From 114f877190fcc528b884e8dbbfff6605a5638c72 Mon Sep 17 00:00:00 2001 From: Dan McGlinn Date: Mon, 8 Feb 2021 22:14:40 -0500 Subject: [PATCH] Add a preliminary mob multi-metric analysis. Results are still very prelim as we have not controlled for sampling effort (i.e., number of checklists) across grids). --- .gitignore | 1 + Figures/ENS.pdf | Bin 0 -> 167831 bytes Figures/ghm_vs_N.pdf | Bin 0 -> 22678 bytes R/prelim_analyses.R | 103 ++++++++++++++++++++++++++++++++++++++++--- 4 files changed, 97 insertions(+), 7 deletions(-) create mode 100644 Figures/ENS.pdf create mode 100644 Figures/ghm_vs_N.pdf diff --git a/.gitignore b/.gitignore index c4c0b0b..4f57365 100644 --- a/.gitignore +++ b/.gitignore @@ -1 +1,2 @@ Data/ebird_data_raw.RDS +.Rproj.user diff --git a/Figures/ENS.pdf b/Figures/ENS.pdf new file mode 100644 index 0000000000000000000000000000000000000000..c09a9830c22f78c3a4198275009b2bcdf97960fa GIT binary patch literal 167831 zcmZ_#cQ{<%9|eqlqef2HYs|A@cv$=82%L57PRHpn`?Fg@d#06G2T2TWhx`qWA>bPyRm{#Q%RX{4d@AQoC9^ zx&PnE1r?l~-0`PwPaywic0p}xH)ju5EBy07|6kAhUxNRqIo?=1*}B7@2!ln%#YLV7 zzCt*-l>@$c6s@hC-&_B$=5FrzR&ylxMz1xg4e|H6q)EuOSkkP^C-Uqhyw#y_ 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zq2Xu{SPqH8xWha^V85S5LV+m&fpGhqe#0N>>E`F}jd1hFfWRnE6cXkIWOMr&8NJiF zJN*s+`m^o-)rkMWfBiWXfUW$mV}9abcSK;^xa#H&lmrY4KiB0C8+$h&4+Ef#>uvz( z^?%L(&-vm15mWK+F^yq}02l@ib>jj5T5HhGxAb=eZIB<#gGU~upa}dC@7DoRRZ&q= z0eOJ_(g1|)&x;`BUz&m{FlX%?e`yLpj|l_-|i%0N@KC zb^qZDXd3_46csf7Qy&2RRRd_a|L|1=QvA&qxIU+`C)#pmZGAPiU!Y#6Bo@4dHxR|x2m21 literal 0 HcmV?d00001 diff --git a/R/prelim_analyses.R b/R/prelim_analyses.R index 5e7ff8d..b280942 100644 --- a/R/prelim_analyses.R +++ b/R/prelim_analyses.R @@ -14,6 +14,7 @@ library(readr) library(lubridate) library(sf) library(concaveman) +library(mobr) # read in eBird data and # only read in breeding season eBird data for the time being @@ -85,7 +86,7 @@ target <- st_geometry(initial) # can mess with grid size a bit more down the road grids <- con_hull %>% - st_make_grid(cellsize=.1, square=FALSE) + st_make_grid(cellsize=.5, square=FALSE) # To sf grid <- st_sf(index = 1:length(lengths(grids)), grids) # Add index @@ -116,6 +117,7 @@ sum(is.na(a$grid_id)) dat_with_grids <- filtered_dat %>% left_join(., a, by="SAMPLING_EVENT_IDENTIFIER") + ##################################################### ############## SOME PLAY STUFF FROM COREY ########### ##################################################### @@ -124,21 +126,26 @@ dat_with_grids <- filtered_dat %>% # can easily add other things like phylo diversity # and/or shannon diversity and/or functional diversity etc. # just need to pull a few extra things together -community_dat <- filtered_dat %>% - group_by(SAMPLING_EVENT_IDENTIFIER, COMMON_NAME, SCIENTIFIC_NAME) %>% +community_dat <- dat_with_grids %>% + group_by(SAMPLING_EVENT_IDENTIFIER, COMMON_NAME, SCIENTIFIC_NAME, + LATITUDE, LONGITUDE, grid_id) %>% summarize(OBSERVATION_COUNT=sum(OBSERVATION_COUNT), ghm=mean(ghm)) %>% - ungroup() %>% + ungroup() + +metric_dat <- community_dat %>% group_by(SAMPLING_EVENT_IDENTIFIER) %>% summarize(SPECIES_RICHNESS=length(unique(COMMON_NAME)), ABUNDANCE=sum(OBSERVATION_COUNT), GHM=mean(ghm)) %>% dplyr::filter(complete.cases(GHM)) +plot(density(metric_dat$SPECIES_RICHNESS)) +plot(density(log10(metric_dat$ABUNDANCE))) # raw