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################## Seguimento da avifauna nun parque eologico #########################
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## 1. Desenho de mostraxe:
#install.packages(dssd)
library(dssd)
##ler un shapefile delimitando a area de estudo e os dous habitats de interese
shapefile.name <- system.file("extdata", "Strata.shp", package = "dssd")
region <- make.region(region.name = "Area de estudo",
strata.name = c("Matos", "Bosque"),
shape = shapefile.name)
plot(region)
##crear 100 puntos aleatorios
cover <- make.coverage(region,
n.grid.points = 100)
plot(region, cover)
##crear 200 Estacions de escoita
#aleatoriamente distribuidas
default.design <- make.design(region = region,
transect.type = "point",
design = "random",
samplers = 200,
design.angle = c(155, 90),
edge.protocol = "minus",
truncation = 500,
coverage.grid = cover)
EEs_random <- generate.transects(default.design)
plot(region, EEs_random, lwd = 1.5, col = 4)
#sistematicamente distribuidas
default.design <- make.design(region = region,
transect.type = "point",
design = "systematic",
samplers = 200,
design.angle = c(155, 90),
edge.protocol = "minus",
truncation = 500,
coverage.grid = cover)
EEs_system <- generate.transects(default.design)
plot(region, EEs_system, lwd = 1.5, col = 4)
EEs_system
##
## Strata Matos:
## ______________
## Design: systematically spaced transects
## Spacing: 10246.14
## Number of samplers: 90
## Design angle: 155
## Edge protocol: minus
## Covered area: 70297238
## Strata coverage: 0.76%
## Strata area: 9280648398
##
## Strata Bosque:
## _______________
## Design: systematically spaced transects
## Spacing: 10246.14
## Number of samplers: 122
## Design angle: 90
## Edge protocol: minus
## Covered area: 95672891
## Strata coverage: 0.82%
## Strata area: 11716012130
##
## Study Area Totals:
## _________________
## Number of samplers: 212
## Covered area: 165970129
## Average coverage: 0.79%
##porcentaxe de cobertura do noso desenho nun grid de puntos
design <- run.coverage(default.design, reps = 100)
## 1% complete 2% complete 3% complete 4% complete 5% complete 6% complete 7%
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plot(design)
design
##
## Strata Matos:
## ______________
## Design: systematically spaced transects
## Spacing: NA
## Number of samplers: 200 (shared across strata)
## Design angle: 155
## Edge protocol: minus
##
## Strata Bosque:
## _______________
## Design: systematically spaced transects
## Spacing: NA
## Number of samplers: 200 (shared across strata)
## Design angle: 90
## Edge protocol: minus
##
## Strata areas: 9280648398, 11716012130
## Region units: m
## Coverage Simulation repetitions: 100
##
## Number of samplers:
##
## Matos Bosque Total
## Minimum 85.0 100.0 185.0
## Mean 88.2 110.9 199.1
## Median 88.0 110.5 199.0
## Maximum 93.0 122.0 214.0
## sd 1.8 5.8 6.2
##
## Covered area:
##
## Matos Bosque Total
## Minimum 66380427 78503934 145197614
## Mean 68912435 86782835 155695269
## Median 68883214 86074314 155215111
## Maximum 72504619 95192020 167494380
## sd 1342976 4591051 4848828
##
## % of region covered:
##
## Matos Bosque Total
## Minimum 0.72 0.67 0.69
## Mean 0.74 0.74 0.74
## Median 0.74 0.73 0.74
## Maximum 0.78 0.81 0.80
## sd 0.01 0.04 0.02
##
## Coverage Score Summary:
##
## Matos Bosque Total
## Minimum 0.000000000 0.000000000 0.000000000
## Mean 0.007954545 0.007017544 0.007425743
## Median 0.010000000 0.000000000 0.010000000
## Maximum 0.040000000 0.030000000 0.040000000
## sd 0.008234794 0.008444126 0.008325078
##crear 20 trasectos lineais de 500 metros
#aleatoriamente
default.design <- make.design(region = region,
transect.type = "line",
design = "random",
samplers = 20,
design.angle = c(155, 90),
edge.protocol = "minus",
truncation = 500,
coverage.grid = cover)
transects_random <- generate.transects(default.design)
plot(region, transects_random, lwd = 1.5, col = 4)
#sistematicamente
default.design <- make.design(region = region,
transect.type = "line",
design = "systematic",
samplers = 20,
design.angle = c(155, 90),
edge.protocol = "minus",
truncation = 500,
coverage.grid = cover)
transects_system <- generate.transects(default.design)
plot(region, transects_system, lwd = 1.5, col = 4)
##crear 20 transectos lineais de 1 km en zizag
design <- make.design(region = region,
transect.type = "line",
