trend-analysis

Last updated: 2021-05-21

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Knit directory: booksn_dispersantes/

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File	Version	Author	Date	Message
Rmd	599c816	Antonio J Perez-Luque	2021-05-21	change plots for publication
html	0bb400c	Antonio J Perez-Luque	2021-05-20	Build site.
html	684a3f9	Antonio J Perez-Luque	2021-05-07	Build site.
html	725547c	Antonio J Perez-Luque	2021-05-05	Build site.
Rmd	55b3a47	Antonio J Perez-Luque	2021-05-05	add mankendall anañysis

Introduction

knitr::opts_chunk$set(echo = TRUE, 
                      warning = FALSE, 
                      message = FALSE)

library("tidyverse")
library("here")
library("rtrim")
library("trend")
library("DT")

Read data and compute the abundance average by year

passerine <- read_csv(here::here("data/passerine.csv")) 
passerine_abbreviations <- read_csv(here::here("data/passerine_abbrev.csv")) 

passerine.ab <- passerine %>% 
  rename(sp = sp.abb) %>% 
  dplyr::select(-especie) %>% 
  group_by(sp, year, habitat) %>% 
  summarise(ab_avg = round(mean(den, na.rm=TRUE),2), 
            sd = sd(den, na.rm = TRUE), 
            se = sd/sqrt(length(den)), 
            n = length(den))

Explore Abundances as index

For each species we compute the abundance index, i.e. relative abundance comparing with initial values (\(ab_{index} = abundance_{time_{i}} / abundance_{time_{0}}\))
Data was exported as (data/passerine_ab.csv)

compute_abindex <- function(x){
  ab.base <- x %>% filter(year == min(x$year)) %>% pull(ab_avg)
  out <- x %>% mutate(ab.index = ab_avg/ab.base) %>% dplyr::select(year,ab.index)
  return(out)
}

cumbres <- passerine.ab %>% 
  filter(habitat == "cumbres") 
cumbres.index <- cumbres %>% 
  group_by(sp) %>% group_modify(
    ~ {compute_abindex(.x)})
cumbres <-cumbres %>%  inner_join(cumbres.index)


enebral <- passerine.ab %>% 
  filter(habitat == "enebral") 
enebral.index <- enebral %>% 
  group_by(sp) %>% group_modify(
    ~ {compute_abindex(.x)})
enebral <- enebral %>%  inner_join(enebral.index)

robledal <- passerine.ab %>% 
  filter(habitat == "robledal") 
robledal.index <- robledal %>% 
  group_by(sp) %>% group_modify(
    ~ {compute_abindex(.x)})
robledal <- robledal %>%  inner_join(robledal.index)

passerine.abindex <- bind_rows(cumbres, enebral, robledal) %>% 
  inner_join(passerine_abbreviations, by = c("sp" = "sp.abb"))

write_csv(passerine.abindex, here::here("data/passerine_ab.csv"))

bird_theme <- 
  ggplot2::theme_bw() + 
  ggplot2::theme(
    panel.grid.minor = element_blank(),
    strip.background = element_blank()
  )

# Generate functions to plot abundances and abundance index 
plotabundances <- function(df, myhabitat, selected_especies, colorea, nrows, escalas,...){
  if(missing(colorea)) { colorea = "blue"} 
  if(missing(escalas)) { escalas = "fixed"}
  df %>% 
  filter(habitat == myhabitat) %>% 
  filter(stringr::str_detect(especie, selected_especies)) %>%
  ggplot(aes(x=as.factor(year), y=ab_avg)) + 
  geom_errorbar(aes(ymin = ab_avg - se, 
                    ymax = ab_avg + se), colour = colorea, 
                width = 0.2) +
  geom_point(colour = colorea) +
  facet_wrap(~especie, nrow = nrows, scales = escalas) + 
  geom_line(aes(x=as.factor(year), y=ab_avg, group=1), colour = colorea, na.rm = TRUE) +
  ylab("abundance (ind / 10ha)") + xlab("") + 
  bird_theme + 
  ggtitle(myhabitat) +
  theme(panel.grid = element_blank())
} 

