From a95049c5dacc103a255a2766b2dc5f573ec96783 Mon Sep 17 00:00:00 2001 From: silasprincipe <53846571+silasprincipe@users.noreply.github.com> Date: Wed, 22 May 2024 14:52:43 +0200 Subject: [PATCH] Updates on plot code --- analysis_paf.R | 3 + codes/analysis_occurrence_data.R | 72 +- codes/get_sst_marinespecies.R | 60 +- codes/models_preview.pdf | Bin 0 -> 591738 bytes codes/models_preview.qmd | 376 ++++++++++ codes/plot_analysis_occurrence_data.R | 256 +++++++ codes/plot_paf.R | 225 ++++++ codes/plot_temperatures_glossy.R | 136 ++++ codes/sdm_components_mhs.R | 443 ++++++++++++ codes/sdm_model_fishes.R | 180 +++++ codes/sdm_model_fishes_mhs.R | 203 ++++++ codes/sdm_model_function_mhs.R | 941 +++++++++++++++++++++++++ functions/sdm_model_function.R | 970 ++++++++++++++++++++++++++ plots_experimental.R | 169 +++++ sdm_demonstration.pdf | Bin 0 -> 1144482 bytes sdm_demonstration.qmd | 75 ++ 16 files changed, 4088 insertions(+), 21 deletions(-) create mode 100644 analysis_paf.R create mode 100644 codes/models_preview.pdf create mode 100644 codes/models_preview.qmd create mode 100644 codes/plot_analysis_occurrence_data.R create mode 100644 codes/plot_paf.R create mode 100644 codes/plot_temperatures_glossy.R create mode 100644 codes/sdm_components_mhs.R create mode 100644 codes/sdm_model_fishes.R create mode 100644 codes/sdm_model_fishes_mhs.R create mode 100644 codes/sdm_model_function_mhs.R create mode 100644 functions/sdm_model_function.R create mode 100644 plots_experimental.R create mode 100644 sdm_demonstration.pdf create mode 100644 sdm_demonstration.qmd diff --git a/analysis_paf.R b/analysis_paf.R new file mode 100644 index 0000000..2b44333 --- /dev/null +++ b/analysis_paf.R @@ -0,0 +1,3 @@ +### Proportion of affected species + +# prop_critical ~ sites + (1|method) + (1|richness) \ No newline at end of file diff --git a/codes/analysis_occurrence_data.R b/codes/analysis_occurrence_data.R index 06ae69f..d9f1f8d 100644 --- a/codes/analysis_occurrence_data.R +++ b/codes/analysis_occurrence_data.R @@ -1,10 +1,10 @@ #################### eDNA expeditions - scientific analysis #################### ########################## Environmental data download ######################### # January of 2024 -# Author: Mike Burrows -# Contact: michael.burrows@sams.ac.uk +# Author: Silas Principe, Mike Burrows +# Contact: s.principe@unesco.org; michael.burrows@sams.ac.uk # -########## Read and analyse eDNA data from UNESCO World Heritage Sites ######### +############## Plot eDNA data from UNESCO World Heritage Sites ################# # Load packages library(dplyr) @@ -12,11 +12,10 @@ library(stringr) library(purrr) library(arrow) library(data.table) +library(ggplot2) fs::dir_create("figures") -#### - speciestherm<-read_parquet("results/species_tsummaries.parquet") speciesthermdt<-data.table(speciestherm) @@ -30,28 +29,18 @@ speciesthermsite<-read_parquet("results/tsummaries_aggregated.parquet") speciesthermsitedt<-data.table(speciesthermsite) -### CTI by higher geography areas ##### - -names(speciesthermsitedt) -# [1] "species" "AphiaID" "source_obis" "source_gbif" "source_dna" -# [6] "higherGeography" "q_0.5" "q_0.75" "q_0.9" "q_0.95" -# [11] "q_0.99" "q_1" "mean" "sd" "q_0" -# [16] "q_0.01" "q_0.05" "q_0.1" "q_0.25" "site_current" -# [21] "site_ssp126_dec100" "site_ssp126_dec50" "site_ssp245_dec100" "site_ssp245_dec50" "site_ssp370_dec100" -# [26] "site_ssp370_dec50" "site_ssp460_dec100" "site_ssp460_dec50" "site_ssp585_dec100" "site_ssp585_dec50" -# [31] "records" "where" +speciesthermsitedt$where <- ifelse(speciesthermsitedt$where == "Both" | + speciesthermsitedt$where == "OBIS/GBIF", "Databases", "eDNA") +### CTI by higher geography areas ##### ctihighergeog<-speciesthermsitedt[,list(ctiavg=mean(q_0.5), sdcti=sd(q_0.5), str=mean(q_0.9-q_0.1), sstavg=mean(site_current), nspp=.N),by=c("higherGeography","where")] -# Save for later use -write_parquet(ctihighergeog, "results/cti_highergeo.parquet") # Save figures -pdf("figures/AllSpeciesThermalMetrics.pdf",height=3.5,width=8) par(mfrow=c(1,3)) plot(data=ctihighergeog,ctiavg~sstavg,pch=16,col=unclass(as.factor(where)),cex=1.5,main="All species", xlab="Sea surface temperature (°C)", ylab="Community Temperature Index (°C)") @@ -68,6 +57,53 @@ plot(data=ctihighergeog,str~sstavg,pch=16,col=unclass(as.factor(where)),cex=1.5, #abline(a=0,b=1,lty=3) legend("bottomleft",legend=levels(as.factor(ctihighergeog$where)),inset=0.02,col=1:3,pt.cex=1.5,pch=16) + +(p1 <- ggplot(ctihighergeog) + + geom_abline(slope = 1, intercept = 0, color = "grey90") + + geom_point(aes(x = sstavg, y = ctiavg, colour = where, size = nspp), alpha = .5) + + scale_color_manual(values = c("#00c3c9", "#f58000")) + #"#5151db", + scale_y_continuous(limits = c(5, 30)) + + scale_x_continuous(limits = c(5, 30)) + + scale_size(range = c(2,7)) + + guides(colour = guide_legend(override.aes = list(alpha = 1, size = 3))) + + ylab("Community Thermal Index (°C)") + + xlab("Sea Surface Temperature (°C)") + + theme_light() + + theme(panel.grid = element_blank(), + panel.grid.major.y = element_line(colour = "grey90", linewidth = 0.3, linetype = 2), + legend.title = element_blank())) + +(p2 <- ggplot(ctihighergeog) + + geom_point(aes(x = sstavg, y = sdcti, colour = where, size = nspp), alpha = .5) + + scale_color_manual(values = c("#00c3c9", "#f58000")) + #"#5151db", + # scale_y_continuous(limits = c(5, 30)) + + # scale_x_continuous(limits = c(5, 30)) + + scale_size(range = c(2,7)) + + guides(colour = guide_legend(override.aes = list(alpha = 1, size = 3))) + + ylab("SD CTI (°C)") + + xlab("Sea Surface Temperature (°C)") + + theme_light() + + theme(panel.grid = element_blank(), + panel.grid.major.y = element_line(colour = "grey90", linewidth = 0.3, linetype = 2), + legend.title = element_blank())) + +(p3 <- ggplot(ctihighergeog) + + geom_point(aes(x = sstavg, y = str, colour = where, size = nspp), alpha = .5) + + scale_color_manual(values = c("#00c3c9", "#f58000")) + #"#5151db", + # scale_y_continuous(limits = c(5, 30)) + + # scale_x_continuous(limits = c(5, 30)) + + scale_size(range = c(2,7)) + + guides(colour = guide_legend(override.aes = list(alpha = 1, size = 3))) + + ylab("Species thermal range (average, °C)") + + xlab("Sea Surface Temperature (°C)") + + theme_light() + + theme(panel.grid = element_blank(), + panel.grid.major.y = element_line(colour = "grey90", linewidth = 0.3, linetype = 2), + legend.title = element_blank())) + +library(patchwork) +p1 + p2 + p3 + patchwork::plot_layout(guides = "collect") + ### Add taxon information specieslist<-read_parquet("results/species_list.parquet") diff --git a/codes/get_sst_marinespecies.R b/codes/get_sst_marinespecies.R index 39ba517..8edf7b1 100644 --- a/codes/get_sst_marinespecies.R +++ b/codes/get_sst_marinespecies.R @@ -8,10 +8,13 @@ # Load packages ---- library(DBI) +library(furrr) +library(storr) library(dplyr) library(tidyr) library(arrow) library(terra) +library(polars) fs::dir_create("results") # Load files/settings ---- @@ -65,15 +68,66 @@ names(env) <- env_vars # Get H3 ids from the environmental layer biooracle_h3 <- open_dataset("data/biooracle_h3id.parquet") -bio_h3_sel <- biooracle_h3 %>% - filter(h3 %in% unique(db_species$h3)) %>% +# Get unique h3 ids +un_h3s <- unique(db_species$h3) + +# Get cells for those available +bio_h3_presel <- biooracle_h3 %>% + filter(h3 %in% un_h3s) %>% select(cell, h3) %>% collect() +# Get not available +not_available <- un_h3s[!un_h3s %in% unique(bio_h3_presel$h3)] + +# Get disks +# Create a temporary folder to hold disks +fs::dir_create("t_disks") + +plan(multisession, workers = 8) +not_available_h3_b <- split(not_available, as.integer((seq_along(not_available) - 1) / 2000)) + +un_h3s_disk <- future_map(not_available_h3_b, function(x){ + + bio_h3 <- open_dataset("data/biooracle_h3id.parquet") + + disk_cells <- lapply(x, function(z){ + dc <- unlist(h3jsr::get_disk(z)) + data.frame(h3_original = z, h3 = dc) + }) + disk_cells <- bind_rows(disk_cells) + + pc <- bio_h3 %>% + filter(h3 %in% unique(disk_cells$h3)) %>% + select(cell, h3) %>% + collect() + + disk_cells <- left_join(disk_cells, pc) %>% + filter(!is.na(cell)) + + write_parquet(disk_cells, + paste0("t_disks/", x[1], ".parquet")) + NULL +}, .progress = T) + +# Aggregate expanded disks data +disk_f <- open_dataset("t_disks/") +disk_f <- disk_f %>% + collect() + +# Merge with previous one +bio_h3_presel <- bio_h3_presel %>% + mutate(h3_original = h3) %>% + select(h3_original, h3, cell) + +bio_h3_sel <- bind_rows(disk_f, bio_h3_presel) + + # Extract env data env_ext <- terra::extract(env, y = as.vector(bio_h3_sel$cell)) %>% bind_cols(bio_h3_sel) %>% - select(-cell) %>% + select(-cell, -h3) %>% + rename(h3 = h3_original) %>% group_by(h3) %>% summarise(across(everything(), ~ mean(.x, na.rm = TRUE))) diff --git a/codes/models_preview.pdf b/codes/models_preview.pdf new file mode 100644 index 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z28C?}_b4TNjS>c}5m0>1&$VNHnLqa6habiJb-7|61k=DM2H$Sm$8sjb`#oj5`oI#~ zO@pQtM=+Jxn|M2os!V-)e}iV!-)X?x4fLf@q-Pd(ayxf+7150~z>*`7s&kFA4#^_i zFx90a`f}`5;du>p+PV5kPL_+F7QZ*E4441EhM$OoOTIOu zi&Tl}sUQqJnx7)6Ub#bhM?*9htR;p6-pJKG)`QK1dJP5htF9`q%YR#!2(Nkx>#(l| z0iCG!e+RYT-e{zK$*u6~j-0|#ldTA(y;6>h!XXuogunoSTEyj@y(g&40a{09;b5gb z&L^g=s1_sC%ORmBqU!#m8)$EI!@h@=Xx2RgZ*2QPo*QPtFnd$Pt&pyjP}pnkPGZAh zo@+E_fU2p#r%T~DCuH^YJ<&!Wll++<2g8l9D1@Fs%=@Va#r^zR? zg9UwB16zm{FBYdGQ^+SlKl?9JHg7q_0.9)/.N, + pgt90ssp1d50=sum(site_ssp126_dec50>q_0.9)/.N, + pgt90ssp1d100=sum(site_ssp126_dec100>q_0.9)/.N, + pgt90ssp2d50=sum(site_ssp245_dec50>q_0.9)/.N, + pgt90ssp2d100=sum(site_ssp245_dec100>q_0.9)/.N, + pgt90ssp3d50=sum(site_ssp370_dec50>q_0.9)/.N, + pgt90ssp3d100=sum(site_ssp370_dec100>q_0.9)/.N, + pgt90ssp4d50=sum(site_ssp460_dec50>q_0.9)/.N, + pgt90ssp4d100=sum(site_ssp460_dec100>q_0.9)/.N, + pgt90ssp5d50=sum(site_ssp585_dec50>q_0.9)/.N, + pgt90ssp5d100=sum(site_ssp585_dec100>q_0.9)/.N), + by=c("higherGeography","where")] + +head(sthighergeog) +``` + +Once we prepare the data we can make the models. We start by doing a simple linear regression. + +```{r} +sthighergeog$where <- as.factor(sthighergeog$where) +sthighergeog$higherGeography <- as.factor(sthighergeog$higherGeography) + +sthighergeog_l <- data.table(tidyr::pivot_longer(sthighergeog, + cols = 8:18, + names_to = "scenario", values_to = "paf")) + +sthighergeog_l50 <- sthighergeog_l[grepl("d50|now", scenario),,] +sthighergeog_l100 <- sthighergeog_l[grepl("d100|now", scenario),,] + +sthighergeog_l50$scenario <- as.factor(sthighergeog_l50$scenario) +sthighergeog_l100$scenario <- as.factor(sthighergeog_l100$scenario) + +boxplot(paf ~ scenario, data = sthighergeog_l50) + +paf_lm1 <- glm(paf ~ scenario, data = sthighergeog_l50, family = binomial(), weights = nspp) + +summary(paf_lm1) +``` + +Check the residuals: + +```{r fig.height=6} +par(mfrow = c(2,2)) +plot(paf_lm1) +``` + +QQ plot is not good, but the rest is ok. + +Model considering the source of the data: + +```{r} +paf_lm2 <- glm(paf ~ scenario + where, data = sthighergeog_l50, family = binomial(), weights = nspp) + +summary(paf_lm2) +``` + +Check the residuals: + +```{r fig.height=6} +par(mfrow = c(2,2)) +plot(paf_lm2) +``` + +Try to include the interaction between "where" and "scenario": + +```{r} +paf_lm3 <- glm(paf ~ scenario*where, data = sthighergeog_l50, family = binomial(), weights = nspp) + +summary(paf_lm3) +``` + +Check residuals: + +```{r fig.height=6} +par(mfrow = c(2,2)) +plot(paf_lm3) +``` + +Try a Linear Mixed-Effects model. In this case, we will focus in modelling PAF ~ scenario, with "where" as a random effect. + +```{r} +paf_lme1 <- glmer(paf ~ scenario + (1|where), data = sthighergeog_l50, family = binomial(), + weights = nspp) + +summary(paf_lme1) +``` + +The source does explain some of the variation, but not a great amount compared to the scenarios. + +```{r} +par(mfrow = c(1,2)) +plot(paf_lme1) +qqnorm(resid(paf_lme1)) +qqline(resid(paf_lme1)) +``` + +Try now a model with higherGeography as a random effect: + +```{r} +paf_lme2 <- glmer(paf ~ scenario + (1|higherGeography), data = sthighergeog_l50, family = binomial(), weights = nspp) + +summary(paf_lme2) +``` + +In this case, the higherGeography have more influence on the values of PAF. This seems to be an interesting model to proceed with. + +Check residuals: + +```{r} +par(mfrow = c(1,2)) +plot(paf_lme2) +qqnorm(resid(paf_lme2)) +qqline(resid(paf_lme2)) +``` + +Both models show the trend of increase in affected species as we go to the worst case scenarios. + +Try to include the temperature trend on the model: + +```{r} +paf_lm_sst1 <- glm(paf ~ scenario + where + sstavg, data = sthighergeog_l50, family = binomial(), weights = nspp) + +summary(paf_lm_sst1) + +sthighergeog_l50_sc <- sthighergeog_l50 +sthighergeog_l50_sc$sstavg <- scale(sthighergeog_l50_sc$sstavg) + +paf_lme_sst1 <- glmer(paf ~ scenario + sstavg + (1|where), data = sthighergeog_l50_sc, family = binomial(), weights = nspp) + +summary(paf_lme_sst1) + +paf_gam_sst1 <- mgcv::gam(paf ~ scenario + where + s(sstavg, k = 4), data = sthighergeog_l50, family = binomial(), weights = nspp) + +summary(paf_gam_sst1) +``` + + +### Removing species close to the current temperature limits (tropical) + +```{r} +sst <- terra::rast("~/Research/mpa_europe/mpaeu_sdm/data/env/current/thetao_baseline_depthsurf_mean.tif") +sst <- as.data.frame(sst) + +plot(density(sst$thetao_mean)) + +sst_q <- quantile(sst$thetao_mean, 0.9) # We can try other values + +speciesthermsitedt <- speciesthermsitedt[q_1 <= sst_q,,] + +sthighergeog <- speciesthermsitedt[,list(ctiavg=mean(q_0.5), + sdcti=sd(q_0.5), + str=mean(q_0.9-q_0.1), + sstavg=mean(site_current), + nspp=.N, + pgt90now=sum(site_current>q_0.9)/.N, + pgt90ssp1d50=sum(site_ssp126_dec50>q_0.9)/.N, + pgt90ssp1d100=sum(site_ssp126_dec100>q_0.9)/.N, + pgt90ssp2d50=sum(site_ssp245_dec50>q_0.9)/.N, + pgt90ssp2d100=sum(site_ssp245_dec100>q_0.9)/.N, + pgt90ssp3d50=sum(site_ssp370_dec50>q_0.9)/.N, + pgt90ssp3d100=sum(site_ssp370_dec100>q_0.9)/.N, + pgt90ssp4d50=sum(site_ssp460_dec50>q_0.9)/.N, + pgt90ssp4d100=sum(site_ssp460_dec100>q_0.9)/.N, + pgt90ssp5d50=sum(site_ssp585_dec50>q_0.9)/.N, + pgt90ssp5d100=sum(site_ssp585_dec100>q_0.9)/.N), + by=c("higherGeography","where")] + +sthighergeog$where <- as.factor(sthighergeog$where) +sthighergeog$higherGeography <- as.factor(sthighergeog$higherGeography) + +sthighergeog_l <- data.table(tidyr::pivot_longer(sthighergeog, + cols = 8:18, + names_to = "scenario", values_to = "paf")) + +sthighergeog_l50 <- sthighergeog_l[grepl("d50|now", scenario),,] +sthighergeog_l100 <- sthighergeog_l[grepl("d100|now", scenario),,] + +sthighergeog_l50$scenario <- as.factor(sthighergeog_l50$scenario) +sthighergeog_l100$scenario <- as.factor(sthighergeog_l100$scenario) + +boxplot(paf ~ scenario, data = sthighergeog_l50) +``` + +Try again the same models: + +```{r} +paf_lm1_rs <- glm(paf ~ scenario, data = sthighergeog_l50, family = binomial(), weights = nspp) + +summary(paf_lm1_rs) +``` + +Check the residuals: + +```{r fig.height=6} +par(mfrow = c(2,2)) +plot(paf_lm1_rs) +``` + +QQ plot is not good, but the rest is ok. + +Model considering the source of the data: + +```{r} +paf_lm2_rs <- glm(paf ~ scenario + where, data = sthighergeog_l50, family = binomial(), weights = nspp) + +summary(paf_lm2_rs) +``` + +Check the residuals: + +```{r fig.height=6} +par(mfrow = c(2,2)) +plot(paf_lm2_rs) +``` + +Try to include the interaction between "where" and "scenario": + +```{r} +paf_lm3_rs <- glm(paf ~ scenario*where, data = sthighergeog_l50, family = binomial(), weights = nspp) + +summary(paf_lm3_rs) +``` + +Check residuals: + +```{r fig.height=6} +par(mfrow = c(2,2)) +plot(paf_lm3_rs) +``` + +Try a Linear Mixed-Effects model. In this case, we will focus in modelling PAF ~ scenario, with "where" as a random effect. + +```{r} +paf_lme1_rs <- glmer(paf ~ scenario + (1|where), data = sthighergeog_l50, family = binomial(), + weights = nspp) + +summary(paf_lme1_rs) +``` + +The source does explain some of the variation, but not a great amount compared to the scenarios. + +```{r} +par(mfrow = c(1,2)) +plot(paf_lme1_rs) +qqnorm(resid(paf_lme1_rs)) +qqline(resid(paf_lme1_rs)) +``` + +Try now a model with higherGeography as a random effect: + +```{r} +paf_lme2_rs <- glmer(paf ~ scenario + (1|higherGeography), data = sthighergeog_l50, family = binomial(), weights = nspp) + +summary(paf_lme2_rs) +``` + +The higherGeography have even more influence on the values of PAF. + +Check residuals: + +```{r} +par(mfrow = c(1,2)) +plot(paf_lme2_rs) +qqnorm(resid(paf_lme2_rs)) +qqline(resid(paf_lme2_rs)) +``` + + +## Thermal limits: extracted X experimental + +I obtained the thermal limits from the [GlobTherm dataset](https://datadryad.org/stash/dataset/doi:10.5061/dryad.1cv08). + +```{r} +suppressPackageStartupMessages(library(dplyr)) + +# Get only unique species - because we will use only the thermal limits, we can get from the speciesthermsitedt table +species_filt <- speciesthermsitedt[!duplicated(speciesthermsitedt$species),] + +# Load the GlobTherm dataset +globtherm <- suppressMessages(suppressWarnings(readxl::read_xlsx("~/Downloads/GlobalTherm_upload_10_11_17.xlsx"))) +globtherm_sp <- paste(globtherm$Genus, globtherm$Species) + +# See which species are available +gt_sel <- which(globtherm_sp %in% species_filt$species) +gt_sel <- globtherm[gt_sel,] +gt_sel <- gt_sel %>% + mutate(species = globtherm_sp[globtherm_sp %in% species_filt$species]) %>% + select(species, Tmax, max_metric) + +# See which metrics are available +table(gt_sel$max_metric) + +# Select only those that are on our list +species_filt_sel <- species_filt[species_filt$species %in% gt_sel$species,] +species_filt_sel <- species_filt_sel %>% + select(species, q_0.9) + +# Join both tables +tlimits <- species_filt_sel %>% + left_join(gt_sel) %>% + filter(!is.na(Tmax)) + +nrow(tlimits) + +# Plot +plot(tlimits$q_0.9 ~ tlimits$Tmax, xlim = c(0, 40), ylim = c(0,40)) +abline(a = 0, b = 1) + +m1 <- lm(q_0.9~Tmax, data = tlimits) +abline(m1, col = "red") +``` + +We see that for a great portion of the species, the species thermal maxima is actually much higher than the one we estimated through the occurrence data. + +It is also possible to use only "CTmax": + +```{r} + +gt_sel_ctmax <- gt_sel %>% + filter(max_metric == "ctmax") + +# Join both tables +tlimits_ctmax <- species_filt_sel %>% + left_join(gt_sel_ctmax) %>% + filter(!is.na(Tmax)) + +nrow(tlimits_ctmax) + +# Plot +plot(tlimits_ctmax$q_0.9 ~ tlimits_ctmax$Tmax, xlim = c(0, 40), ylim = c(0,40)) +abline(a = 0, b = 1) + +m2 <- lm(q_0.9~Tmax, data = tlimits_ctmax) +abline(m1, col = "red") +``` + diff --git a/codes/plot_analysis_occurrence_data.R b/codes/plot_analysis_occurrence_data.R new file mode 100644 index 0000000..1414a41 --- /dev/null +++ b/codes/plot_analysis_occurrence_data.R @@ -0,0 +1,256 @@ +#################### eDNA expeditions - scientific analysis #################### +########################## Environmental data download ######################### +# January of 2024 +# Author: Silas Principe, Mike Burrows +# Contact: s.principe@unesco.org; michael.burrows@sams.ac.uk +# +############## Plot eDNA data from UNESCO World Heritage Sites ################# + +# Load packages +library(dplyr) +library(stringr) +library(purrr) +library(arrow) +library(data.table) +library(ggplot2) +fs::dir_create("figures") + + +speciestherm<-read_parquet("results/species_tsummaries.parquet") + +speciesthermdt<-data.table(speciestherm) + +speciesthermsst<-speciesthermdt[depth=="depthsurf" & variant=="mean",,] +speciesthermsstc<-dcast(speciesthermdt,species~metric,value.var = "value",fun=mean) + +## Site by species occurrence table + +speciesthermsite<-read_parquet("results/tsummaries_aggregated.parquet") + +speciesthermsitedt<-data.table(speciesthermsite) + +speciesthermsitedt$where <- ifelse(speciesthermsitedt$where == "Both" | + speciesthermsitedt$where == "OBIS/GBIF", "Databases", "eDNA") + +### CTI by higher geography areas ##### +ctihighergeog<-speciesthermsitedt[,list(ctiavg=mean(q_0.5), + sdcti=sd(q_0.5), + str=mean(q_0.9-q_0.1), + sstavg=mean(site_current), + nspp=.N),by=c("higherGeography","where")] + + +# Save figures +par(mfrow=c(1,3)) +plot(data=ctihighergeog,ctiavg~sstavg,pch=16,col=unclass(as.factor(where)),cex=1.5,main="All species", + xlab="Sea surface temperature (°C)", ylab="Community Temperature Index (°C)") +abline(a=0,b=1,lty=3) +legend("topleft",legend=levels(as.factor(ctihighergeog$where)),inset=0.02,col=1:3,pt.cex=1.5,pch=16) + +plot(data=ctihighergeog,sdcti~sstavg,pch=16,col=unclass(as.factor(where)),cex=1.5,main="All species", + xlab="Sea surface temperature (°C)", ylab="SD CTI (°C)") +#abline(a=0,b=1,lty=3) +legend("bottomleft",legend=levels(as.factor(ctihighergeog$where)),inset=0.02,col=1:3,pt.cex=1.5,pch=16) + +plot(data=ctihighergeog,str~sstavg,pch=16,col=unclass(as.factor(where)),cex=1.5,main="All species", + xlab="Sea surface temperature (°C)", ylab="Species thermal range (average, °C)") +#abline(a=0,b=1,lty=3) +legend("bottomleft",legend=levels(as.factor(ctihighergeog$where)),inset=0.02,col=1:3,pt.cex=1.5,pch=16) + + +(p1 <- ggplot(ctihighergeog) + + geom_abline(slope = 1, intercept = 0, color = "grey90") + + geom_point(aes(x = sstavg, y = ctiavg, colour = where, size = nspp), alpha = .5) + + scale_color_manual(values = c("#00c3c9", "#f58000")) + #"#5151db", + scale_y_continuous(limits = c(5, 30)) + + scale_x_continuous(limits = c(5, 30)) + + scale_size(range = c(2,7)) + + guides(colour = guide_legend(override.aes = list(alpha = 1, size = 3))) + + ylab("Community Thermal Index (°C)") + + xlab("Sea Surface Temperature (°C)") + + theme_light() + + theme(panel.grid = element_blank(), + panel.grid.major.y = element_line(colour = "grey90", linewidth = 0.3, linetype = 2), + legend.title = element_blank())) + +(p2 <- ggplot(ctihighergeog) + + geom_point(aes(x = sstavg, y = sdcti, colour = where, size = nspp), alpha = .5) + + scale_color_manual(values = c("#00c3c9", "#f58000")) + #"#5151db", + # scale_y_continuous(limits = c(5, 30)) + + # scale_x_continuous(limits = c(5, 30)) + + scale_size(range = c(2,7)) + + guides(colour = guide_legend(override.aes = list(alpha = 1, size = 3))) + + ylab("SD CTI (°C)") + + xlab("Sea Surface Temperature (°C)") + + theme_light() + + theme(panel.grid = element_blank(), + panel.grid.major.y = element_line(colour = "grey90", linewidth = 0.3, linetype = 2), + legend.title = element_blank())) + +(p3 <- ggplot(ctihighergeog) + + geom_point(aes(x = sstavg, y = str, colour = where, size = nspp), alpha = .5) + + scale_color_manual(values = c("#00c3c9", "#f58000")) + #"#5151db", + # scale_y_continuous(limits = c(5, 30)) + + # scale_x_continuous(limits = c(5, 30)) + + scale_size(range = c(2,7)) + + guides(colour = guide_legend(override.aes = list(alpha = 1, size = 3))) + + ylab("Species thermal range (average, °C)") + + xlab("Sea Surface Temperature (°C)") + + theme_light() + + theme(panel.grid = element_blank(), + panel.grid.major.y = element_line(colour = "grey90", linewidth = 0.3, linetype = 2), + legend.title = element_blank())) + +library(patchwork) +p1 + p2 + p3 + patchwork::plot_layout(guides = "collect") + plot_annotation(tag_levels = 'A') + +ggsave("figures/thermal_comp_allsp.png", width = 14, height = 5) + + +### ALTERNATIVE PLOT +for (i in 1:100) { + + resampled <- speciesthermsitedt[, list( + q_0.5 = sample(q_0.5, .N, replace = T), + q_0.9 = sample(q_0.9, .N, replace = T), + q_0.1 = sample(q_0.1, .N, replace = T), + site_current = sample(site_current, .N, replace = T) + #resample = 1:.N#sample(1:.N, .N, replace = T) + ), by = c("higherGeography", "where")] + + ctihighergeog_temp <- resampled[, list( + ctiavg = mean(q_0.5), + sdcti = sd(q_0.5), + str = mean(q_0.9 - q_0.1), + sstavg = mean(site_current), + nspp = .N + ), by = c("higherGeography", "where")] + + if (i == 1) { + ctihighergeog_boot <- ctihighergeog_temp + } else { + ctihighergeog_boot <- rbind(ctihighergeog_boot, ctihighergeog_temp) + } + +} + + + +### Bootstrap version +ctihighergeog$higherGeography <- substr(ctihighergeog$higherGeography, 1, 10) +ctihighergeog$higherGeography <- factor(ctihighergeog$higherGeography, + levels = unique(ctihighergeog$higherGeography[order(ctihighergeog$sstavg)])) + +ctihighergeog_boot$higherGeography <- substr(ctihighergeog_boot$higherGeography, 1, 10) +ctihighergeog_boot$higherGeography <- factor(ctihighergeog_boot$higherGeography, + levels = levels(ctihighergeog$higherGeography)) + +ctihighergeog_boot_sum <- ctihighergeog_boot[, list( + ctiavg_low = quantile(ctiavg, 0.25), + ctiavg_median = median(ctiavg), + ctiavg_high = quantile(ctiavg, 0.75), + sdcti_low = quantile(sdcti, 0.25), + sdcti_median = median(sdcti), + sdcti_high = quantile(sdcti, 0.75), + str_low = quantile(str, 0.25), + str_median = median(str), + str_high = quantile(str, 0.75), + nspp = mean(nspp) +), by = c("higherGeography", "where")] + +ctihighergeog_sst <- ctihighergeog_boot[,list( + sstavg = mean(sstavg) +), by = c("higherGeography", "where")] +ctihighergeog_sst$what = "Average site SST" + +(p1_boot <- ggplot() + + geom_point(data = ctihighergeog_sst, aes(x = higherGeography, y = sstavg, fill = what), + shape = 15, size = 4, alpha = .1) + + geom_jitter(data = ctihighergeog_boot, aes(x = higherGeography, y = ctiavg, color = where), + shape = 16, size = 1, alpha = .05, + position = position_jitterdodge(dodge.width = 0.5, + jitter.height = 0, jitter.width = 0.1)) + + geom_linerange(data = ctihighergeog_boot_sum, + aes(x = higherGeography, ymin = ctiavg_low, ymax = ctiavg_high, + color = where), linewidth = 1, + position = position_dodge(width = 0.5)) + + geom_point(data = ctihighergeog, aes(x = higherGeography, y = ctiavg, color = where, size = nspp), + shape = 16, position = position_dodge(width = 0.5), alpha = .6) + + scale_color_manual(values = c("#00c3c9", "#f58000"), guide = "none") + #"#5151db", + scale_fill_manual(values = c("grey70")) + + scale_size(range = c(2,7), guide = "none") + + #guides(fill = guide_legend(override.aes = list(alpha = 1))) + + ylab("Temperature (°C)") + + xlab(NULL) + + theme_light() + + coord_flip() + + theme(panel.grid = element_blank(), + panel.grid.major.y = element_line(colour = "grey90", linewidth = 0.3, linetype = 2), + legend.title = element_blank())) + + + +p1_boot + p2 + p3 + patchwork::plot_layout(guides = "collect") + plot_annotation(tag_levels = 'A') + +ggsave("figures/test_newcti_boot.png", width = 14, height = 5) + + + + + + + + + +### Add taxon information + +specieslist<-read_parquet("results/species_list.parquet") + +speciesthermsitedt1<-merge(speciesthermsitedt,specieslist,by="species") + +fishthermsitedt<-speciesthermsitedt1[group=="fish",,] + +### CTI by fish only ##### + + +fishctihighergeog<-fishthermsitedt[,list(ctiavg=mean(q_0.5), + sdcti=sd(q_0.5), + str=mean(q_0.9-q_0.1), + sstavg=mean(site_current), + pgt90now=sum(site_current>q_0.9)/.N, + pgt90ssp2=sum(site_ssp245_dec50>q_0.9)/.N, + nspp=.N),by=c("higherGeography","where")] + +par(mfrow=c(1,3)) +plot(data=fishctihighergeog,ctiavg~sstavg,pch=16,col=unclass(as.factor(where)),cex=1.5, + xlab="Sea surface temperature (°C)", ylab="Community Temperature Index (°C)",main="Fish") +abline(a=0,b=1,lty=3) +legend("topleft",legend=levels(as.factor(fishctihighergeog$where)),inset=0.02,col=1:3,pt.cex=1.5,pch=16) + +plot(data=fishctihighergeog,sdcti~sstavg,pch=16,col=unclass(as.factor(where)),cex=1.5, + xlab="Sea surface temperature (°C)", ylab="SD CTI (°C)",main="Fish") +#abline(a=0,b=1,lty=3) +legend("bottomleft",legend=levels(as.factor(fishctihighergeog$where)),inset=0.02,col=1:3,pt.cex=1.5,pch=16) + +plot(data=fishctihighergeog,str~sstavg,pch=16,col=unclass(as.factor(where)),cex=1.5, + xlab="Sea surface temperature (°C)", ylab="Species thermal range (average, °C)",main="Fish") +#abline(a=0,b=1,lty=3) +legend("bottomleft",legend=levels(as.factor(fishctihighergeog$where)),inset=0.02,col=1:3,pt.cex=1.5,pch=16) +dev.off() + +### Proportion of species living above their T90 temperatures + +par(mfrow=c(1,2)) +plot(data=fishctihighergeog,pgt90now~sstavg,pch=16,col=unclass(as.factor(where)),cex=1.5,ylim=c(0,1), + xlab="Sea surface temperature (°C)", ylab="Proportion of species with SST>T90",main="Fish") +abline(a=0,b=1,lty=3) +legend("topleft",legend=levels(as.factor(fishctihighergeog$where)),inset=0.02,col=1:3,pt.cex=1.5,pch=16) + +plot(data=fishctihighergeog,pgt90ssp2~sstavg,pch=16,col=unclass(as.factor(where)),cex=1.5,ylim=c(0,1), + xlab="Sea surface temperature (°C)", ylab="Proportion of species with SST>T90 under SSP2",main="Fish") +abline(a=0,b=1,lty=3) +legend("topleft",legend=levels(as.factor(fishctihighergeog$where)),inset=0.02,col=1:3,pt.cex=1.5,pch=16) + + + + diff --git a/codes/plot_paf.R b/codes/plot_paf.R new file mode 100644 index 0000000..aef7e3e --- /dev/null +++ b/codes/plot_paf.R @@ -0,0 +1,225 @@ +#################### eDNA expeditions - scientific analysis #################### +########################## Environmental data download ######################### +# January of 2024 +# Author: Silas Principe, Mike Burrows +# Contact: s.principe@unesco.org; michael.burrows@sams.ac.uk +# +############## Plot eDNA data from UNESCO World Heritage Sites ################# + +# Load packages ---- +library(dplyr) +library(terra) +library(tidyr) +library(stringr) +library(purrr) +library(arrow) +library(data.table) +library(patchwork) +library(ggplot2) +library(fishualize) +library(ggdist) +library(see) +fs::dir_create("figures") + +# Load data ---- +# Load functional traits data +funcdiv <- read.csv("data/funcdiversity.csv", sep = "\t") +table(funcdiv$Habitat) +summary(funcdiv$DepthRangeDeep) + +# Select shallow water species +summary(funcdiv$DepthRangeDeep) # There are 1348 species with NA values + +# Limit to those with maximum depth 200m +sel_species <- funcdiv %>% + filter(!is.na(DepthRangeDeep)) %>% + filter(DepthRangeDeep <= 200) + + + +# Load temperature summaries by site +speciesthermsite <- read_parquet("results/tsummaries_aggregated.parquet") + +# Process the data ---- +# Convert to data.table +speciesthermsitedt <- data.table(speciesthermsite) + +# Change `where` column IDs +speciesthermsitedt$where <- ifelse( + speciesthermsitedt$where == "Both" | speciesthermsitedt$where == "OBIS/GBIF", + "Databases", + "eDNA" +) + +# Load species list +specieslist <- read_parquet("results/species_list.parquet") + +# Merge group information +speciesthermsitedt_gp <- merge(speciesthermsitedt, specieslist[,c("species", "group")], by = "species") + +# Filter for fish only +fishthermsitedt <- speciesthermsitedt_gp[group == "fish", , ] + +# Convert to longer format +fishthermsitedt_long <- fishthermsitedt %>% + filter(species %in% unique(sel_species$X)) %>% # Remove this line to not filter by the traits + pivot_longer(cols = starts_with("site"), names_to = "scenario", + values_to = "sst") %>% + filter(scenario %in% c("site_current", "site_ssp126_dec50", "site_ssp245_dec50", + "site_ssp370_dec50", "site_ssp585_dec50")) + +# Create a function to see at which scenario the species become at risk +scenario_order <- c("site_current", "site_ssp126_dec50", + "site_ssp585_dec50") +get_class <- function(x) { + if(all(x < 0)) { + return("not_risk") + } else { + return(scenario_order[min(which(x > 0))]) + } +} + +# Retrieve sites names and latitudes +sites <- vect("data/shapefiles/marine_world_heritage.gpkg") +sites$latitude <- unlist(sapply(1:length(sites), function(x){ + unname(terra::geom(centroids(sites[x,]))[,4]) +})) +sites_org <- data.frame(sites = sites$name, latitude = sites$latitude) + +# Change names of sites +sites_org <- sites_org %>% + rename(higherGeography = sites) %>% + mutate(higherGeography = case_when( + higherGeography == "Wadden Sea" ~ "Wadden Sea", + higherGeography == "Archipiélago de Revillagigedo" ~ "Revillagigedo", + higherGeography == "iSimangaliso Wetland Park" ~ "iSimangaliso", + higherGeography == "Ningaloo Coast" ~ "Ningaloo coast", + higherGeography == "Socotra Archipelago" ~ "Socotra Archipelago", + higherGeography == "Belize Barrier Reef Reserve System" ~ "Belize Barrier Reef", + higherGeography == "Cocos Island National Park" ~ "Cocos Island", + higherGeography == "Lord Howe Island Group" ~ "Lord Howe Island", + higherGeography == "Sundarbans National Park" ~ "Sundarbans", + higherGeography == "Península Valdés" ~ "Peninsula Valdes", + higherGeography == "Gulf of Porto: Calanche of Piana, Gulf of Girolata, Scandola Reserve" ~ "Scandola", + higherGeography == "Coiba National Park and its Special Zone of Marine Protection" ~ "Coiba", + higherGeography == "Lagoons of New Caledonia: Reef Diversity and Associated Ecosystems" ~ "Lagoons of New Caledonia", + higherGeography == "Shark Bay, Western Australia" ~ "Shark Bay", + higherGeography == "Tubbataha Reefs Natural Park" ~ "Tubbataha Reefs", + higherGeography == "Brazilian Atlantic Islands: Fernando de Noronha and Atol das Rocas Reserves" ~ "Fernando de Noronha", + higherGeography == "Everglades National Park" ~ "Everglades", + higherGeography == "Aldabra Atoll" ~ "Aldabra Atoll", + higherGeography == "Banc d'Arguin National Park" ~ "Banc d'Arguin", + higherGeography == "French Austral Lands and Seas" ~ "French Austral Lands", + )) %>% + filter(!is.na(higherGeography)) %>% + arrange(desc(latitude)) %>% + distinct(higherGeography, .keep_all = T) + +# Update names on the longer object +fishthermsitedt_long <- fishthermsitedt_long %>% + mutate(higherGeography = case_when( + higherGeography == "wadden sea" ~ "Wadden Sea", + higherGeography == "archipielago de revillagigedo" ~ "Revillagigedo", + higherGeography == "isimangaliso wetland park" ~ "iSimangaliso", + higherGeography == "ningaloo coast" ~ "Ningaloo coast", + higherGeography == "socotra archipelago" ~ "Socotra Archipelago", + higherGeography == "belize barrier reef reserve system" ~ "Belize Barrier Reef", + higherGeography == "cocos island national park" ~ "Cocos Island", + higherGeography == "lord howe island group" ~ "Lord Howe Island", + higherGeography == "the sundarbans" ~ "Sundarbans", + higherGeography == "peninsula valdes" ~ "Peninsula Valdes", + higherGeography == "gulf of porto calanche of piana gulf of girolata scandola reserve" ~ "Scandola", + higherGeography == "coiba national park and its special zone of marine protection" ~ "Coiba", + higherGeography == "lagoons of new caledonia reef diversity and associated ecosystems" ~ "Lagoons of New Caledonia", + higherGeography == "shark bay western australia" ~ "Shark Bay", + higherGeography == "tubbataha reefs natural park" ~ "Tubbataha Reefs", + higherGeography == "brazilian atlantic islands fernando de noronha and atol das rocas reserves" ~ "Fernando de Noronha", + higherGeography == "everglades national park" ~ "Everglades", + higherGeography == "aldabra atoll" ~ "Aldabra Atoll", + higherGeography == "banc d arguin national park" ~ "Banc d'Arguin", + higherGeography == "french austral lands and seas" ~ "French Austral Lands", + )) + +# Get the number of species in each site +sites_species <- fishthermsitedt_long %>% + group_by(higherGeography, species) %>% + summarise(n = n()) %>% + group_by(higherGeography) %>% + summarise(n = n()) + +# Join with the sites object +sites_org <- left_join(sites_org, sites_species) + +# Create the plot object with all information +plot_obj <- fishthermsitedt_long %>% + filter(scenario %in% c("site_current", "site_ssp126_dec50", "site_ssp585_dec50")) %>% + select(species, higherGeography, q_0.95, scenario, sst) %>% + mutate(differences = sst - q_0.95) %>% + group_by(higherGeography, species) %>% + mutate(q_0.95 = mean(q_0.95), point_class = get_class(differences)) %>% + mutate(higherGeography = factor(higherGeography, levels = rev(sites_org$higherGeography), + labels = rev(paste(sites_org$higherGeography, paste0("(", sites_org$n, ")"))))) +plot_obj$point_class <- factor(plot_obj$point_class, levels = c("not_risk", "site_current", "site_ssp126_dec50", "site_ssp585_dec50")) + +# Extract sites SST +sites_sst <- fishthermsitedt_long %>% + filter(scenario %in% c("site_current", "site_ssp126_dec50", "site_ssp585_dec50")) %>% + group_by(higherGeography, scenario) %>% + summarise(sst = mean(sst)) %>% + mutate(point_class = scenario) %>% + left_join(sites_org[,c("higherGeography", "n")]) %>% + mutate(higherGeography = paste(higherGeography, paste0("(", n, ")"))) +sites_sst$point_class <- factor(sites_sst$point_class, levels = c("not_risk", "site_current", "site_ssp126_dec50", "site_ssp585_dec50")) + + +# Make plots ---- +# Left plot - dots +plot_a <- plot_obj %>% + ggplot() + + geom_violinhalf(aes(x = higherGeography, y = q_0.95), + position = position_nudge(x = 0.3)) + + geom_jitter(aes(x = higherGeography, y = q_0.95, color = point_class), alpha = .3, + width = 0.12, shape = 16, show.legend = F) + + geom_linerange(data = sites_sst, aes(ymin = sst-0.1, ymax = sst+0.1, x = higherGeography, color = point_class), + linewidth = 7, show.legend = T) + + scale_color_manual(values = c("grey90", "#d4b9da", "#df65b0", "#980043"), + labels = c("Not at risk", "Current", "SSP1", "SSP5")) + + coord_flip() + + ylab("Temperature (°C)") + xlab(NULL) + + theme_light() + + theme(panel.border = element_blank(), + panel.grid.minor.x = element_blank(), + panel.grid.major.y = element_blank(), + legend.title = element_blank(), + legend.position = "bottom", + strip.background = element_blank(), + strip.text = element_text(color = "black"), + plot.margin = unit(c(0.1,0.1,0.1,0), "cm"));plot_a + +# Right plot - bars +plot_b <- plot_obj %>% + group_by(higherGeography, scenario) %>% + summarise(percentage = sum(differences > 0)/length(differences)) %>% + ggplot() + + geom_bar(aes(x = higherGeography, y = percentage, fill = scenario), stat = "identity", position="dodge", width = .6) + + scale_fill_manual(values = c("#d4b9da", "#df65b0", "#980043")) + + scale_y_continuous(labels = c("0%", "", "50%", "", "100%")) + + coord_flip() + + ylab("Potentially Affected Fraction") + xlab(NULL) + + theme_light() + + theme(panel.border = element_blank(), + panel.grid.minor.x = element_blank(), + panel.grid.major.y = element_blank(), + axis.text.y = element_blank(), + axis.ticks.y = element_blank(), + legend.title = element_blank(), + legend.position = "none", + strip.background = element_blank(), + strip.text = element_text(color = "black"), + plot.margin = unit(c(0,0,0,0), "cm"));plot_b + +# Put together +plot_a + plot_b + plot_layout(widths = c(4, 1)) + +# Save +ggsave("figures/paf_fishes_filtered_v1.png", width = 12, height = 7) \ No newline at end of file diff --git a/codes/plot_temperatures_glossy.R b/codes/plot_temperatures_glossy.R new file mode 100644 index 0000000..37cc02a --- /dev/null +++ b/codes/plot_temperatures_glossy.R @@ -0,0 +1,136 @@ +# Plot SST maps with differences for glossy report + +# Load packages +library(terra) +library(ggplot2) +library(patchwork) + +# Set directory +sh <- "../../../mpa_europe/mpaeu_sdm/data/env/" + +# Load environmental layers +current <- rast(paste0(sh, "current/thetao_baseline_depthsurf_mean.tif")) +ssp1 <- rast(paste0(sh, "future/ssp126/thetao_ssp126_depthsurf_dec50_mean.tif")) +ssp5 <- rast(paste0(sh, "future/ssp585/thetao_ssp585_depthsurf_dec50_mean.tif")) + +# Aggregate