In turf-reserves/MPAtools: Tools to simulate and analyze no-take Marine Reserves

title: `r params$title`

knitr::opts_chunk$set(echo = F, fig.height = 3, warning = F, message = F, results = 'asis')

suppressPackageStartupMessages({
  library(ggplot2)
  library(dplyr)
  library(tidyr)
  library(MPAtools)
  library(reshape)
  library(stargazer)
  library(ggExtra)
})

Este documento fue generado por el paquete MPAtools, uno de los entregables del grupo TURFeffect. Los resultados presentados son una version preliminar del software que se esta desarrollando, y no recomendamos la toma de decisiones basado en la informacion aqui presentada

Acentos y caracteres especiales omitidos

Resumen

Leyenda

peces = params$peces
invertebrados = params$invertebrados
pesca = params$pesca
comunidad = params$comunidad
reserva = params$reserva
control = params$control

Dp <- summary(turfeffect(density(peces, comunidad), reserva, control))
Sp <- summary(turfeffect(richness(peces, comunidad), reserva, control))
Bp <- summary(turfeffect(fish_biomass(peces, comunidad), reserva, control))
NT <- summary(turfeffect(trophic(peces, comunidad), reserva, control))

Di <- summary(turfeffect(density(invertebrados, comunidad), reserva, control))

lang <- filter(invertebrados, GeneroEspecie == "Panulirus argus")
Nlang <- summary(turfeffect(density(lang, comunidad), reserva, control))

car <- filter(invertebrados, GeneroEspecie == "Strombus gigas")
Ncar <- summary(turfeffect(density(car, comunidad), reserva, control))

lut <- filter(peces, Genero == "Lutjanus")
Nlut <- summary(turfeffect(density(lut, comunidad), reserva, control))
Blut <- summary(turfeffect(fish_biomass(lut, comunidad), reserva, control))

## Landings section

arribos <- landings(data = pesca, site = coop, type = 'kg')

arrG <- arribos %>%
  group_by(Ano) %>%
  summarize(Peso = sum(Peso)/1000)

modG <- lm(Peso ~ Ano, arrG)

arrM <- arribos %>%
  filter(NombreCientifico == "Epinephelus spp") %>%
  mutate(Peso = Peso/1000)

modM <- lm(Peso ~ Ano, arrM)

arrP <- arribos %>%
  filter(NombreCientifico == "Lutjanus aratus" |
           NombreCientifico == "Lutjanus cyanopterus" |
           NombreCientifico == "Lujanus griseus" |
           NombreCientifico == "Lutjanus spp." |
           NombreCientifico == "Lutjanus viridis" |
           NombreCientifico == "Lutjanus spp") %>%
  mutate(Peso = Peso /1000)

modP <- lm(Peso ~ Ano, arrP)

arrL <- arribos %>%
  filter(NombreCientifico == "Panulirus argus") %>%
  mutate(Peso = Peso /1000)

modL <- lm(Peso ~ Ano, arrL)

# Landings and price

ingresos <- landings(data = pesca, site =coop, type = 'price')

ingG <- ingresos %>%
  group_by(Ano) %>%
  summarize(Valor = sum(Precio)/1000)

modG2 <- lm(Valor ~ Ano, ingG)

ingM <- ingresos %>%
  filter(NombreCientifico == "Epinephelus spp")

modM2 <- lm(Precio ~ Ano, ingM)

ingP <- ingresos %>%
  filter(NombreCientifico == "Lutjanus aratus" |
           NombreCientifico == "Lutjanus cyanopterus" |
           NombreCientifico == "Lujanus griseus" |
           NombreCientifico == "Lutjanus spp." |
           NombreCientifico == "Lutjanus viridis" |
           NombreCientifico == "Lutjanus spp") %>%
  mutate(Precio = Precio /1000)

modP2 <- lm(Precio ~ Ano, ingP)

ingL <- ingresos %>%
  filter(NombreCientifico == "Panulirus argus") %>%
  mutate(Precio = Precio /1000)

modL2 <- lm(Precio ~ Ano, ingL)