plotabindex <- function(df, myhabitat, selected_especies){
  df %>% 
  filter(habitat == myhabitat) %>% 
  filter(stringr::str_detect(especie, selected_especies)) %>%
  ggplot(aes(x=as.factor(year), y=ab.index)) + 
  geom_point(color = "red", shape=15) + 
  geom_hline(yintercept = 1, colour="red", linetype = "dashed") + 
  facet_wrap(~especie) + 
  geom_line(aes(x=as.factor(year), y=ab.index, group=1), colour = "red") +
  ylab("abundance (ind / 10ha)") + xlab("") + 
  bird_theme + 
  ggtitle(myhabitat)
} 



plotabmixed <- function(df, myhabitat, selected_especies){
  df %>% 
  filter(habitat == myhabitat) %>% 
  filter(stringr::str_detect(especie, selected_especies)) %>%
  ggplot(aes(x=as.factor(year), y=ab_avg)) + 
  geom_errorbar(aes(ymin = ab_avg - se, 
                    ymax = ab_avg + se),
                width = 0.2) +
  geom_point() +
  geom_point(aes(x=as.factor(year), y=ab.index), color = "red", shape=15) + 
  geom_hline(yintercept = 1, colour="red", linetype = "dashed") + 
  facet_wrap(~especie) + 
  geom_line(aes(x=as.factor(year), y=ab.index, group=1), colour = "red") +
  ylab("abundance (ind / 10ha)") + xlab("") + 
  bird_theme + 
  ggtitle(myhabitat)
}

sp_cumbres <- "Card|Oena|Phoeni|collaris"
sp_enebral <- "Alauda|Anthus|cannabina|Embe|Oena|Phoeni|collaris|conspici|Troglo"
sp_robledal <- "Aeg|cannabina|Certhia|Cyanis|Erith|Fringi|Garrulus|Lullula|Parus|Peripares|Phoeni|bonelli|Regulus|rubicola|serinus|Sitta|atricapilla|cantillans|merula|visci|Troglo"

# enebral rubicola
# robledal Lopho

plotabundances(df = passerine.abindex, 
           myhabitat = "cumbres",
           selected_especies = sp_cumbres, nrows = 2)

plotabundances(df = passerine.abindex, 
           myhabitat = "enebral",
           selected_especies = sp_enebral, nrows = 3)

plotabundances(df = passerine.abindex, 
           myhabitat = "robledal",
           selected_especies = sp_robledal, nrows = 4)

plotabindex(df = passerine.abindex, 
           myhabitat = "cumbres",
           selected_especies = sp_cumbres)

Version	Author	Date
725547c	Antonio J Perez-Luque	2021-05-05

plotabindex(df = passerine.abindex, 
           myhabitat = "enebral",
           selected_especies = sp_enebral)

Version	Author	Date
725547c	Antonio J Perez-Luque	2021-05-05

Compute Mann-Kendall trends

# compute Sens and Mk for each spec and habitat 

computeMKS <- function(x) {
  v <- x %>% pull(ab_avg)
  s <- sens.slope(v, conf.level = 0.95)
  mk <- mk.test(v)
  out <- data.frame(
    S = mk$estimates[1],
    varS = mk$estimates[2],
    tau = mk$estimates[3],
    pvalue = s$p.value,
    z = mk$statistic,
    n = mk$parameter,
    senslope = s$estimates,
    sens.cilo = s$conf.int[1],
    sens.ciup = s$conf.int[2]
  )
  row.names(out) <- ""
  return(out)
}

mk.cumbres <- passerine.abindex %>% 
  filter(habitat == "cumbres") %>% 
  filter(stringr::str_detect(especie, sp_cumbres)) %>% 
  group_by(especie) %>% 
  group_modify(
    ~ {computeMKS(.x)}) %>% 
  mutate(habitat = "cumbres")

mk.enebral <- passerine.abindex %>% 
  filter(habitat == "enebral") %>% 
  filter(stringr::str_detect(especie, sp_enebral)) %>% 
  group_by(especie) %>% 
  group_modify(
    ~ {computeMKS(.x)}) %>% 
  mutate(habitat = "enebral")

mk.robledal <- passerine.abindex %>% 
  filter(habitat == "robledal") %>% 
  filter(stringr::str_detect(especie, sp_robledal)) %>% 
  group_by(especie) %>% 
  group_modify(
    ~ {computeMKS(.x)}) %>% 
  mutate(habitat = "robledal")
 

mk.all <- bind_rows(mk.cumbres, mk.enebral, mk.robledal) %>%
  relocate(habitat) %>%
  mutate(across(.cols = c(varS, tau, pvalue, z, senslope, sens.cilo, sens.ciup), round, 3))

write_csv(mk.all, here::here("output/mktrend/mktrend_especies.csv"))