plot of community level vs # modification score -ggplot(community_dat, aes(x=GHM, y=SPECIES_RICHNESS))+ +ggplot(metric_dat, aes(x=GHM, y=SPECIES_RICHNESS))+ geom_point()+ theme_bw()+ theme(axis.text=element_text(color="black"))+ @@ -147,7 +154,7 @@ ggplot(community_dat, aes(x=GHM, y=SPECIES_RICHNESS))+ geom_smooth(method="gam") -ggplot(community_dat, aes(x=GHM, y=ABUNDANCE))+ +ggplot(metric_dat, aes(x=GHM, y=ABUNDANCE))+ geom_point()+ theme_bw()+ theme(axis.text=element_text(color="black"))+ @@ -157,7 +164,89 @@ ggplot(community_dat, aes(x=GHM, y=ABUNDANCE))+ geom_smooth(method="gam") - +# MoB data restructure -------------------------------------------------- + +comm <- pivot_wider(community_dat, id_cols = 'SAMPLING_EVENT_IDENTIFIER', + names_from = 'SCIENTIFIC_NAME', + values_from = 'OBSERVATION_COUNT', + values_fill = 0) + +env <- community_dat %>% + group_by(SAMPLING_EVENT_IDENTIFIER) %>% + summarize(lat = mean(LATITUDE), + long = mean(LONGITUDE), + ghm = mean(ghm), + grid_id = mean(grid_id)) + +all.equal(comm$SAMPLING_EVENT_IDENTIFIER, env$SAMPLING_EVENT_IDENTIFIER) + +comm <- as.data.frame(comm) +row.names(comm) <- comm$SAMPLING_EVENT_IDENTIFIER +comm <- comm[ , -1] +comm[1:5, 1:5] + +bird_mob <- make_mob_in(comm, env, coord_names = c('long', 'lat'), + latlong = TRUE) +bird_mob +## multi-metric MoB analysis -------------------------------------------------- + +stats <- get_mob_stats(bird_mob, group_var = 'grid_id', n_perm = 1) + +alphas <- stats$samples_stats +alphas$ghm <- env$ghm[match(alphas$group, env$grid_id)] + +gammas <- stats$groups_stats +gammas$ghm <- env$ghm[match(gammas$group, env$grid_id)] + +# fit linear models +lm_alpha = alphas %>% group_by(index, effort) %>% + do(mod = lm(value ~ ghm, data = .)) +lm_gamma = gammas %>% group_by(index, effort) %>% + do(mod = lm(value ~ ghm, data = .)) + +# get model coefs + +mod_coef_alpha = broom::tidy(lm_alpha, mod) +mod_coef_gamma = broom::tidy(lm_gamma, mod) + +# get model summary +mod_sum_alpha = broom::glance(lm_alpha, mod) +mod_sum_gamma = broom::glance(lm_gamma, mod) + +mob_met = rbind(data.frame(scale = 'alpha', alphas), + data.frame(scale = 'gamma', gammas)) +mob_met$index = factor(mob_met$index, + levels = levels(mob_met$index)[c(2:1, 3:7)]) + +N1 = mob_met %>% + subset(index %in% 'N') %>% + ggplot(aes(x = ghm, y = value)) + + geom_point(aes(color = scale)) + + geom_smooth(aes(color = scale), method = 'lm', se = F) + + labs(x = "GHM", y = "Total abundance (N)") +ggsave("./Figures/ghm_vs_N.pdf", plot = N1, width = 15, height = 12, + units = "cm") + +p1 = mob_met %>% + subset(abs(value) < 1000) %>% + subset(index %in% c('S', 'S_n', 'S_PIE')) %>% + ggplot(aes(x = ghm, y = value, col = scale)) + + geom_point() + + geom_smooth(method = 'lm', se = F) + + facet_wrap(. ~ index, scales = "free") + + +p2 = mob_met %>% + subset(abs(value) < 1000) %>% + subset(index %in% c('beta_S', 'beta_S_n', 'beta_S_PIE')) %>% + ggplot(aes(x = ghm, y = value)) + + geom_point() + + geom_smooth(method = 'lm', se = F) + + facet_wrap(. ~ index, scales = "free") + +g = egg::ggarrange(p1, p2) +ggsave("./Figures/ENS.pdf", plot = g, width = 20, height = 15, + units = "cm")