##

summary <- list(Bio = list(P = list(Dp = score(x = data.frame(est = coefficients(Dp)[4],
                                                              p = coefficients(Dp)[16])),

                                    Sp = score(x = data.frame(est = coefficients(Sp)[4],
                                                              p = coefficients(Sp)[16])),

                                    Bp = score(x = data.frame(est = coefficients(Bp)[4],
                                                              p = coefficients(Bp)[16])),

                                    NT = score(x = data.frame(est = coefficients(NT)[4],
                                                              p = coefficients(NT)[16]))),

                           I = list(Di = score(x = data.frame(est = coefficients(Di)[4],
                                                              p = coefficients(Di)[16]))),

                           O = list(L = score(x = data.frame(est = coefficients(Nlang)[4],
                                                             p = coefficients(Nlang)[16])),

                                    C = score(x = data.frame(est = coefficients(Ncar)[4],
                                                             p = coefficients(Ncar)[16])),

                                    P = score(x = data.frame(est = coefficients(Nlut)[4],
                                                             p = coefficients(Nlut)[16])),

                                    Pb = score(x = data.frame(est = coefficients(Blut)[4],
                                                              p = coefficients(Blut)[16])))),

                Soc = list(L = list(Lg = score(x = data.frame(est = coefficients(summary(modG))[2],
                                                              p = coefficients(summary(modG))[8])),

                                    Lm = score(x = data.frame(est = coefficients(summary(modM))[2],
                                                              p = coefficients(summary(modM))[8])),

                                    Lp = score(x = data.frame(est = coefficients(summary(modL))[2],
                                                              p = coefficients(summary(modM))[8])),

                                    Ll = score(x = data.frame(est = coefficients(summary(modM))[2],
                                                              p = coefficients(summary(modM))[8]))),
                           V = list(Vg = score(x = data.frame(est = coefficients(summary(modG2))[2],
                                                              p = coefficients(summary(modG2))[8])),

                                    Vm = score(x = data.frame(est = coefficients(summary(modM2))[2],
                                                              p = coefficients(summary(modM2))[8])),

                                    Vp = score(x = data.frame(est = coefficients(summary(modL2))[2],
                                                              p = coefficients(summary(modM2))[8])),

                                    Vl = score(x = data.frame(est = coefficients(summary(modM2))[2],
                                                              p = coefficients(summary(modM2))[8])))),

                Gov = list(1))

Cat | Indicador | Valor |Cat | Indicador | Valor | -------|-------------------------|----------------------------------------------|-------|-------------------------|----------------------------------------------| Bio | Densidad Peces | r knitr::include_graphics(summary$Bio$P$Dp)| Soc | Arribos totales | r knitr::include_graphics(summary$Soc$L$Lg) Bio | Riqueza Peces | r knitr::include_graphics(summary$Bio$P$Sp)| Soc | Arribos de mero | r knitr::include_graphics(summary$Soc$L$Lm) Bio | Biomasa Peces | r knitr::include_graphics(summary$Bio$P$Bp)| Soc | Arribos de pargos | r knitr::include_graphics(summary$Soc$L$Lp) Bio | Nivel Trofico Peces | r knitr::include_graphics(summary$Bio$P$NT)| Soc | Arribos de langosta | r knitr::include_graphics(summary$Soc$L$Ll) Bio | Densidad Iinvertebrados | r knitr::include_graphics(summary$Bio$I$Di)| Soc | Ingresos totales | r knitr::include_graphics(summary$Soc$V$Vg) Bio | Densidad de langostas | r knitr::include_graphics(summary$Bio$O$L) | Soc | Ingresos por mero | r knitr::include_graphics(summary$Soc$V$Vm) Bio | Densidad de caracol rosa| r knitr::include_graphics(summary$Bio$O$C) | Soc | Ingresos por pargos | r knitr::include_graphics(summary$Soc$V$Vp) Bio | Densidad de pargos | r knitr::include_graphics(summary$Bio$O$P) | Soc | Ingresos por langosta | r knitr::include_graphics(summary$Soc$V$Vl) Bio | Biomasa de pargos | r knitr::include_graphics(summary$Bio$O$Pb)| | |

