Temporal beta diversity
Antonio J Perez-Luque
2021-04-30
Last updated: 2021-05-20
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Knit directory: booksn_dispersantes/
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Explore temporal beta diversity
We explore the temporal evolution of \(\beta\)-diversity (BD)
We read about BD and found several papers:
There are several R packages for BD computation: betapart; adespatial.
Computation of Temporal Beta Diversity Index
- For each location we explore the TBI (see Legrende 2019) in three temporal times: t0 (1982), t1 (2010), t2(2019)
knitr::opts_chunk$set(echo = TRUE,
warning = FALSE,
message = FALSE)library("tidyverse")
library("here")
library("plotly")
library("adespatial")
library("DT")
library("betapart")
source(here::here("code/computeTBI.R"))- 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)) Prepare data for Temporal Beta Diversity analysis:
- generate a site.year variable = “site”.“year”
- transpose from long to wide format to get sites as rows and especies as column
- abundance values as values
- NA values are filled with 0
df <- passerine.ab %>%
unite("site", habitat:year, sep="_", remove = FALSE) %>%
dplyr::select(-sd, -se, -n) %>%
pivot_wider(names_from = sp,
values_from = ab_avg,
values_fill = 0)TBI summit habitat
- years selected: 1982, 2008, 2018
cumbres <- df %>%
filter(habitat == "cumbres") %>%
dplyr::select(-year) %>%
column_to_rownames(var="site") %>%
dplyr::select_if(
negate(function(x) is.numeric(x) && sum(x) == 0)) %>%
arrange(year)
years <- df %>% filter(habitat == "cumbres") %>% arrange(year) %>% pull(year)
years_selected <- c(1982, 2008, 2018)
tbi_summits <- computeTBI(cumbres, vector.years = years_selected, y0=1982)TBI juniper habitat
- years selected: 1984, 2008, 2018
enebral <- df %>%
filter(habitat == "enebral") %>%
dplyr::select(-year) %>%
column_to_rownames(var="site") %>%
dplyr::select_if(
negate(function(x) is.numeric(x) && sum(x) == 0)) %>%
arrange(year)
years <- df %>% filter(habitat == "enebral") %>% arrange(year) %>% pull(year)
years_selected <- c(1984, 2008, 2018)
tbi_juniper <- computeTBI(enebral, vector.years = years_selected, y0 = 1984)TBI oak habitat
- years selected: 1981, 2008, 2018
robledal <- df %>%
filter(habitat == "robledal") %>%
dplyr::select(-year) %>%
column_to_rownames(var="site") %>%
dplyr::select_if(
negate(function(x) is.numeric(x) && sum(x) == 0)) %>%
arrange(year)
years <- df %>% filter(habitat == "robledal") %>% arrange(year) %>% pull(year)
years_selected <- c(1981, 2008, 2018)
tbi_robledal <- computeTBI(robledal, vector.years = years_selected, y0 = 1981)Explore dissimilarities
d <- bind_rows(tbi_summits, tbi_juniper, tbi_robledal) %>%
mutate(year_t0common = "1980's") %>%
rename("losses" = `mean(B/den)`,
"gains" = `mean(C/den)`,
"D" = `mean(D)`) %>%
unite("pair", year_t0common, year_t1, sep="-") %>%
dplyr::select(habitat:D, Change)
write_csv(d, file=here::here("output/betadiversity/tbi.csv"))
DT::datatable(d) %>% DT::formatRound(columns = c("losses", "gains", "D"), digits=3)cols <- c("cumbres" = "#8d96a3", "enebral" = "#66a182", "robledal" = "#edae49")
plot_BD <- d %>%
ggplot(aes(x=pair, fill=habitat, colour=habitat)) +
geom_bar(aes(y=D), stat="identity", position = position_dodge2(width = .9)) +
ylab(expression(paste("Dissimilarity in ", beta, "-diversisty"))) +
xlab("") +
theme_bw() +
scale_color_manual(values = cols) +
