", method = "auto") +
labs(x="PrecipitaciĂ³n anual media, mm", y="ANPP, g/m^2/yr")
NPP_prec
Prueba de hipĂ³tesis - RegresiĂ³n polinomial
library(sjPlot)
regprec <- lm(ANPP_gm2yr ~ map_mm + I(map_mm^2), data=ProdPrim)
tab_model(regprec)| Â | ANPP gm 2 yr | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 11.58 | -167.27 – 190.43 | 0.898 |
| map_mm | 0.88 | 0.66 – 1.10 | <0.001 |
| map_mm^2 | -0.00 | -0.00 – -0.00 | <0.001 |
| Observations | 120 | ||
| R2 / R2 adjusted | 0.425 / 0.415 | ||
Biomasa y factores ambientales
Latitud
library(ggplot2)
#grĂ¡fica de puntos
biom_lat <- ggplot(data=ProdPrim, aes(x=lat, y=agb_gm2)) +
geom_point(pch=19, color="darkgreen", size=2) +
labs(x="Latitud", y="Biomasa, g/m2")
biom_lat
#grĂ¡fica con tendencia
biom_lat <- ggplot(data=ProdPrim, aes(x=lat, y=agb_gm2)) +
geom_point(pch=19, color="darkgreen", size=2) +
geom_smooth(stat = "smooth", method = "auto") +
labs(x="Latitud", y="Biomasa, g/m2")
biom_lat
Biomasa y temperatura media
#grĂ¡fica de puntos
biom_tm <- ggplot(data=ProdPrim_tm, aes(x=tmean, y=agb_gm2)) +
geom_point(pch=19, color="darkgreen", size=2) +
labs(x="Temperatura media anual, C", y="Biomasa, g/m2")
biom_tm
#grĂ¡fica con tendencia
biom_tm <- ggplot(data=ProdPrim_tm, aes(x=tmean, y=agb_gm2)) +
geom_point(pch=19, color="darkgreen", size=2) +
geom_smooth(stat = "smooth", method = "auto") +
labs(x="Temperatura media anual, C", y="Biomasa, g/m2")
biom_tm
Sequoia sempervirens