Ecologicos

Peces

Densidad

Np <- density(data = peces, location = comunidad)

mpa_plot3(Np, reserve = reserva, control = control, y.lab = "Densidad (org / m^2)")

stargazer(turfeffect(Np, reserve = reserva, control = control), dep.var.labels = "Densidad (org / transecto)", type = "html", dep.var.caption = "", report = "vc*", single.row = T, omit.stat = c("adj.rsq", "n"), digits = 2, df = F, covariate.labels = c("Ano", "Zona", "**Ano:Zona**", "Constante"), notes = "+p < 0.1, ++p<0.05, +++p<0.001", notes.append = FALSE, star.char = "+")

Riqueza

Sp <- richness(data = peces, location = comunidad)
mpa_plot3(Sp, reserve = reserva, control = control, y.lab = "Riqueza (Sp. / transecto)")

stargazer(turfeffect(Sp, reserve = reserva, control = control), dep.var.labels = "Riqueza (Sp. / transecto)", type = "html", dep.var.caption = "", report = "vc*", single.row = T, omit.stat = c("adj.rsq", "n"), digits = 2, df = F, covariate.labels = c("Ano", "Zona", "**Ano:Zona**", "Constante"), notes = "+p < 0.1, ++p<0.05, +++p<0.001", notes.append = FALSE, star.char = "+")

Biomasa

Bp <- fish_biomass(data = peces, location = comunidad)
mpa_plot3(Bp, reserve = reserva, control = control, y.lab = "Biomasa (Kg / transecto)")

stargazer(turfeffect(Bp, reserve = reserva, control = control), dep.var.labels = "Biomasa (Kg / transecto)", type = "html", dep.var.caption = "", report = "vc*", single.row = T, omit.stat = c("adj.rsq", "n"), digits = 2, df = F, covariate.labels = c("Ano", "Zona", "**Ano:Zona**", "Constante"), notes = "+p < 0.1, ++p<0.05, +++p<0.001", notes.append = FALSE, star.char = "+")

Nivel trofico

NTp <- trophic(data = peces, location = comunidad)
mpa_plot3(NTp, reserve = reserva, control = control, y.lab = "Nivel trofico")

stargazer(turfeffect(NTp, reserve = reserva, control = control), dep.var.labels = "Nivel trofico", type = "html", dep.var.caption = "", report = "vc*", single.row = T, omit.stat = c("adj.rsq", "n"), digits = 2, df = F, covariate.labels = c("Ano", "Zona", "**Ano:Zona**", "Constante"), notes = "+p < 0.1, ++p<0.05, +++p<0.001", notes.append = FALSE, star.char = "+")

Invertebrados

Densidades

Ni <- density(data = invertebrados, location = comunidad) %>%
  filter(Sitio == reserva|
           Sitio == control)

mpa_plot3(Ni, reserve = reserva, control = control, y.lab = "Densidad (org / transecto)")

stargazer(turfeffect(Ni, reserve = reserva, control = control), dep.var.labels = "Densidad (org / transecto)", type = "html", dep.var.caption = "", report = "vc*", single.row = T, omit.stat = c("adj.rsq", "n"), digits = 2, df = F, covariate.labels = c("Ano", "Zona", "**Ano:Zona**", "Constante"), notes = "+p < 0.1, ++p<0.05, +++p<0.001", notes.append = FALSE, star.char = "+")

Especies de interes

Pargos (Lutjanus sp.)

Densidad

lutjanus <- peces %>%
  filter(Genero == "Lutjanus")

Nlut <- density(lutjanus, location = comunidad)

mpa_plot3(Nlut, reserve = reserva, control = control, y.lab = "Densidad de pargos (org / transecto)")

stargazer(turfeffect(Nlut, reserve = reserva, control = control), dep.var.labels = "Densidad (org / transecto)", type = "html", dep.var.caption = "", report = "vc*", single.row = T, omit.stat = c("adj.rsq", "n"), digits = 2, df = F, covariate.labels = c("Ano", "Zona", "**Ano:Zona**", "Constante"), notes = "+p < 0.1, ++p<0.05, +++p<0.001", notes.append = FALSE, star.char = "+")