datatable(mk.all)

plotmk <- mk.all %>% 
  mutate(sig = ifelse(pvalue < 0.05, "*", "")) %>% 
  ggplot(aes(x=especie, y=tau)) + 
  geom_bar(stat="identity", fill="gray") +
  facet_wrap(~habitat) + coord_flip() + 
  theme_bw() + 
  geom_hline(yintercept = 0) + 
  geom_text(aes(label=sig), hjust=1.5, size=4) + 
  theme(
    panel.grid.minor = element_blank(),
    strip.background = element_blank()
  ) + 
  xlab("Species") + ylab("Mann-Kendall trend (tau)")


ggsave(here::here("output/mktrend/plot_mktrend.pdf"),
       width =21, height = 15, units = "cm")
plotmk

Version	Author	Date
725547c	Antonio J Perez-Luque	2021-05-05

dev.off()

null device 
          1

Genrate plots by habitats

sp_cumbres <- "Card|Oena|Phoeni|collaris"
sp_enebral2 <- "Alauda|Anthus|cannabina|Embe|Oena|Phoeni"
sp_robledal2 <- "Aeg|Certhia|Cyanis|Erith|Fringi|Garrulus|Parus|Peripares|bonelli|Regulus|Sitta|atricapilla|cantillans|merula|visci|Troglo"

plot_cumbres <- plotabundances(df = passerine.abindex, 
           myhabitat = "cumbres",
           selected_especies = sp_cumbres, nrows = 2) +
  ggplot2::theme(
    axis.text.x = element_text(size=6)
  )
 
plot_enebral <- plotabundances(df = passerine.abindex, 
           myhabitat = "enebral",
           selected_especies = sp_enebral2, nrows = 2) +
    ggplot2::theme(
    axis.text.x = element_text(size=5)
  )
 

plot_robledal <- plotabundances(df = passerine.abindex, 
           myhabitat = "robledal",
           selected_especies = sp_robledal2, nrows = 3) +
    ggplot2::theme(
    axis.text.x = element_text(size=5))

null device 
          1

null device 
          1

null device 
          1

sessionInfo()

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] DT_0.17         trend_1.1.4     rtrim_2.1.1     here_1.0.1     
 [5] forcats_0.5.1   stringr_1.4.0   dplyr_1.0.4     purrr_0.3.4    
 [9] readr_1.4.0     tidyr_1.1.2     tibble_3.0.6    ggplot2_3.3.3  
[13] tidyverse_1.3.0 workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.6        lubridate_1.7.10  assertthat_0.2.1  rprojroot_2.0.2  
 [5] digest_0.6.27     R6_2.5.0          cellranger_1.1.0  backports_1.2.1  
 [9] reprex_1.0.0      evaluate_0.14     highr_0.8         httr_1.4.2       
[13] pillar_1.4.7      rlang_0.4.10      readxl_1.3.1      rstudioapi_0.13  
[17] whisker_0.4       jquerylib_0.1.3   rmarkdown_2.6.6   labeling_0.4.2   
[21] htmlwidgets_1.5.3 munsell_0.5.0     broom_0.7.4       compiler_4.0.2   
[25] httpuv_1.5.5      modelr_0.1.8      xfun_0.20         pkgconfig_2.0.3  
[29] htmltools_0.5.1.1 tidyselect_1.1.0  crayon_1.4.1      dbplyr_2.1.0     
[33] withr_2.4.1       later_1.1.0.1     grid_4.0.2        jsonlite_1.7.2   
[37] gtable_0.3.0      lifecycle_1.0.0   DBI_1.1.1         git2r_0.28.0     
[41] magrittr_2.0.1    scales_1.1.1      cli_2.3.0         stringi_1.5.3    
[45] farver_2.0.3      fs_1.5.0          promises_1.2.0.1  xml2_1.3.2       
[49] bslib_0.2.4       ellipsis_0.3.1    generics_0.1.0    vctrs_0.3.6      
[53] tools_4.0.2       glue_1.4.2        crosstalk_1.1.1   hms_1.0.0        
[57] yaml_2.2.1        colorspace_2.0-0  extraDistr_1.9.1  rvest_0.3.6      
[61] knitr_1.31        haven_2.3.1       sass_0.3.1

trend-analysis

Antonio J Perez-Luque

2021-05-04

Introduction

Explore Abundances as index

Compute Mann-Kendall trends

Genrate plots by habitats