scale_fill_manual(values = cols) +
geom_point(aes(x=pair, y=losses), colour="black", shape=25,
position = position_dodge2(width = .9))
ggsave(here::here("output/betadiversity/plot_betadiversity.pdf"),
width = 12, height = 10, units = "cm")
plot_BD
| Version | Author | Date |
|---|---|---|
| 7369da6 | Antonio J Perez-Luque | 2021-05-04 |
dev.off()null device
1 BetaPart Analysis
b.cumbres <- betapart.core.abund(cumbres[,-c(1:2)])
bd.cumbres <- beta.multi.abund(b.cumbres, index.family = "bray")
b.enebral <- betapart.core.abund(enebral[,-c(1:2)])
bd.enebral <- beta.multi.abund(b.enebral, index.family = "bray")
b.robledal <- betapart.core.abund(robledal[,-c(1:2)])
bd.robledal <- beta.multi.abund(b.robledal, index.family = "bray")
bd <- cbind(data.frame(robledal = unlist(bd.robledal)),
data.frame(enebral = unlist(bd.enebral)),
data.frame(cumbres = unlist(bd.cumbres))) %>%
rownames_to_column("variable") %>%
pivot_longer(robledal:cumbres, names_to = "habitat")
write.csv(bd, file=here::here("output/betadiversity/betapart.csv"))
DT::datatable(bd) %>% DT::formatRound("value", digits=3)
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] betapart_1.5.4 DT_0.17 adespatial_0.3-14 plotly_4.9.3
[5] here_1.0.1 forcats_0.5.1 stringr_1.4.0 dplyr_1.0.4
[9] purrr_0.3.4 readr_1.4.0 tidyr_1.1.2 tibble_3.0.6
[13] ggplot2_3.3.3 tidyverse_1.3.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] snow_0.4-3 readxl_1.3.1 uuid_0.1-4
[4] backports_1.2.1 fastmatch_1.1-0 plyr_1.8.6
[7] igraph_1.2.6 lazyeval_0.2.2 sp_1.4-5
[10] splines_4.0.2 crosstalk_1.1.1 rncl_0.8.4
[13] digest_0.6.27 foreach_1.5.1 htmltools_0.5.1.1
[16] gdata_2.18.0 magrittr_2.0.1 cluster_2.1.0
[19] modelr_0.1.8 gmodels_2.18.1 prettyunits_1.1.1
[22] jpeg_0.1-8.1 colorspace_2.0-0 rvest_0.3.6
[25] haven_2.3.1 xfun_0.20 crayon_1.4.1
[28] jsonlite_1.7.2 phylobase_0.8.10 iterators_1.0.13
[31] ape_5.4-1 glue_1.4.2 gtable_0.3.0
[34] seqinr_4.2-5 adegraphics_1.0-15 abind_1.4-5
[37] scales_1.1.1 DBI_1.1.1 Rcpp_1.0.6
[40] viridisLite_0.3.0 xtable_1.8-4 progress_1.2.2
[43] spData_0.3.8 magic_1.5-9 units_0.6-7
[46] spdep_1.1-5 htmlwidgets_1.5.3 httr_1.4.2
[49] RColorBrewer_1.1-2 ellipsis_0.3.1 farver_2.0.3
[52] pkgconfig_2.0.3 XML_3.99-0.5 sass_0.3.1
[55] dbplyr_2.1.0 deldir_0.2-9 labeling_0.4.2
[58] tidyselect_1.1.0 rlang_0.4.10 reshape2_1.4.4
[61] later_1.1.0.1 munsell_0.5.0 adephylo_1.1-11
[64] cellranger_1.1.0 tools_4.0.2 cli_2.3.0
[67] generics_0.1.0 ade4_1.7-16 broom_0.7.4
[70] geometry_0.4.5 evaluate_0.14 fastmap_1.1.0
[73] yaml_2.2.1 knitr_1.31 fs_1.5.0
[76] nlme_3.1-152 whisker_0.4 mime_0.10
[79] adegenet_2.1.3 xml2_1.3.2 compiler_4.0.2
[82] rstudioapi_0.13 png_0.1-7 e1071_1.7-4
[85] reprex_1.0.0 bslib_0.2.4 RNeXML_2.4.5
[88] stringi_1.5.3 highr_0.8 lattice_0.20-41
[91] Matrix_1.3-2 classInt_0.4-3 vegan_2.5-7
[94] permute_0.9-5 vctrs_0.3.6 pillar_1.4.7
[97] LearnBayes_2.15.1 lifecycle_1.0.0 jquerylib_0.1.3
[100] data.table_1.13.6 raster_3.4-5 httpuv_1.5.5
[103] R6_2.5.0 latticeExtra_0.6-29 promises_1.2.0.1
[106] KernSmooth_2.23-18 codetools_0.2-18 rcdd_1.2-2
[109] boot_1.3-26 MASS_7.3-53 gtools_3.8.2
[112] assertthat_0.2.1 picante_1.8.2 rprojroot_2.0.2
[115] withr_2.4.1 mgcv_1.8-33 expm_0.999-6
[118] parallel_4.0.2 hms_1.0.0 doSNOW_1.0.19
[121] grid_4.0.2 coda_0.19-4 class_7.3-18
[124] rmarkdown_2.6.6 git2r_0.28.0 itertools_0.1-3
[127] sf_0.9-7 shiny_1.6.0 lubridate_1.7.10