Biomasa

Blut <- fish_biomass(lutjanus, location = comunidad)

mpa_plot3(Blut, reserve = reserva, control = control, y.lab = "Biomasa de pargos (Kg / transecto)")

stargazer(turfeffect(Blut, reserve = reserva, control = control), dep.var.labels = "Biomasa (Kg / transecto)", type = "html", dep.var.caption = "", report = "vc*", single.row = T, omit.stat = c("adj.rsq", "n"), digits = 2, df = F, covariate.labels = c("Ano", "Zona", "**Ano:Zona**", "Constante"), notes = "+p < 0.1, ++p<0.05, +++p<0.001", notes.append = FALSE, star.char = "+")

Langosta (Panulirus argus)

lang <- filter(invertebrados, GeneroEspecie == "Panulirus argus")

Nlang <- density(lang, location = comunidad)

mpa_plot3(Nlang, reserve = reserva, control = control, y.lab = "Densidad (langostas / transecto)")

stargazer(turfeffect(Nlang, reserve = reserva, control = control), dep.var.labels = "Densidad (org / transecto)", type = "html", dep.var.caption = "", report = "vc*", single.row = T, omit.stat = c("adj.rsq", "n"), digits = 2, df = F, covariate.labels = c("Ano", "Zona", "**Ano:Zona**", "Constante"), notes = "+p < 0.1, ++p<0.05, +++p<0.001", notes.append = FALSE, star.char = "+")

Caracol rosa (Lobatus gigas)

car <- filter(invertebrados, GeneroEspecie == "Strombus gigas")

Ncar <- density(car, location = comunidad)

mpa_plot3(Ncar, reserve = reserva, control = control, y.lab = "Densidad (caracoles / transecto)")

stargazer(turfeffect(Ncar, reserve = reserva, control = control), dep.var.labels = "Densidad (org / transecto)", type = "html", dep.var.caption = "", report = "vc*", single.row = T, omit.stat = c("adj.rsq", "n"), digits = 2, df = F, covariate.labels = c("Ano", "Zona", "**Ano:Zona**", "Constante"), notes = "+p < 0.1, ++p<0.05, +++p<0.001", notes.append = FALSE, star.char = "+")

Socioeconomico

Capturas

# Data and models for this section were calculated at the beggining of the document

Generales

ggplot(arrG, aes(x = Ano, y = Peso)) +
  geom_point() +
  geom_abline(intercept = coef(summary(modG))[1], slope = coef(summary(modG))[2]) +
  theme_bw() +
  labs(x = "Ano", y = "Capturas (T)")

Especies Objetivo

Mero

ggplot(arrM, aes(x = Ano, y = Peso)) +
  geom_point() +
  geom_abline(intercept = coef(summary(modM))[1], slope = coef(summary(modM))[2]) +
  theme_bw() +
  labs(x = "Ano", y = "Capturas (T)")

Pargo

ggplot(arrP, aes(x = Ano, y = Peso)) +
  geom_point() +
  geom_abline(intercept = coef(summary(modP))[1], slope = coef(summary(modP))[2]) +
  theme_bw() +
  labs(x = "Ano", y = "Capturas (T)")

Langosta

ggplot(arrL, aes(x = Ano, y = Peso)) +
  geom_point() +
  geom_abline(intercept = coef(summary(modL))[1], slope = coef(summary(modL))[2]) +
  theme_bw() +
  labs(x = "Ano", y = "Capturas (T)")

Ingresos

# Code for this section is at the begining of the document

Generales

ggplot(ingG, aes(x = Ano, y = Valor)) +
  geom_point()  +
  geom_abline(intercept = coef(summary(modG2))[1], slope = coef(summary(modG2))[2]) +
  theme_bw() +
  labs(x = "Ano", y = "Ingresos (Miles de pesos)")