design = c("eszigzag", "systematic"),
line.length = 1200000,
design.angle = c(65, 90),
bounding.shape = c("convex.hull", NA),
edge.protocol = "minus",
truncation = 1000,
coverage.grid = cover)
transects_zigzag <- generate.transects(design)
plot(region, transects_zigzag, lwd = 1.5, col = 4)
##porcentaxe de cobertura do noso deseño nun grid de puntos
design <- run.coverage(design, reps = 100)
## 1% complete 2% complete 3% complete 4% complete 5% complete 6% complete 7%
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plot(design)
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###### 2. Calcular abundacias relativas e absolutas a partir das EEs virtuais ######
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#### Abundancia relativa a partir de Estacions de escoita
## leer taboa cos datos das estacions
library(readxl)
EE <- read_excel("/Users/adrianregossanz/Downloads/OneDrive_1_16-4-2020/EE.xlsx")
## como calcular media e erro tipico para unha especie
x <- EE$`Turdus merula`
mean(x)
## [1] 1.25
sd(x)/sqrt(length(x))
## [1] 0.6291529
####### e para todas as especies a un tempo
#creo unha funcion que calcula o erro tipico
mi.SE.funcion <- function(x) {
sd(x)/sqrt(length(x))
}
spp.names <- names(EE)## nomes das especies
#creo unha taboa
my.ab <- as.data.frame(spp.names)
my.ab$media <- apply(EE, 2, mean)##calculo a media
my.ab$erro <- apply(EE, 2, mi.SE.funcion)## e o erro tipico
head(my.ab)
## spp.names media erro
## 1 Turdus merula 1.25 0.6291529
## 2 Erithacus rubecula 1.50 0.2886751
## 3 Corvus corone 0.75 0.4787136
##gardo a taboa coas abundancias relativas
write.csv2(my.ab, "/Users/adrianregossanz/Downloads/Ab_relativa.csv")
##representar valores medios
names(my.ab) <- c("especies", "media", "error")
library(ggplot2)
ggplot(my.ab, aes(x=especies, y=media, fill=especies)) + theme_light() +
geom_bar(position=position_dodge(), stat="identity") +
scale_fill_brewer(palette = "Reds") +
geom_errorbar(aes(ymin=media-error, ymax=media+error),
width=.2, # Width of the error bars
position=position_dodge(.9))
########### Densidade absoluta a partir de transecto lineal
Tran <- read_excel("/Users/adrianregossanz/Downloads/Transecto.xlsx")
abundancia.funcion <- function (data, long, banda){
ConBanda <- colSums(data)[c(TRUE, FALSE)]
ConTotal <- colSums(data)[c(TRUE, FALSE)] + colSums(Tran)[c(FALSE, TRUE)]
nT <- nrow(data)##numero de transectos na taboa equivale o numero de filas
L <- (long*nT)# n e o numero de transecto de 500 m
w <- banda## banda de reconto de 25
K <- (1-sqrt(1-ConBanda/ConTotal))/w
D <- as.data.frame(ConTotal*K/L)*100000
rownames(D) <- sub('*Banda principal', '', row.names(D))
colnames(D) <- "Abundancia"
return(D)
}
## calculamos para transecto de 500 m, e banda de 25 metros
T_ab <- abundancia.funcion(Tran, 500, 25)
##gardo a taboa coas abundancias relativas
write.csv2(T_ab, "/Users/adrianregossanz/Downloads/Ab_absoluta.csv")
########### 3. simulamos cambios nas abundancias
T_ab$Ano2 <- T_ab$Abundancia*c(1.3, 0.3, 0.8)
T_ab$Ano3 <- T_ab$Ano2*c(0.3, 0.8, 1.3)
T_ab$Ano4 <- T_ab$Ano3*c(0.3, 0.8, 1.3)
T_ab$Ano5 <- T_ab$Ano4*c(0.3, 0.8, 1.3)
T_ab$Ano6 <- T_ab$Ano5*c(0.3, 0.8, 1.3)
T_ab$spp <- rownames(T_ab)
## representamos graficamente as tendencias
library(reshape2)
my.tb <- melt(T_ab, id.vars=c("spp"))
my.tb$Ano <- rep(c(2009:2014),each=3)
head(my.tb)
## spp variable value Ano
## 1 Turdus merula Abundancia 6.516685 2009
## 2 Erithacus rubecula Abundancia 7.607695 2009
## 3 Corvus corone Abundancia 3.514719 2009
## 4 Turdus merula Ano2 8.471691 2010
## 5 Erithacus rubecula Ano2 2.282309 2010
## 6 Corvus corone Ano2 2.811775 2010
library(ggplot2)
my.ggplot <- ggplot(my.tb, aes(y=value, x=Ano, shape=spp, color=spp)) +
theme_light() +
geom_line(aes(x=Ano, y=value, group=spp)) +
geom_point(aes(x=Ano, y=value, group=spp)) +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
scale_color_brewer(palette = "Greys") + geom_vline(xintercept = 2010, linetype="dotted",
color = "blue", size=1) +
labs(title = "Tendencias nas abundancias") + ylab("Abundancia (Aves/10 ha)")
my.ggplot
## representar a tendencia asumindo un modelo lineal
my.ggplot + geom_smooth(method = "lm") + scale_color_brewer(palette = "Reds")
## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.
## `geom_smooth()` using formula 'y ~ x'
#rmarkdown::render("/Users/adrianregossanz/Downloads/Taller_Aves.R", "pdf_document")