Especies Objetivo

Mero

ggplot(ingM, aes(x = Ano, y = Precio)) +
  geom_point() +
  geom_abline(intercept = coef(summary(modM2))[1], slope = coef(summary(modM2))[2]) +
  theme_bw() +
  labs(x = "Ano", y = "Ingresos (Miles de pesos)")

Pargo

ggplot(ingP, aes(x = Ano, y = Precio)) +
  geom_point() +
  geom_abline(intercept = coef(summary(modP2))[1], slope = coef(summary(modP2))[2]) +
  theme_bw() +
  labs(x = "Ano", y = "Ingresos (Miles de pesos)")

Langosta

ggplot(ingL, aes(x = Ano, y = Precio)) +
  geom_point() +
  geom_abline(intercept = coef(summary(modL2))[1], slope = coef(summary(modL2))[2]) +
  theme_bw() +
  labs(x = "Ano", y = "Ingresos (Miles de pesos)")

Indice de conocimiento

#knowledge()

Gobernanza

La informacion de gobernanza se presenta con propositos de realizar el ejercicio; de ninguna manera representa una evaluacion que pueda utilizarse para recomendaciones.

Acceso a la pesqueria

gov$Access.to.the.fishery[2]

Numero de pescadores

gov$Number.of.fishers[1]

Reconocimiento legal de la reserva

gov$Legal.recognition.of.reserve[2]

Tipo de reserva

gov$Reserve.type[2]

Grado de pesca ilegal

gov$Illegal.harvesting[1]

gov$Illegal.harvesting[2]

Plan de manejo

gov$Management.plan[1]

gov$Management.plan[2]

Procuracion de la reserva

gov$Reserve.enforcement[2]

Razonamiento para la ubicacion

gov$Reasoning.for.reserve.location[2]

Organizacion

gov$Type.of.fisher.organizations[2]

Nivel inclusividad

gov$Representation[1]

gov$Representation[2]

Referencias

R Core Team (2015). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
JJ Allaire, Joe Cheng, Yihui Xie, Jonathan McPherson, Winston Chang, Jeff Allen, Hadley Wickham, Aron Atkins and Rob Hyndman (2016). rmarkdown: Dynamic Documents for R. R package version 0.9.6. https://CRAN.R-project.org/package=rmarkdown
Hadley Wickham and Romain Francois (2016). dplyr: A Grammar of Data Manipulation. R package version 0.5.0. https://CRAN.R-project.org/package=dplyr
Hadley Wickham (2016). tidyr: Easily Tidy Data with spread() and gather() Functions. R package version 0.6.0. https://CRAN.R-project.org/package=tidyr
Hlavac, Marek (2015). stargazer: Well-Formatted Regression and Summary Statistics Tables. R package version 5.2. http://CRAN.R-project.org/package=stargazer

turf-reserves/MPAtools documentation built on June 1, 2019, 2:54 a.m.

rdrr.io home R language documentation Run R code online

CRAN packages Bioconductor packages R-Forge packages GitHub packages

Note that we can't provide technical support on individual packages. You should contact the package authors for that.

turf-reserves/MPAtools Tools to simulate and analyze no-take Marine Reserves

In turf-reserves/MPAtools: Tools to simulate and analyze no-take Marine Reserves

title: r params$title

Resumen

Ecologicos

Peces

Densidad

Riqueza

Biomasa

Nivel trofico

Invertebrados

Densidades

Especies de interes

Pargos (Lutjanus sp.)

Densidad

Biomasa

Langosta (Panulirus argus)

Caracol rosa (Lobatus gigas)

Socioeconomico

Capturas

Generales

Especies Objetivo

Mero

Pargo

Langosta

Ingresos

Generales

Especies Objetivo

Mero

Pargo

Langosta

Indice de conocimiento

Gobernanza

Acceso a la pesqueria

Numero de pescadores

Reconocimiento legal de la reserva

Tipo de reserva

Grado de pesca ilegal

Plan de manejo

Procuracion de la reserva

Razonamiento para la ubicacion

Organizacion

Nivel inclusividad

Referencias

R Package Documentation

Browse R Packages

We want your feedback!

turf-reserves/MPAtools
Tools to simulate and analyze no-take Marine Reserves

title: `r params$title`