for faster plotting +current <- aggregate(current, 2) +ssp1 <- aggregate(ssp1, 2) +ssp5 <- aggregate(ssp5, 2) + +# Get deltas +ssp1_d <- ssp1 - current +ssp5_d <- ssp5 - current + +# Convert to data.frame +current_df <- as.data.frame(current, xy = T) +ssp1_d_df <- as.data.frame(ssp1_d, xy = T) +ssp5_d_df <- as.data.frame(ssp5_d, xy = T) + +# Change column names +colnames(ssp1_d_df)[3] <- colnames(ssp5_d_df)[3] <- "delta" +colnames(current_df)[3] <- "val" + +# Verify range for breaks +range(ssp1_d_df$delta) +range(ssp5_d_df$delta) +# See how many are above 2.5, as difference is small +length(ssp5_d_df$delta[ssp5_d_df$delta > 2.5]) + +# Add extremes on one small cell to overcome ggplot limits +ssp1_d_df[1,3] <- -1.5 +ssp1_d_df[2,3] <- 2.5 +ssp5_d_df[1,3] <- -1.5 +ssp5_d_df[2,3] <- 2.5 + +# Change high values of SSP5 (i.e 2.53) to be within the scale +ssp5_d_df$delta[ssp5_d_df$delta > 2.5] <- 2.5 + +# Load world shapefile +world <- rnaturalearth::ne_countries(scale = 110, returnclass = "sf") + +# Make SST plot +a <- ggplot() + + geom_sf(data = world, color = "white", fill = "white") + + geom_contour_filled(data = current_df, aes(x = x, y = y, z = val)#, + #breaks = c(-1.5, -1, -0.5, 0, 0.5, 1, 1.5, 2)#, + #color = "grey80", linewidth = 0.2 + ) + + scale_fill_brewer(palette = "BuPu", direction = 1, + guide = guide_coloursteps(show.limits = T), + name = "SST (°C)") + + coord_sf(expand = F, label_axes = list(bottom = "", right = "")) + + ggtitle("Current period") + + theme_light() + + theme(plot.background = element_blank(), + #panel.border = element_blank(), + axis.title = element_blank(), + legend.key.height = unit(0.03, "npc"), + legend.key.width = unit(0.1, "npc"), + legend.position = "bottom", + legend.title.position = "top", + legend.justification = "center", + legend.title = element_text(hjust = 0.5), + panel.grid = element_blank());a + +# Make SSP1 plot +b <- ggplot() + + geom_sf(data = world, color = "white", fill = "white") + + geom_contour_filled(data = ssp1_d_df, aes(x = x, y = y, z = delta), + breaks = c(-1.5, -1, -0.5, 0, 0.5, 1, 1.5, 2, 2.5)#, + #color = "grey80", linewidth = 0.2 + ) + + scale_fill_manual(values = rev(c("#B2182B", "#D6604D", "#F4A582", "#FDDBC7", "#F7F7F7", "#D1E5F0", "#92C5DE", "#4393C3")), + guide = guide_coloursteps(show.limits = T), + name = "Difference in temperature (°C)") + + coord_sf(expand = F, + label_axes = list(bottom = "", right = "")) + + ggtitle("SSP1 (2050)") + + theme_light() + + theme(plot.background = element_blank(), + #panel.border = element_blank(), + axis.title = element_blank(), + legend.key.height = unit(0.03, "npc"), + legend.key.width = unit(0.1, "npc"), + legend.position = "bottom", + legend.title.position = "top", + legend.justification = "center", + legend.title = element_text(hjust = 0.5), + panel.grid = element_blank());b + +# Make SSP5 plot +c <- ggplot() + + geom_sf(data = world, color = "white", fill = "white") + + geom_contour_filled(data = ssp5_d_df, aes(x = x, y = y, z = delta), + breaks = c(-1.5, -1, -0.5, 0, 0.5, 1, 1.5, 2, 2.5)#, + #color = "grey80", linewidth = 0.2 + ) + + scale_fill_manual(values = rev(c("#B2182B", "#D6604D", "#F4A582", "#FDDBC7", "#F7F7F7", "#D1E5F0", "#92C5DE", "#4393C3")), + guide = guide_coloursteps(show.limits = T), + name = "Difference in temperature (°C)") + + coord_sf(expand = F, + label_axes = list(bottom = "", right = "")) + + ggtitle("SSP5 (2050)") + + theme_light() + + theme(plot.background = element_blank(), + #panel.border = element_blank(), + axis.title = element_blank(), + legend.key.height = unit(0.03, "npc"), + legend.key.width = unit(0.1, "npc"), + legend.position = "bottom", + legend.title.position = "top", + legend.justification = "center", + legend.title = element_text(hjust = 0.5), + panel.grid = element_blank());c + +# Compose +pc <- ((plot_spacer() + a + plot_layout(guides = "keep")) | (b + c + plot_layout(guides = "collect"))) & theme(legend.position = "bottom", + legend.key.height = unit(0.025, "npc"), + legend.key.width = unit(0.04, "npc"), + legend.title = element_text(hjust = 0.5, size = 12), + legend.text = element_text(hjust = 0.5, size = 12)) + +# Save +ggsave(filename = "figures/sst_isomaps.png", plot = pc, width = 28, height = 8) + +# END \ No newline at end of file diff --git a/codes/sdm_components_mhs.R b/codes/sdm_components_mhs.R new file mode 100644 index 0000000..09d689f --- /dev/null +++ b/codes/sdm_components_mhs.R @@ -0,0 +1,443 @@ +############################## MPA Europe project ############################## +########### WP3 - Species and biogenic habitat distributions (UNESCO) ########## +# March of 2024 +# Authors: Silas C. Principe, Pieter Provoost +# Contact: s.principe@unesco.org +# +################## Components for main modelling function ###################### + +# Loading main data ---- +.cm_load_species <- function(species, species_dataset) { + # Load species data + if (tools::file_ext(species_dataset) == "parquet") { + if (utils::file_test("-d", species_dataset)) { + ds <- pl$scan_parquet(file.path(species_dataset, "**/*")) + } else { + ds <- pl$scan_parquet(species_dataset) + } + species_data <- ds$filter(pl$col("taxonID") == species)$collect() + species_data <- species_data$to_data_frame() + } else if (tools::file_ext(species_dataset) %in% c("sqlite", "db")) { + + con <- DBI::dbConnect(RSQLite::SQLite(), species_dataset) + + species_name <- worrms::wm_id2name(species) + + sel_species <- paste0("'", species_name, "'", collapse = ", ") + + db_query <- glue::glue(" + select species, h3, records + from occurrence + where species in ({sel_species}) + ") + + db_res <- DBI::dbSendQuery(con, db_query) + species_data <- DBI::dbFetch(db_res) + + DBI::dbDisconnect(con) + + species_data_coord <- h3jsr::cell_to_point(species_data$h3) + species_data_coord <- sf::st_coordinates(species_data_coord) + colnames(species_data_coord) <- c("decimalLongitude", "decimalLatitude") + + species_data <- dplyr::bind_cols(species_data, species_data_coord) + species_data$data_type <- "fit_points" + species_data$dataset_sel <- NA + species_data$taxonID <- species + + require(polars) + fao_areas <- pl$scan_parquet("data/fao_areas.parquet") + + sp_sbase <- try(rfishbase::species(list(species_name))) + + if (!inherits(sp_sbase, "try-error")) { + if (nrow(sp_sbase) < 1) { + sp_sbase <- try(rfishbase::species(list(species_name), + server = "sealifebase")) + } + } + + if (!inherits(sp_sbase, "try-error") & nrow(sp_sbase) > 0) { + fao_areas_sp <- fao_areas$filter(pl$col("SpecCode") == sp_sbase$SpecCode[1])$ + collect() + fao_areas_sp <- fao_areas_sp$to_data_frame() + + fao_areas_sp <- fao_areas_sp %>% + filter(Status %in% c("endemic", "Endemic", "native", "Native", "introduced", "Introduced")) + + if (nrow(fao_areas_sp) > 0) { + fao_shp <- terra::vect("data/shapefiles/World_Fao_Zones.shp") + + fao_shp_sel <- fao_shp[fao_shp$zone %in% fao_areas_sp$AreaCode] + + if (length(fao_shp_sel) > 0) { + sf::sf_use_s2(FALSE) + fao_shp_sel <- suppressMessages( + suppressWarnings( + sf::st_buffer(sf::st_make_valid(sf::st_as_sf(fao_shp_sel)), 0.2) + ) + ) + + is_onarea <- terra::is.related(terra::vect(species_data[,c("decimalLongitude", "decimalLatitude")], geom = c("decimalLongitude", "decimalLatitude")), + terra::vect(fao_shp_sel), + "intersects") + is_onarea <- ifelse(is_onarea, 1, 0) + + species_data$fao_confirmed <- is_onarea + } else { + species_data$fao_confirmed <- NA + } + + } else { + warning("Impossible to retrieve area information from FishBase/SeaLifeBase or no native areas") + } + } else { + warning("Impossible to retrieve species information from FishBase/SeaLifeBase") + } + + if (!"fao_confirmed" %in% colnames(species_data)) { + species_data$fao_confirmed <- NA + } + + + } else if (any(grepl("key", list.files(species_dataset, recursive = T)))) { + lf <- list.files(species_dataset, recursive = T) + lf <- lf[grepl(paste0("key=", species), lf)] + if (length(lf) > 0) { + species_data <- arrow::read_parquet(paste0(species_dataset, "/", lf)) + } else { + species_data <- matrix(nrow = 0, ncol = 1) + } + } else { + cli::cli_abort("No folder or files were found in {.path {species_dataset}}") + } + + return(species_data) +} + +# Check coastal ---- +.cm_check_coastal <- function(species_data, env, coord_names, verbose) { + + if ("coastal" %in% names(env$hypothesis)) { + # Test if is within europe/coastal + europe_starea <- vect("data/shapefiles/mpa_europe_starea_v2.shp") + europe_starea <- ext(europe_starea) + + is_within <- is.related(vect(species_data[,coord_names], geom = coord_names), + europe_starea, "intersects") + if (!all(is_within)) { + env$hypothesis <- env$hypothesis[names(env$hypothesis) != "coastal"] + env$layers <- subset(env$layers, "wavefetch", negate = T) + if (verbose) cli::cli_alert_warning("Coastal hypothesis found but discarded") + } else { + + wave <- rast("data/env/terrain/wavefetch.tif") + wave_pts <- extract(wave, species_data[,coord_names]) + + if (sum(is.na(wave_pts[,2])) > ceiling(nrow(species_data) * .05)) { + env$hypothesis <- env$hypothesis[names(env$hypothesis) != "coastal"] + if (verbose) cli::cli_alert_warning("Coastal hypothesis found but discarded") + } else if ((nrow(species_data) - sum(is.na(wave_pts[,2]))) < 15) { + env$hypothesis <- env$hypothesis[names(env$hypothesis) != "coastal"] + if (verbose) cli::cli_alert_warning("Coastal hypothesis found but discarded") + } else { + env$layers <- crop(env$layers, europe_starea) + } + } + } + + return(env) +} + +# Check ecoregions ---- +.cm_check_ecoregions <- function(ecoregions, fit_pts, eval_pts, + env, limit_by_depth, depth_buffer) { + + # Check which eco-regions are covered by the points + ecoreg_unique <- ecoregions$Realm[is.related(ecoregions, + vect(bind_rows(fit_pts, eval_pts), + geom = coord_names), + "intersects")] + + model_log$model_details$ecoregions <- ecoreg_unique + ecoreg_occ <- ecoregions[ecoregions$Realm %in% ecoreg_unique,] + + # Apply a buffer to ensure that all areas are covered + sf::sf_use_s2(FALSE) + ecoreg_occ_buff <- suppressMessages( + suppressWarnings(vect(sf::st_buffer(sf::st_as_sf(ecoreg_occ), 0.2))) + ) + + adj_ecoreg <- ecoregions$Realm[is.related(ecoregions, ecoreg_occ_buff, + "intersects")] + + # Mask areas + ecoreg_sel <- ecoregions[ecoregions$Realm %in% unique( + c(ecoreg_unique, adj_ecoreg) + ),] + model_log$model_details$ecoregions_included <- unique(ecoreg_sel$Realm) + + # Apply a buffer to ensure that all areas are covered + sf::sf_use_s2(FALSE) + ecoreg_sel <- suppressMessages( + suppressWarnings(vect(sf::st_buffer(sf::st_as_sf(ecoreg_sel), 0.5))) + ) + + # Limit to max extension based on distance to the points + max_ext <- ext(vect(rbind(fit_pts, eval_pts), geom = coord_names)) + max_ext <- ext(as.vector(max_ext) + c(-20, 20, -20, 20)) + ecoreg_sel <- crop(ecoreg_sel, max_ext) + + # Limit by depth if TRUE + if (limit_by_depth) { + if (verbose) cli::cli_alert_info("Limiting by depth") + # To decide if this step will be kept: + # Load bathymetry layer + bath <- rast("data/env/terrain/bathymetry_mean.tif") + bath <- mask(crop(bath, ecoreg_sel), ecoreg_sel) + + bath_pts <- terra::extract(bath, bind_rows(fit_pts, eval_pts)) + + bath_range <- range(bath_pts[,2]) + bath_range[1] <- bath_range[1] - depth_buffer + bath_range[2] <- ifelse((bath_range[2] + depth_buffer) > 0, + 0, bath_range[2] + depth_buffer) + + bath[bath < bath_range[1] | bath > bath_range[2]] <- NA + + if ("coastal" %in% names(env$hypothesis)) { + bath <- crop(bath, europe_starea) + env$layers <- mask(crop(env$layers, ecoreg_sel), bath) + env$layers <- mask(env$layers, env$layers$wavefetch) + } else { + env$layers <- mask(crop(env$layers, ecoreg_sel), bath) + } + + } else { + if ("coastal" %in% names(env$hypothesis)) { + ecoreg_sel <- crop(ecoreg_sel, europe_starea) + env$layers <- mask(env$layers, ecoreg_sel) + } else { + env$layers <- mask(env$layers, ecoreg_sel) + } + } + + return(env) +} + +# Prepare data object ---- +.cm_prepare_data_obj <- function(fit_pts, eval_pts, env, verbose = FALSE) { + # Assess SAC + fit_pts_sac <- try(obissdm::outqc_sac(fit_pts, + env_layers = subset(env$layers, env$hypothesis[[1]]), + plot_result = FALSE, + verbose = verbose)) + + if (inherits(fit_pts_sac, "try-error")) { + fit_pts_sac <- fit_pts + } + + # Make data object + quad_n <- ifelse(nrow(as.data.frame(env$layers, xy = T, na.rm = T)) < 50000, + round(nrow(as.data.frame(env$layers, xy = T, na.rm = T))), 50000) + + sp_data <- mp_prepare_data(fit_pts_sac, eval_data = eval_pts, + species_id = species_name, + env_layers = env$layers, + quad_number = quad_n, + verbose = verbose) + + block_grid <- get_block_grid(sp_data, env$layers, + sel_vars = env$hypothesis$basevars, + verbose = verbose) + + sp_data <- mp_prepare_blocks(sp_data, + method = "manual", + block_types = "spatial_grid", + n_iterate = 300, + retry_if_zero = TRUE, + manual_shp = block_grid, + verbose = verbose) + + return(sp_data) +} + + +# Assess bias (TODO) ---- +.cm_bias_assess <- function(sp_data, env) { + + # Add some way of assessing spatial bias + model_log$model_details$control_bias <- correct_bias + + # Get spatstat statistics and point process for control + spat_im <- as.im(as.data.frame(aggregate(env$layers[[1]], 10, na.rm = T), xy = T)) + spat_window <- as.owin(spat_im) + # Define ppp object based on point locations + spat_ppp <- ppp(x = sp_data$coord_training$decimalLongitude[sp_data$training$presence == 1], + y = sp_data$coord_training$decimalLatitude[sp_data$training$presence == 1], + window = spat_window) + # calculate envelope around L-hat estimates. + spat_env_k <- envelope(spat_ppp, Kest, verbose = F) + spat_env_l <- envelope(spat_ppp, Lest, verbose = F) + + # Point process model (?) + spat_quad <- ppp(x = sp_data$coord_training$decimalLongitude[sp_data$training$presence == 0], + y = sp_data$coord_training$decimalLatitude[sp_data$training$presence == 0], + window = spat_window) + spat_quad_sc <- quadscheme(data = spat_ppp, dummy = spat_quad, method = "dirichlet") + spat_data_obj <- lapply(1:nlyr(env$layers), function(x){ + as.im(as.data.frame(aggregate(env$layers[[x]], 4, na.rm = T), xy = T)) + }) + names(spat_data_obj) <- names(env$layers) + spat_ppm <- ppm(spat_quad_sc, + trend = as.formula(paste("~", + paste(names(spat_data_obj)[ + names(spat_data_obj) %in% env$hypothesis[[1]] + ], collapse = "+"))), + covariates = spat_data_obj) + + spat_pred <- predict(spat_ppm, covariates = spat_data_obj, ngrid = c(spat_data_obj[[1]]$dim[1], spat_data_obj[[1]]$dim[2])) + + if (correct_bias) { + bias_layer <- density(spat_ppp, sigma = 1) + bias_layer <- rast(bias_layer) + bias_layer <- disagg(bias_layer, 10) + crs(bias_layer) <- crs("EPSG:4326") + bias_layer <- project(bias_layer, env$layers[[1]]) + bias_layer <- mask(bias_layer, env$layers[[1]]) + } + + return(list()) + +} + +# Check good models ---- +.cm_check_good_models <- function(model_fits, tg_metrics = "cbi", tg_threshold = 0.3) { + + # Get what models are above threshold + good_models <- lapply(model_fits, function(model){ + if (!is.null(model)) { + cv_res <- model$cv_metrics + cv_res <- apply(cv_res, 2, mean, na.rm = T) + the_metric <- cv_res[[tg_metrics]] + if (!is.na(the_metric)) { + if (the_metric >= tg_threshold) { + if (sum(is.na(model$cv_metrics[[tg_metrics]])) >= 4) { + return(FALSE) + } else { + return(TRUE) + } + } else { + return(FALSE) + } + } else { + return(FALSE) + } + } else { + return(FALSE) + } + }) + + good_models <- which(unlist(good_models)) + + return(good_models) +} + +# Predict models ---- +.cm_predict_models <- function(good_models, model_fits, best_hyp, hab_depth, + outfolder, species, outacro, + verbose) { + + # Predict models + model_predictions <- lapply(good_models, function(id) { + model_name <- model_fits[[id]]$name + + #best_hyp <- multi_mod_max$best_model + + outpath <- paste0(outfolder, "/taxonid=", species, "/model=", outacro, "/predictions/") + + if (!dir.exists(outpath)) fs::dir_create(outpath) + + outmess <- paste0(outpath, "taxonid=", species, "_model=", outacro, "_what=mess.tif") + + if (!file.exists(gsub("mess", "mess_cog", outmess))) {do_mess <- TRUE} else {do_mess <- FALSE} + + scenarios <- data.frame( + scenario = c("current", rep(c("ssp116", "ssp245", "ssp370", "ssp460", "ssp585"), + each = 2)), + year = c(NA, rep(c("dec50", "dec100"), 5)) + ) + + for (k in 1:nrow(scenarios)) { + + if (is.na(scenarios$year[k])) { + period <- NULL + } else { + period <- scenarios$year[k] + } + + if (verbose) cli::cli_alert_info("Predicting scenario {k} of {nrow(scenarios)}.") + + env_to_pred <- obissdm::get_envofgroup(group, + depth = hab_depth, load_all = F, + scenario = scenarios$scenario[k], + period = period, + hypothesis = best_hyp, + env_folder = "data/env", + conf_file = "sdm_conf.yml", + verbose = verbose) + + pred <- predict(model_fits[[id]], env_to_pred) + + names(pred) <- paste0(scenarios$scenario[k], ifelse(is.null(period), "", paste0("_", period))) + + if (do_mess) { + # Save MESS + if (!exists("to_mess")) { + maxpt <- nrow(as.data.frame(env_to_pred, xy = T, na.rm = T)) + to_mess <- sample(1:maxpt, + ifelse(maxpt > 10000, 10000, maxpt)) + } + mess_map <- ecospat::ecospat.mess( + na.omit(as.data.frame(env_to_pred, xy = T)[to_mess,]), + cbind(sp_data$coord_training, sp_data$training[,2:ncol(sp_data$training)])) + mess_map_t <- env_to_pred[[1]] + mess_map_t[] <- NA + mess_map_t[cellFromXY(mess_map_t, mess_map[,1:2])] <- mess_map[,5] + mess_map <- mask(mess_map_t, env_to_pred[[1]]) + + names(mess_map) <- names(pred) <- paste0(scenarios$scenario[k], ifelse(is.null(period), "", paste0("_", period))) + + if (k == 1) { + pred_mess <- mess_map + } else { + pred_mess <- c(pred_mess, mess_map) + } + } + + + if (k == 1) { + pred_f <- pred + } else { + pred_f <- c(pred_f, pred) + } + } + + writeRaster(pred_f, paste0(outpath, "taxonid=", species, "_model=", outacro, "_method=", model_name,".tif"), + overwrite = T) + + if (do_mess) { + writeRaster(pred_mess, outmess, overwrite = T) + cogeo_optim(outmess) + } + + return(pred_f[["current"]]) + + }) + + return(model_predictions) +} + +.cm_get_respcurves <- function() { + +} \ No newline at end of file diff --git a/codes/sdm_model_fishes.R b/codes/sdm_model_fishes.R new file mode 100644 index 0000000..eb8a3f0 --- /dev/null +++ b/codes/sdm_model_fishes.R @@ -0,0 +1,180 @@ +############################## MPA Europe project ############################## +########### WP3 - Species and biogenic habitat distributions (UNESCO) ########## +# March of 2024 +# Authors: Silas C. Principe, Pieter Provoost +# Contact: s.principe@unesco.org +# +################## Run multiple species distribution models #################### + + +# Load packages ---- +library(obissdm) +library(furrr) +library(progressr) +library(storr) +library(polars) +library(arrow) +library(dplyr) +library(DBI) +library(terra) +source("../../obis/research/marineheritage_sst/functions/sdm_model_function.R") +set.seed(2023) +handlers("cli") +options("progressr.enable" = TRUE) +setwd("~/Research/mpa_europe/mpaeu_sdm") +fs::dir_create("../../obis/research/marineheritage_sst/results/sdms") + +# Define settings ---- + +# General +# The output folder +outfolder <- "../../obis/research/marineheritage_sst/results/sdms" +# An unique code for identifying this model run +outacro <- "mhs" +# Run in parallel? +run_parallel <- FALSE +# Number of cores for parallel processing +n_cores <- 4 + +# Modelling +# Algorithms to be used +algos <- c("maxent") +# Should areas be masked by the species depth? +limit_by_depth <- TRUE +# A buffer to be applied to the depth limitation +depth_buffer <- 500 +# Assess spatial bias? +assess_bias <- FALSE +# Try to correct spatial bias? +correct_bias <- FALSE + +# Create storr to hold results +st <- storr_rds(paste0(outacro, "_storr")) +# If does not exist, add start date +if (!st$exists("startdate")) { + st$set("startdate", format(Sys.Date(), "%Y%m%d")) +} +# Should the storr object be destructed at the end if the model succeeded? +destroy_storr <- FALSE + +# Get list of species +splist <- read_parquet("~/Research/obis/research/marineheritage_sst/results/species_list.parquet") + +fishes <- splist[splist$group == "fish", ] + +head(fishes) + +fishes$where <- apply(fishes[,3:5], 1, function(x){ + if (x[1] | x[2]) { + "databases" + } else if (!x[1] & !x[2]) { + "edna" + } +}) + +fishes$sdm_group <- "fishes" + + + +# Fit models ---- +# Create a function to save results in a storr object for better control +pmod <- function(sp, gp, sdat, outf, outac, alg, lmd, lmd_buf, assb, corb, p) { + + p() + + if (st$exists(sp)) { + if (st$get(as.character(sp))[[1]] %in% c("done", "succeeded")) { + to_do <- FALSE + } else { + to_do <- TRUE + } + } else { + to_do <- TRUE + } + + if (to_do) { + st$set(sp, "running") + + sp_name <- fishes$species[fishes$AphiaID == sp][1] + + sel_species <- paste0("'", sp_name, "'") + + con <- dbConnect(RSQLite::SQLite(), "~/Research/mpa_europe/mpaeu_shared/occurrence_h3_db.sqlite") + + query <- glue::glue(' + select h3, species, records + from occurrence + where species in ({sel_species}) + ') + + # Get results + res <- dbSendQuery(con, query) + records <- dbFetch(res) + + dbDisconnect(con) + + # Get occurrence coordinates + rec_coords <- h3jsr::cell_to_point(records$h3) + rec_coords <- sf::st_coordinates(rec_coords) + colnames(rec_coords) <- c("decimalLongitude", "decimalLatitude") + rec_coords <- as.data.frame(rec_coords) + rec_coords$species <- sp_name + rec_coords$data_type <- "fit_points" + + #print(head(rec_coords)) + + fit_result <- try(model_species(species = sp, + group = gp, + species_data = rec_coords, + outfolder = outf, + outacro = outac, + algorithms = alg, + limit_by_depth = lmd, + depth_buffer = lmd_buf, + assess_bias = assb, + correct_bias = corb, verbose = T), + silent = F) + + if (!inherits(fit_result, "try-error")) { + st$set(sp, fit_result) + } else { + st$set(sp, list(status = "failed", + error = fit_result)) + } + } + + return(invisible(NULL)) +} + +# Run models according to the strategy +if (run_parallel) { + plan(multisession, workers = n_cores) + + with_progress({ + p <- progressor(steps = nrow(species_list)) + result <- future_map2(species_list$taxonID, species_list$sdm_group, pmod, + outf = outfolder, + outac = outacro, alg = algos, lmd = limit_by_depth, + lmd_buf = depth_buffer, assb = assess_bias, + corb = correct_bias, + p = p, .options = furrr_options(seed = T)) + }) +} else { + with_progress({ + p <- progressor(steps = nrow(fishes[1:2,])) + result <- purrr::map2(fishes$AphiaID[1:2], fishes$sdm_group[1:2], pmod, + outf = outfolder, + outac = outacro, alg = algos, lmd = limit_by_depth, + lmd_buf = depth_buffer, assb = assess_bias, + corb = correct_bias, + p = p) + }) +} + + + +# Check results ---- +# Check if everything was processed +cli::cli_alert_warning("{.val {length(st$list())}} out of {.val {nrow(species_list)}} model{?s} processed.") + +# And if so, destroy storr object \ No newline at end of file diff --git a/codes/sdm_model_fishes_mhs.R b/codes/sdm_model_fishes_mhs.R new file mode 100644 index 0000000..bb672c8 --- /dev/null +++ b/codes/sdm_model_fishes_mhs.R @@ -0,0 +1,203 @@ +############################## MPA Europe project ############################## +########### WP3 - Species and biogenic habitat distributions (UNESCO) ########## +# March of 2024 +# Authors: Silas C. Principe, Pieter Provoost +# Contact: s.principe@unesco.org +# +################## Run multiple species distribution models #################### + + +# Load packages ---- +library(obissdm) +library(furrr) +library(progressr) +library(storr) +library(polars) +library(arrow) +library(dplyr) +library(terra) +source("codes/sdm_components_mhs.R") +source("codes/sdm_model_function_mhs.R") + +setwd("~/Research/mpa_europe/mpaeu_sdm/") +source("functions/auxiliary_modelfit.R") +set.seed(2023) +handlers("cli") +options("progressr.enable" = TRUE) + + +# Define settings ---- +# This file can also be run as "source". In that case, the default objects +# are used. To enable also a more flexible run, for testing purposes, three +# arguments can be override by creating the objects on the environment before. +# `Those are outfolder`, `outacro`, and `sel_species`. The latter have as +# default the value "all", what means that all species in the species list will +# be modeled. Supplying a vector of AphiaID will filter the species based on the +# supplied values. +# +# We create a small function to perform those checks +check_exists <- function(object_name, if_not) { + if (exists(object_name)) { + return(eval(parse(text = object_name))) + } else { + return(if_not) + } +} + +# General +# The output folder +outfolder <- check_exists("outfolder", "~/Research/obis/research/marineheritage_sst/results/sdms") +# An unique code for identifying this model run +outacro <- check_exists("outacro", "mhs") +# What will be modelled? +# 'all' will model all species. Supply a vector of AphiaIDs to filter +sel_species <- check_exists("sel_species", "all") +# The path for the species data dataset +species_dataset <- "~/Research/mpa_europe/mpaeu_shared/occurrence_h3_db.sqlite" +# The path for the species list +species_list <- "~/Research/obis/research/marineheritage_sst/results/fish_paf_species.csv"#"~/Research/obis/research/marineheritage_sst/results/fish_paf_sdmlist.csv" +# Run in parallel? For avoiding parallel, change both to FALSE +run_parallel <- ifelse(length(sel_species) > 1 | sel_species == "all", TRUE, FALSE)[1] +# Number of cores for parallel processing +n_cores <- 4 + +# Modelling +# Algorithms to be used +algos <- c("maxent") +# Should areas be masked by the species depth? +limit_by_depth <- TRUE +# A buffer to be applied to the depth limitation +depth_buffer <- 500 +# Assess spatial bias? +assess_bias <- FALSE +# Try to correct spatial bias? +correct_bias <- FALSE + +# Create storr to hold results +st <- storr_rds(paste0(outacro, "_storr")) +# If does not exist, add start date +if (!st$exists("startdate")) { + st$set("startdate", format(Sys.Date(), "%Y%m%d")) +} +# Should the storr object be destructed at the end if the model succeeded? +destroy_storr <- FALSE + + +# Define species ---- +species_list <- read.csv(species_list) +species_list$sdm_group <- "fishes" +species_list <- species_list %>% + distinct(species, .keep_all = T) +species_list <- species_list[order(species_list$records),] + +if (length(sel_species) > 1 | !any("all" %in% sel_species)) { + species_list <- species_list %>% + filter(AphiaID %in% sel_species) +} + + +# Output for control +cli::cat_rule() +cli::cat_line(cli::col_cyan("MPA EUROPE PROJECT - WP3 Species and biogenic habitat distributions")) +cli::cat_line("Run species distribution models for multiple species in parallel") +cli::cat_line() +cli::cli_inform("Running SDM for {nrow(species_list)} species") +cli::cli_inform("Chosen algorithm{?s}: {.val {algos}}") +if (!limit_by_depth) { + cli::cli_inform(c("x" = "Not limiting by depth")) +} else { + cli::cli_inform(c("v" = "Limiting by depth using {.val {depth_buffer}} buffer")) +} +cli::cat_line() +cli::cli_inform("Outputing to folder {.path {outfolder}} using acronym {.val {outacro}}") +if (dir.exists(outfolder)) { + cli::cli_alert_warning("Folder already exists, files may be overwritten!") +} else { + fs::dir_create(outfolder) +} +if (run_parallel) { + cli::cli_alert_warning("Running in parallel using {n_cores} cores") +} else { + cli::cli_alert_warning("Running in sequence") +} +cli::cat_rule() + + + + +# Fit models ---- +# Create a function to save results in a storr object for better control +pmod <- function(sp, gp, sdat, outf, outac, alg, lmd, lmd_buf, assb, corb, p) { + + p() + + if (st$exists(sp)) { + if (st$get(as.character(sp))[[1]] %in% c("done", "succeeded", "low_data", + "failed", "no_good_model")) { + to_do <- FALSE + } else { + to_do <- TRUE + } + } else { + to_do <- TRUE + } + + if (to_do) { + st$set(sp, "running") + + fit_result <- try(model_species(species = sp, + group = gp, + species_dataset = sdat, + outfolder = outf, + outacro = outac, + algorithms = alg, + limit_by_depth = lmd, + depth_buffer = lmd_buf, + assess_bias = assb, + correct_bias = corb, + verbose = FALSE), + silent = T) + + if (!inherits(fit_result, "try-error")) { + st$set(sp, fit_result) + } else { + st$set(sp, list(status = "failed", + error = fit_result)) + } + } + + return(invisible(NULL)) +} + +# Run models according to the strategy +if (run_parallel) { + plan(multisession, workers = n_cores) + + with_progress({ + p <- progressor(steps = nrow(species_list)) + result <- future_map2(species_list$AphiaID, species_list$sdm_group, pmod, + sdat = species_dataset, outf = outfolder, + outac = outacro, alg = algos, lmd = limit_by_depth, + lmd_buf = depth_buffer, assb = assess_bias, + corb = correct_bias, + p = p, .options = furrr_options(seed = T)) + }) +} else { + with_progress({ + p <- progressor(steps = nrow(species_list)) + result <- purrr::map2(species_list$AphiaID, species_list$sdm_group, pmod, + sdat = species_dataset, outf = outfolder, + outac = outacro, alg = algos, lmd = limit_by_depth, + lmd_buf = depth_buffer, assb = assess_bias, + corb = correct_bias, + p = p) + }) +} + + + +# Check results ---- +# Check if everything was processed +cli::cli_alert_warning("{.val {length(st$list())}} out of {.val {nrow(species_list)}} model{?s} processed.") + +# And if so, destroy storr object \ No newline at end of file diff --git a/codes/sdm_model_function_mhs.R b/codes/sdm_model_function_mhs.R new file mode 100644 index 0000000..60532be --- /dev/null +++ b/codes/sdm_model_function_mhs.R @@ -0,0 +1,941 @@ +############################## MPA Europe project ############################## +########### WP3 - Species and biogenic habitat distributions (UNESCO) ########## +# March of 2024 +# Authors: Silas C. Principe, Pieter Provoost +# Contact: s.principe@unesco.org +# +########################### Main modelling function ############################ + + +#' Model species distribution according to MPA Europe framework +#' +#' @param species AphiaID of the species +#' @param group group of the species, according to the configuration file +#' @param species_dataset the path for the Parquet file containing species +#' occurrences +#' @param outfolder the folder to output the results +#' @param outacro an unique acronym for the modelling +#' @param algorithms which algorithms to fit. See [obissdm::sdm_options()] for a +#' list of available algorithms +#' @param limit_by_depth should the study area be limited by depth? If so, the +#' depth is extracted from occurrence records and after applying the buffer +#' the layers are masked. Recommended to keep `TRUE` +#' @param depth_buffer the depth buffer to be added in case `limit_by_depth = +#' TRUE`. Can be 0 to apply no buffer (not recommended) +#' @param assess_bias if `TRUE`, perform tests for potential spatial bias on +#' occurrence records +#' @param correct_bias if `TRUE`, apply a bias layer to try to correct the +#' spatial bias +#' @param verbose if `TRUE` print essential messages. Can also be numeric: 0 for +#' no messages, 1 for progress messages and 2 for all messages. +#' +#' @return nothing, saved files +#' @export +#' +#' @details +#' This function is a wrapper around functions from the [obissdm] package, to +#' provide a consistent pipeline for generating the SDMs according to the +#' MPA Europe project. +#' +#' Output will be in the format: +#' {outfolder}/taxonid={AphiaID}/model={outacro}/predictions OR models OR metrics/ +#' +#' - predictions: contains all raster files, optimized for cloud (COG) +#' - models: contain model object in zip files +#' - metrics: contain relevant metrics +#' +#' A single `.json` file is saved on the root of the model containing details +#' of the model fit. Also a `.parquet` file is saved with the occurrence data +#' used to fit the model (i.e. already filtered and standardized) +#' +#' @examples +#' \dontrun{ +#' model_species(124316, "benthic_invertebrates", "sp_dataset.parquet", +#' "results", "mr1") +#' } +model_species <- function(species, + group, + species_dataset, + outfolder, + outacro, + algorithms = c("maxent", "rf", "brt", "lasso"), + limit_by_depth = TRUE, + depth_buffer = 500, + assess_bias = TRUE, + correct_bias = TRUE, + verbose = FALSE) { + + # Check verbosity + verb_1 <- verb_2 <- FALSE + if (is.numeric(verbose)) { + if (verbose == 1) { + verb_1 <- TRUE + } else if (verbose == 2) { + verb_1 <- verb_2 <- TRUE + } + } else { + if (verbose) { + verb_1 <- TRUE + } + } + + if (verb_1) cli::cli_alert_info("Starting model for species {species}") + + # Record timing + treg <- obissdm::.get_time() + + # Define global objects and fill log + coord_names <- c("decimalLongitude", "decimalLatitude") + model_log <- obissdm::gen_log(algorithms) + model_log$taxonID <- species + model_log$model_acro <- outacro + model_log$model_date <- Sys.Date() + + # Load species data + if (verbose) cli::cli_alert_info("Reading data") + species_data <- .cm_load_species(species, species_dataset) + + treg <- obissdm::.get_time(treg, "Species data loading") + + # If data is available, proceeds + if (nrow(species_data) >= 15) { + + if (verbose) cli::cli_alert_info("Enough number of points, proceeding") + # PART 1: DATA LOADING ---- + # Load species information + species_name <- species_data$species[1] + model_log$scientificName <- species_name + + # Load ecological information + eco_info <- arrow::open_csv_dataset("data/species_ecoinfo.csv") %>% + filter(taxonID == species) %>% + collect() + + if (nrow(eco_info) < 1) { + eco_info <- obissdm::mp_get_ecoinfo( + species_list = species, + outfile = NULL, + return_table = TRUE, + show_progress = FALSE + ) + } + + # Select ecological information for the species + hab <- eco_info$mode_life + hab_depth <- hab_to_depth(hab) + + # Load environmental data + env <- get_envofgroup(group, depth = hab_depth, load_all = T, + conf_file = "sdm_conf.yml", verbose = verbose) + + env <- .cm_check_coastal(species_data, env, coord_names, verbose) + + + # Load ecoregions shapefile + ecoregions <- vect("data/shapefiles/MarineRealms.shp") + + # Split the dataset + fit_pts <- split_ds(species_data) + eval_pts <- split_ds(species_data, "eval") + + treg <- obissdm::.get_time(treg, "Data loading") + + + + # PART 2: DATA PREPARING ---- + if (verbose) cli::cli_alert_info("Preparing data") + # Check which eco-regions are covered by the points + ecoreg_unique <- ecoregions$Realm[is.related(ecoregions, + vect(bind_rows(fit_pts, eval_pts), + geom = coord_names), + "intersects")] + + model_log$model_details$ecoregions <- ecoreg_unique + ecoreg_occ <- ecoregions[ecoregions$Realm %in% ecoreg_unique,] + + # Apply a buffer to ensure that all areas are covered + sf::sf_use_s2(FALSE) + ecoreg_occ_buff <- suppressMessages( + suppressWarnings(vect(sf::st_buffer(sf::st_as_sf(ecoreg_occ), 0.2))) + ) + + adj_ecoreg <- ecoregions$Realm[is.related(ecoregions, ecoreg_occ_buff, + "intersects")] + + # Mask areas + ecoreg_sel <- ecoregions[ecoregions$Realm %in% unique( + c(ecoreg_unique, adj_ecoreg) + ),] + model_log$model_details$ecoregions_included <- unique(ecoreg_sel$Realm) + + # Apply a buffer to ensure that all areas are covered + sf::sf_use_s2(FALSE) + ecoreg_sel <- suppressMessages( + suppressWarnings(vect(sf::st_buffer(sf::st_as_sf(ecoreg_sel), 0.5))) + ) + + # Limit to max extension based on distance to the points + max_ext <- ext(vect(rbind(fit_pts, eval_pts), geom = coord_names)) + max_ext <- ext(as.vector(max_ext) + c(-20, 20, -20, 20)) + ecoreg_sel <- crop(ecoreg_sel, max_ext) + + # Limit by depth if TRUE + if (limit_by_depth) { + if (verbose) cli::cli_alert_info("Limiting by depth") + # To decide if this step will be kept: + # Load bathymetry layer + bath <- rast("data/env/terrain/bathymetry_mean.tif") + bath <- mask(crop(bath, ecoreg_sel), ecoreg_sel) + + bath_pts <- terra::extract(bath, bind_rows(fit_pts, eval_pts)) + + bath_range <- range(bath_pts[,2]) + bath_range[1] <- bath_range[1] - depth_buffer + bath_range[2] <- ifelse((bath_range[2] + depth_buffer) > 0, + 0, bath_range[2] + depth_buffer) + + bath[bath < bath_range[1] | bath > bath_range[2]] <- NA + + if ("coastal" %in% names(env$hypothesis)) { + bath <- crop(bath, europe_starea) + env$layers <- mask(crop(env$layers, ecoreg_sel), bath) + env$layers <- mask(env$layers, env$layers$wavefetch) + } else { + env$layers <- mask(crop(env$layers, ecoreg_sel), bath) + } + + model_log$model_details$limited_by_depth <- TRUE + model_log$model_details$depth_buffer <- depth_buffer + + } else { + if ("coastal" %in% names(env$hypothesis)) { + ecoreg_sel <- crop(ecoreg_sel, europe_starea) + env$layers <- mask(env$layers, ecoreg_sel) + } else { + env$layers <- mask(env$layers, ecoreg_sel) + } + } + + # Assess SAC + fit_pts_sac <- try(obissdm::outqc_sac(fit_pts, + env_layers = subset(env$layers, env$hypothesis[[1]]), + plot_result = FALSE, + verbose = verbose)) + + if (inherits(fit_pts_sac, "try-error")) { + fit_pts_sac <- fit_pts + } + + # Make data object + quad_n <- ifelse(nrow(as.data.frame(env$layers, xy = T, na.rm = T)) < 50000, + round(nrow(as.data.frame(env$layers, xy = T, na.rm = T))), 50000) + + sp_data <- mp_prepare_data(fit_pts_sac, eval_data = eval_pts, + species_id = species_name, + env_layers = env$layers, + quad_number = quad_n, + verbose = verbose) + + block_grid <- get_block_grid(sp_data, env$layers, + sel_vars = env$hypothesis$basevars, + verbose = verbose) + + sp_data <- mp_prepare_blocks(sp_data, + method = "manual", + block_types = "spatial_grid", + n_iterate = 300, + retry_if_zero = TRUE, + manual_shp = block_grid, + verbose = verbose) + + model_log$model_details$block_size <- unname(sp_data$blocks$grid_resolution) + model_log$model_fit_points <- sum(sp_data$training$presence) + model_log$model_eval_points <- sum(sp_data$eval_data$presence) + + treg <- obissdm::.get_time(treg, "Data preparing") + + # PART 3: ASSESS SPATIAL BIAS ---- + if (assess_bias) { + if (verbose) cli::cli_alert_info("Assessing spatial bias") + # Add some way of assessing spatial bias + model_log$model_details$control_bias <- correct_bias + + # Get spatstat statistics and point process for control + spat_im <- as.im(as.data.frame(aggregate(env$layers[[1]], 10, na.rm = T), xy = T)) + spat_window <- as.owin(spat_im) + # Define ppp object based on point locations + spat_ppp <- ppp(x = sp_data$coord_training$decimalLongitude[sp_data$training$presence == 1], + y = sp_data$coord_training$decimalLatitude[sp_data$training$presence == 1], + window = spat_window) + # calculate envelope around L-hat estimates. + spat_env_k <- envelope(spat_ppp, Kest, verbose = F) + spat_env_l <- envelope(spat_ppp, Lest, verbose = F) + + # Point process model (?) + spat_quad <- ppp(x = sp_data$coord_training$decimalLongitude[sp_data$training$presence == 0], + y = sp_data$coord_training$decimalLatitude[sp_data$training$presence == 0], + window = spat_window) + spat_quad_sc <- quadscheme(data = spat_ppp, dummy = spat_quad, method = "dirichlet") + spat_data_obj <- lapply(1:nlyr(env$layers), function(x){ + as.im(as.data.frame(aggregate(env$layers[[x]], 4, na.rm = T), xy = T)) + }) + names(spat_data_obj) <- names(env$layers) + spat_ppm <- ppm(spat_quad_sc, + trend = as.formula(paste("~", + paste(names(spat_data_obj)[ + names(spat_data_obj) %in% env$hypothesis[[1]] + ], collapse = "+"))), + covariates = spat_data_obj) + + spat_pred <- predict(spat_ppm, covariates = spat_data_obj, ngrid = c(spat_data_obj[[1]]$dim[1], spat_data_obj[[1]]$dim[2])) + + if (correct_bias) { + bias_layer <- density(spat_ppp, sigma = 1) + bias_layer <- rast(bias_layer) + bias_layer <- disagg(bias_layer, 10) + crs(bias_layer) <- crs("EPSG:4326") + bias_layer <- project(bias_layer, env$layers[[1]]) + bias_layer <- mask(bias_layer, env$layers[[1]]) + } + + treg <- obissdm::.get_time(treg, "Sample bias assessment") + } + + + # PART 4: MODEL SELECTION ---- + if (verbose) cli::cli_alert_info("Model selection") + multi_mod_max <- sdm_multhypo(sdm_data = sp_data, sdm_method = "maxent", + variables = env$hypothesis, + verbose = verbose) + + if (correct_bias) { + if (verbose) cli::cli_alert_info("Testing bias correction") + temp_data <- sp_data + temp_data$training <- temp_data$training[, c("presence", multi_mod_max$best_variables)] + temp_data$eval_data <- temp_data$eval_data[, c("presence", multi_mod_max$best_variables)] + + temp_data$training$biasgrid <- extract(bias_layer, temp_data$coord_training, ID = F)[,1] + temp_data$eval_data$biasgrid <- extract(bias_layer, temp_data$coord_eval, ID = F)[,1] + + not_na <- !is.na(temp_data$training$biasgrid) + not_na_eval <- !is.na(temp_data$eval_data$biasgrid) + + temp_data$training <- temp_data$training[not_na,] + temp_data$coord_training <- temp_data$coord_training[not_na,] + + temp_data$eval_data <- temp_data$eval_data[not_na_eval,] + temp_data$coord_eval <- temp_data$coord_eval[not_na_eval,] + + max_bias <- sdm_fit(temp_data) + + bias_metrics <- apply(max_bias$cv_metrics, 2, mean, na.rm = T) + model_metrics <- apply(multi_mod_max$model$cv_metrics, 2, mean, na.rm = T) + + if (bias_metrics[["cbi"]] > (model_metrics[["cbi"]] + 0.1)) { + # TODO: continue here + # TODO: bias layer needs to be other, not from the single species + # group or something - to see + } + } + + # Prepare data for the best model + sp_data$training <- sp_data$training[, c("presence", multi_mod_max$best_variables)] + sp_data$eval_data <- sp_data$eval_data[, c("presence", multi_mod_max$best_variables)] + + treg <- obissdm::.get_time(treg, "Model selection") + + + # PART 4: FIT MODELS ---- + if (verbose) cli::cli_alert_info("Starting model fitting") + model_fits <- list() + for (i in 1:length(algorithms)) { + if (verbose) cli::cli_alert_info("Fitting {algorithms[i]}") + model_fits[[i]] <- try(do.call(paste0("sdm_module_", algorithms[i]), list(sp_data, verbose = verbose))) + if (inherits(model_fits[[i]], "try-error")) { + model_log$model_result[[algorithms[i]]] <- "failed" + model_fits[[i]] <- NULL + } else { + model_log$model_result[[algorithms[i]]] <- "succeeded" + } + } + + # For some reason was not registering in the log... using for instead + # model_fits <- lapply(algorithms, function(algo) { + # fit <- try(do.call(paste0("sdm_module_", algo), list(sp_data))) + # if (inherits(fit, "try-error")) { + # model_log$model_result[[algo]] <- "failed" + # return(NULL) + # } else { + # model_log$model_result[[algo]] <- "succeeded" + # return(fit) + # } + # }) + + treg <- obissdm::.get_time(treg, "Model fit") + + + if (any(unlist(model_log$model_result) == "succeeded")) { + if (verbose) cli::cli_alert_info("Model fitting concluded. Checking if models are good") + # PART 5: PREDICTION ---- + + # Define thresholds + tg_metrics <- "cbi" + tg_threshold <- 0.3 + + # Get what models are above threshold + good_models <- lapply(model_fits, function(model){ + if (!is.null(model)) { + cv_res <- model$cv_metrics + cv_res <- apply(cv_res, 2, mean, na.rm = T) + the_metric <- cv_res[[tg_metrics]] + if (!is.na(the_metric)) { + if (the_metric >= tg_threshold) { + if (sum(is.na(model$cv_metrics[[tg_metrics]])) >= 4) { + return(FALSE) + } else { + return(TRUE) + } + } else { + return(FALSE) + } + } else { + return(FALSE) + } + } else { + return(FALSE) + } + }) + + good_models <- which(unlist(good_models)) + + if (length(good_models) > 0) { + if (verbose) cli::cli_alert_info("Good models available, predicting.") + # Predict models + model_predictions <- lapply(good_models, function(id) { + model_name <- model_fits[[id]]$name + + best_hyp <- multi_mod_max$best_model + + outpath <- paste0(outfolder, "/taxonid=", species, "/model=", outacro, "/predictions/") + + if (!dir.exists(outpath)) fs::dir_create(outpath) + + outmess <- paste0(outpath, "taxonid=", species, "_model=", outacro, "_what=mess.tif") + + if (!file.exists(gsub("mess", "mess_cog", outmess))) {do_mess <- TRUE} else {do_mess <- FALSE} + + scenarios <- data.frame( + scenario = c("current", rep(c("ssp126", "ssp245", "ssp370", "ssp585"), + each = 1)), + year = c(NA, rep(c("dec50"), 4)) + ) + + for (k in 1:nrow(scenarios)) { + + if (is.na(scenarios$year[k])) { + period <- NULL + } else { + period <- scenarios$year[k] + } + + if (verbose) cli::cli_alert_info("Predicting scenario {k} of {nrow(scenarios)}.") + + env_to_pred <- obissdm::get_envofgroup(group, + depth = hab_depth, load_all = F, + scenario = scenarios$scenario[k], + period = period, + hypothesis = best_hyp, + env_folder = "data/env", + conf_file = "sdm_conf.yml", + verbose = verbose) + + pred <- predict(model_fits[[id]], env_to_pred) + + names(pred) <- paste0(scenarios$scenario[k], ifelse(is.null(period), "", paste0("_", period))) + + if (do_mess) { + # Save MESS + if (!exists("to_mess")) { + maxpt <- nrow(as.data.frame(env_to_pred, xy = T, na.rm = T)) + to_mess <- sample(1:maxpt, + ifelse(maxpt > 10000, 10000, maxpt)) + } + mess_map <- ecospat::ecospat.mess( + na.omit(as.data.frame(env_to_pred, xy = T)[to_mess,]), + cbind(sp_data$coord_training, sp_data$training[,2:ncol(sp_data$training)])) + mess_map_t <- env_to_pred[[1]] + mess_map_t[] <- NA + mess_map_t[cellFromXY(mess_map_t, mess_map[,1:2])] <- mess_map[,5] + mess_map <- mask(mess_map_t, env_to_pred[[1]]) + + names(mess_map) <- names(pred) <- paste0(scenarios$scenario[k], ifelse(is.null(period), "", paste0("_", period))) + + if (k == 1) { + pred_mess <- mess_map + } else { + pred_mess <- c(pred_mess, mess_map) + } + } + + + if (k == 1) { + pred_f <- pred + } else { + pred_f <- c(pred_f, pred) + } + } + + writeRaster(pred_f, paste0(outpath, "taxonid=", species, "_model=", outacro, "_method=", model_name,".tif"), + overwrite = T) + + if (do_mess) { + writeRaster(pred_mess, outmess, overwrite = T) + cogeo_optim(outmess) + } + + return(pred_f[["current"]]) + + }) + + treg <- obissdm::.get_time(treg, "Model prediction") + + + + # PART X: GET RESPONSE CURVES ---- + if (verbose) cli::cli_alert_info("Getting response curves") + # Predict models + model_respcurves <- lapply(good_models, function(id) { + + model_name <- model_fits[[id]]$name + + outpath <- paste0(outfolder, "/taxonid=", species, "/model=", outacro, "/metrics/") + if (!dir.exists(outpath)) fs::dir_create(outpath) + outfile <- paste0(outpath, + "taxonid=", species, "_model=", outacro, "_method=", model_name, + "_what=respcurves.parquet") + + rcurves <- resp_curves(model_fits[[id]], + subset(env$layers, multi_mod_max$best_variables)) + + write_parquet(rcurves, outfile) + + outpath <- paste0(outfolder, "/taxonid=", species, "/model=", outacro, "/figures/") + if (!dir.exists(outpath)) fs::dir_create(outpath) + outfile <- paste0(outpath, + "taxonid=", species, "_model=", outacro, "_method=", model_name, + "_what=responsecurves.png") + + p <- plot(rcurves) + ggtitle(paste("AphiaID:", species, "| Model:", outacro, "| Algo:", model_name)) + ggplot2::ggsave(filename = outfile, plot = p, + width = 10, height = 8) + + return(invisible(NULL)) + + }) + + treg <- obissdm::.get_time(treg, "Response curves") + + + + # PART X: ENSEMBLE ---- + if (length(good_models) > 1) { + if (verbose) cli::cli_alert_info("Enough number of models, ensembling") + + to_ensemble <- list.files(paste0(outfolder, "/taxonid=", species, "/model=", outacro, "/predictions/"), + pattern = "\\.tif$", full.names = T) + to_ensemble <- to_ensemble[grepl(paste0(substr(algorithms, 1, 3)[good_models], collapse = "|"), to_ensemble)] + to_ensemble <- lapply(to_ensemble, rast) + lays <- nlyr(to_ensemble[[1]]) + + ensemble <- lapply(1:lays, function(l){ + pull_l <- lapply(to_ensemble, function(r){ + r[[l]] + }) + pull_l <- rast(pull_l) + ensemble_models("median", pull_l) + }) + + ensemble_cv <- rast(lapply(ensemble, function(x) x[[2]])) + ensemble <- rast(lapply(ensemble, function(x) x[[1]])) + + names(ensemble) <- names(ensemble_cv) <- names(to_ensemble[[1]]) + names(ensemble_cv) <- paste0(names(ensemble_cv), "_sd") + + outens <- paste0(outfolder, "/taxonid=", species, "/model=", outacro, "/predictions/", + "taxonid=", species, "_model=", outacro, "_method=ensemble.tif") + + writeRaster(c(ensemble, ensemble_cv), outens, + #wopt=list(verbose=TRUE), + overwrite = T) + + model_predictions <- c(model_predictions, ensemble[[1]]) + + res_ens <- cogeo_optim(outens) + + # Evaluate ensemble + ensemble_curr <- ensemble[[1]] + + fit_ens <- extract(ensemble_curr, sp_data$coord_training, ID = F) + + fit_ens_eval <- obissdm::eval_metrics(sp_data$training$presence, fit_ens[,1]) + + if (!is.null(sp_data$eval_data)) { + eval_ens <- extract(ensemble_curr, sp_data$coord_eval, ID = F) + + eval_ens_eval <- obissdm::eval_metrics(sp_data$eval_data$presence, eval_ens[,1]) + + ensemble_metrics <- rbind(cbind(as.data.frame(t(fit_ens_eval)), origin = "fit_data"), + cbind(as.data.frame(t(eval_ens_eval)), origin = "eval_data")) + } else { + ensemble_metrics <- cbind(as.data.frame(t(fit_ens_eval)), origin = "fit_data") + } + + # Get average of models + avg_fit_metrics <- as.data.frame(t(apply(do.call("rbind", lapply(good_models, function(id){ + t(apply(model_fits[[id]]$cv_metrics, 2, mean, na.rm = T)) + })), 2, mean, na.rm = T))) + avg_fit_metrics$origin <- "avg_fit" + + if (!is.null(sp_data$eval_data)) { + avg_eval_metrics <- as.data.frame(t(apply(do.call("rbind", lapply(good_models, function(id){ + t(model_fits[[id]]$eval_metrics) + })), 2, mean, na.rm = T))) + avg_eval_metrics$origin <- "avg_eval" + avg_fit_metrics <- rbind(avg_fit_metrics, avg_eval_metrics) + } + + ensemble_metrics <- rbind(ensemble_metrics, avg_fit_metrics) + + # TODO: add SD? + + write_parquet(ensemble_metrics, + paste0(outfolder, "/taxonid=", species, "/model=", outacro, "/metrics/", + "taxonid=", species, "_model=", outacro, + "_method=ensemble_what=metrics.parquet")) + } + + # Ensemble response curves + if (length(good_models) > 1) { + to_ensemble <- list.files(paste0(outfolder, "/taxonid=", species, "/model=", outacro, "/metrics/"), + pattern = "what=resp", full.names = T) + to_ensemble <- to_ensemble[grepl(paste0(substr(algorithms, 1, 3)[good_models], collapse = "|"), to_ensemble)] + to_ensemble <- lapply(to_ensemble, read_parquet) + + init_data <- to_ensemble[[1]] + + for (k in 2:length(to_ensemble)) { + tdf <- data.frame(to_ensemble[[2]][,c("response")]) + colnames(tdf) <- paste0("response_", k) + init_data <- cbind(init_data, tdf) + } + + ens_resp <- apply(init_data[,startsWith(colnames(init_data), "resp")], 1, "median") + + ens_resp_curves <- cbind(init_data[,c("variable", "base")], response = ens_resp) + + outpath <- paste0(outfolder, "/taxonid=", species, "/model=", outacro, "/metrics/") + outfile <- paste0(outpath, + "taxonid=", species, "_model=", outacro, "_method=ensemble", + "_what=respcurves.parquet") + + write_parquet(ens_resp_curves, outfile) + + } + + + + # PART X: SAVE BINARIZATION INFO ---- + if (verbose) cli::cli_alert_info("Saving binarization info") + # Get thresholds + # P10 threshold + thresh_p10_mtp <- lapply(model_predictions, function(pred) { + predv <- raster::extract(pred, sp_data$coord_training[sp_data$training$presence == 1,], ID = F)[,1] + p10 <- ceiling(length(predv) * 0.9) + data.frame(p10 = rev(sort(predv))[p10], mtp = min(na.omit(predv))) + }) + + # Max Specificity + Sensitivity + thresh_maxss <- lapply(model_predictions, function(pred) { + pred_p <- raster::extract(pred, sp_data$coord_training[sp_data$training$presence == 1,], ID = F)[,1] + pred_a <- raster::extract(pred, sp_data$coord_training[sp_data$training$presence == 0,], ID = F)[,1] + + pred_eval <- predicts::pa_evaluate(p = pred_p, a = pred_a) + + pred_thresh <- predicts::threshold(pred_eval) + + pred_thresh + }) + + if (length(model_predictions) > length(good_models)) { + names(thresh_p10_mtp) <- names(thresh_maxss) <- c(algorithms[good_models], "ensemble") + } else { + names(thresh_p10_mtp) <- names(thresh_maxss) <- algorithms[good_models] + } + + thresh_p10_mtp <- bind_rows(thresh_p10_mtp, .id = "model") + thresh_maxss <- bind_rows(thresh_maxss, .id = "model") + + thresh <- left_join(thresh_p10_mtp, thresh_maxss, by = "model") + + outpath <- paste0(outfolder, "/taxonid=", species, "/model=", outacro, "/metrics/") + write_parquet(thresh, paste0(outpath, + "taxonid=", species, "_model=", outacro, + "_what=thresholds.parquet")) + + + + # PART X: OPTIMIZE GEOTIFF (COG FORMAT) ---- + if (verbose) cli::cli_alert_info("Optimizing rasters (COG)") + + to_optimize <- list.files(paste0(outfolder, "/taxonid=", species, "/model=", outacro, "/predictions"), full.names = T) + to_optimize <- to_optimize[!grepl("aux", to_optimize)] + to_optimize <- to_optimize[!grepl("cog", to_optimize)] + + optimize_result <- lapply(to_optimize, cogeo_optim) + optimize_result <- do.call("rbind", optimize_result) + + + + # PART X: CREATE MASKS AND SAVE ---- + if (verbose) cli::cli_alert_info("Saving masks") + + # Get base raster + base_layer <- rast("data/env/terrain/bathymetry_mean.tif") + base_layer[!is.na(base_layer)] <- 1 + + # Get mask for study area species occurrence + ecoreg_occ_mask <- mask(base_layer, ecoreg_occ) + + # Get mask for study area ecoregions + ecoreg_mask <- mask(base_layer, ecoreg_sel) + + # Get area used for fitting + fit_mask <- terra::extend(env$layers[[1]], model_predictions[[1]]) + fit_mask[!is.na(fit_mask)] <- 1 + + masks <- c(ecoreg_occ_mask, ecoreg_mask, fit_mask) + names(masks) <- c("native_ecoregions", "fit_ecoregions", "fit_region") + + outmask <- paste0(outfolder, "/taxonid=", species, "/model=", outacro, "/predictions/", + "taxonid=", species, "_model=", outacro, "_mask.tif") + terra::writeRaster(masks, outmask, overwrite = T) + mask_opt <- cogeo_optim(outmask) + if (file.exists(paste0(outmask, ".aux.json"))) fs::file_delete(paste0(outmask, ".aux.json")) + + treg <- obissdm::.get_time(treg, "File optimization") + + + + # PART X: EVALUATE MODELS USING OTHER TECHNIQUES ---- + if (verbose) cli::cli_alert_info("Performing post-evaluation") + + # TEST 1: ENVIRONMENTAL SPACE + # Make PCA + # pca_env <- princomp(scale(subset(env$layers, multi_mod_max$best_variables)), + # maxcell = (ncell(env$layers[[1]]) * .5)) + # pca_env_pred <- predict(scale(subset(env$layers, multi_mod_max$best_variables)), pca_env, index = 1:2) + # + # # Extract at data points + # pca_fit <- terra::extract(pca_env_pred, sp_data$coord_training[sp_data$training$presence == 1,], ID = F) + # + # # Extract at SAMPLED data points + # pca_pred <- lapply(model_predictions, function(pred){ + # pred <- mask(pred, masks$fit_region) + # + # samp_pred <- spatSample(pred, size = sum(sp_data$training$presence), method = "weights", values = F, xy = T, na.rm = T) + # + # terra::extract(pca_env_pred, samp_pred, ID = F) + # }) + # + # plot(NULL, xlim = minmax(pca_env_pred)[,1], ylim = minmax(pca_env_pred)[,2]) + # + # points(pca_fit, pch = 20, cex = .5, col = "blue") + # points(pca_pred[[1]], pch = 20, cex = .5, col = "red") + # + # chul1 <- chull(pca_fit) + # chul1 <- c(chul1, chul1[1]) + # chul1 <- vect(as.matrix(pca_fit[chul1,]), type = "polygons") + # + # chul2 <- chull(pca_pred[[1]]) + # chul2 <- c(chul2, chul2[1]) + # chul2 <- vect(as.matrix(pca_pred[[1]][chul2,]), type = "polygons") + # + # lines(chul1, col = "blue") + # lines(chul2, col = "red") + + + # Add Hypervolume option, # TODO + + # TEST 2 - temperature kernel + # Load temperature information + temp_layer <- list.files("data/env/", recursive = T, + pattern = "thetao_baseline", + full.names = T) + temp_layer <- temp_layer[grepl(hab_depth, temp_layer)] + temp_layer <- temp_layer[grepl("mean\\.tif$", temp_layer)] + temp_layer <- rast(temp_layer) + + if (ext(temp_layer) != ext(model_predictions[[1]])) { + temp_layer <- crop(temp_layer, model_predictions[[1]]) + } + + data_fit <- extract(temp_layer, + dplyr::bind_rows(sp_data$coord_training, + sp_data$coord_eval), ID = F) + + kd <- ks::kde(data_fit[,1], h = 8) + percentiles <- ks::qkde(c(0.01, 0.99), kd) + + masked <- temp_layer + masked[temp_layer >= percentiles[1] & temp_layer <= percentiles[2]] <- 3 + masked[temp_layer < percentiles[1] | temp_layer > percentiles[2]] <- 0 + + binary_maps <- lapply(1:length(model_predictions), function(x){ + pred <- model_predictions[[x]] + pred[pred < thresh_p10_mtp[x,]$p10] <- 0 + pred[pred > 0] <- 1 + pred + }) + + plot(masked) + plot(binary_maps[[1]]) + + diff_maps <- lapply(binary_maps, function(x){ + x + masked + }) + + diff_perc <- lapply(diff_maps, function(x){ + x[x == 0] <- NA + x[x == 3] <- NA + result <- as.data.frame(x) + result <- as.data.frame(table(result[,1])) + colnames(result) <- c("status", "frequency") + result$status <- ifelse(result$status == 1, "outside_tenv", "inside_tenv") + result$percentage <- round((result$frequency * 100) / sum(result$frequency), 2) + result + }) + + trange_maps <- lapply(binary_maps, function(x){ + x[x != 1] <- NA + x_pts <- as.data.frame(x, xy = T)[,1:2] + quantile(extract(temp_layer, x_pts, ID = F)[,1], + c(0.01, 0.05, 0.5, 0.95, 0.99), na.rm = T) + }) + + for (gm in 1:length(good_models)) { + salg <- algorithms[good_models[gm]] + model_log$model_posteval[[salg]] <- list( + thermal_range = quantile(data_fit[,1], c(0.01, 0.05, 0.5, 0.95, 0.99)), + thermal_range_binary = trange_maps[[gm]], + thermal_envelope = diff_perc[[gm]] + ) + } + + # jacc <- function(raster1.bin, raster2.bin) { + # combination <- sum(raster1.bin, raster2.bin, na.rm= T) + # intersection <- combination == 2 + # + # # Union is all the area covered by the both rasters + # union <- combination >= 1 + # + # union <- as.data.frame(union) + # inter <- as.data.frame(intersection) + # + # length(inter[inter[,1],]) / length(union[union[,1],]) + # } + # + # overlap_metric <- jacc(masked, crop(binary_maps[[1]], masked)) + # overlap_metric_b <- dismo::nicheOverlap(raster::raster(masked), raster::raster(crop(binary_maps[[1]], masked))) + # + # + # Test 4: niche overlap ecospat + + + + # PART X: SAVE MODELS OBJECTS ---- + if (verbose) cli::cli_alert_info("Saving models objects") + + # Save metrics + save_metrics <- lapply(good_models, function(id){ + model_name <- model_fits[[id]]$name + + outpath <- paste0(outfolder, "/taxonid=", species, "/model=", outacro, "/metrics/") + outfile <- paste0(outpath, + "taxonid=", species, "_model=", outacro, "_method=", model_name) + + write_parquet(model_fits[[id]]$cv_metrics, paste0(outfile, "_what=cvmetrics.parquet")) + + other_metrics <- list(model_fits[[id]]$full_metrics, + NULL)#model_fits[[id]]$eval_metrics) + names(other_metrics) <- c("full", "eval") + other_metrics <- dplyr::bind_rows(other_metrics, .id = "what") + + write_parquet(other_metrics, paste0(outfile, "_what=fullmetrics.parquet")) + }) + + # Update log with best parameters + for (al in 1:length(algorithms)) { + if (!is.null(model_fits[[al]])) { + model_log$model_bestparams[[al]] <- model_fits[[al]]$parameters + } + } + + # Save models + save_models <- lapply(good_models, function(id) { + + model_name <- model_fits[[id]]$name + + outpath <- paste0(outfolder, "/taxonid=", species, "/model=", outacro, "/models/") + if (!dir.exists(outpath)) fs::dir_create(outpath) + outfile <- paste0(outpath, + "taxonid=", species, "_model=", outacro, "_method=", model_name, + "_what=model.rds") + + saveRDS(model_fits[[id]], file = outfile) + + return(invisible(NULL)) + + }) + + # Save fit points + write_parquet(sp_data$coord_training[sp_data$training$presence == 1,], + paste0(outfolder, "/taxonid=", species, "/model=", outacro, + "/taxonid=", species, "_model=", outacro, "_", + "what=fitocc.parquet")) + + + # Save log and return object + outpath <- paste0(outfolder, "/taxonid=", species, "/model=", outacro, "/") + outfile <- paste0(outpath, + "taxonid=", species, "_model=", outacro, "_what=log.json") + save_log(model_log, outfile) + + cli::cli_alert_success("Model concluded for species {species}.") + + st_status <- "succeeded" + } else { + if (verbose) cli::cli_alert_warning("No good model available") + st_status <- "no_good_model" + } + + } else { + if (verbose) cli::cli_alert_warning("All models failed") + st_status <- "all_failed" + } + + } else { + if (nrow(species_data) < 15) { + if (verbose) cli::cli_alert_warning("Low number of points, failed") + # If data is low, annotate storr + st_status <- "low_data" + } else { + if (verbose) cli::cli_alert_warning("No data available, failed") + # If data is not available, annotate storr + st_status <- "no_data" + } + } + + # Return nothing, results are saved + return(st_status) + +} diff --git a/functions/sdm_model_function.R b/functions/sdm_model_function.R new file mode 100644 index 0000000..7f09693 --- /dev/null +++ b/functions/sdm_model_function.R @@ -0,0 +1,970 @@ +############################## MPA Europe project ############################## +########### WP3 - Species and biogenic habitat distributions (UNESCO) ########## +# March of 2024 +# Authors: Silas C. Principe, Pieter Provoost +# Contact: s.principe@unesco.org +# +########################### Main modelling function ############################ + + +#' Model species distribution according to MPA Europe framework +#' +#' @param species AphiaID of the species +#' @param group group of the species, according to the configuration file +#' @param species_data dataframe with species +#' occurrences +#' @param outfolder the folder to output the results +#' @param outacro an unique acronym for the modelling +#' @param algorithms which algorithms to fit. See [obissdm::sdm_options()] for a +#' list of available algorithms +#' @param limit_by_depth should the study area be limited by depth? If so, the +#' depth is extracted from occurrence records and after applying the buffer +#' the layers are masked. Recommended to keep `TRUE` +#' @param depth_buffer the depth buffer to be added in case `limit_by_depth = +#' TRUE`. Can be 0 to apply no buffer (not recommended) +#' @param assess_bias if `TRUE`, perform tests for potential spatial bias on +#' occurrence records +#' @param correct_bias if `TRUE`, apply a bias layer to try to correct the +#' spatial bias +#' @param verbose if `TRUE` print messages +#' +#' @return nothing, saved files +#' @export +#' +#' @details +#' This function is a wrapper around functions from the [obissdm] package, to +#' provide a consistent pipeline for generating the SDMs according to the +#' MPA Europe project. +#' +#' Output will be in the format: +#' {outfolder}/taxonid={AphiaID}/model={outacro}/predictions OR models OR metrics/ +#' +#' - predictions: contains all raster files, optimized for cloud (COG) +#' - models: contain model object in zip files +#' - metrics: contain relevant metrics +#' +#' A single `.json` file is saved on the root of the model containing details +#' of the model fit. +#' +#' @examples +#' \dontrun{ +#' model_species(124316, "benthic_invertebrates", "sp_dataset.parquet", +#' "results", "mr1") +#' } +model_species <- function(species, + group, + species_data, + outfolder, + outacro, + algorithms = c("maxent", "rf", "brt", "lasso"), + limit_by_depth = TRUE, + depth_buffer = 500, + assess_bias = TRUE, + correct_bias = TRUE, + verbose = FALSE) { + + if (verbose) cli::cli_alert_info("Starting model for species {species}") + + # Record timing + treg <- obissdm::.get_time() + + # Define global objects and fill log + coord_names <- c("decimalLongitude", "decimalLatitude") + model_log <- obissdm::gen_log(algorithms) + model_log$taxonID <- species + model_log$model_acro <- outacro + model_log$model_date <- Sys.Date() + + treg <- obissdm::.get_time(treg, "Species data loading") + + # If data is available, proceeds + if (nrow(species_data) >= 15) { + + if (verbose) cli::cli_alert_info("Enough number of points, proceeding") + # PART 1: DATA LOADING ---- + # Load species information + species_name <- species_data$species[1] + model_log$scientificName <- species_name + + # Load ecological information + eco_info <- obissdm::mp_get_ecoinfo( + species_list = species, + outfile = NULL, + return_table = TRUE, + show_progress = FALSE + ) + + # Select ecological information for the species + hab <- eco_info$mode_life + hab_depth <- hab_to_depth(hab) + + # Load environmental data + env <- get_envofgroup(group, depth = hab_depth, load_all = T, + conf_file = "sdm_conf.yml", verbose = verbose) + + if ("coastal" %in% names(env$hypothesis)) { + # Test if is within europe/coastal + europe_starea <- vect("data/shapefiles/mpa_europe_starea_v2.shp") + europe_starea <- ext(europe_starea) + + is_within <- is.related(vect(species_data[,coord_names], geom = coord_names), + europe_starea, "intersects") + if (!all(is_within)) { + env$hypothesis <- env$hypothesis[names(env$hypothesis) != "coastal"] + env$layers <- subset(env$layers, "wavefetch", negate = T) + if (verbose) cli::cli_alert_warning("Coastal hypothesis found but discarded") + } else { + + wave <- rast("data/env/terrain/wavefetch.tif") + wave_pts <- extract(wave, species_data[,coord_names]) + + if (sum(is.na(wave_pts[,2])) > ceiling(nrow(species_data) * .05)) { + env$hypothesis <- env$hypothesis[names(env$hypothesis) != "coastal"] + if (verbose) cli::cli_alert_warning("Coastal hypothesis found but discarded") + } else if ((nrow(species_data) - sum(is.na(wave_pts[,2]))) < 15) { + env$hypothesis <- env$hypothesis[names(env$hypothesis) != "coastal"] + if (verbose) cli::cli_alert_warning("Coastal hypothesis found but discarded") + } else { + env$layers <- crop(env$layers, europe_starea) + } + } + } + + + # Load ecoregions shapefile + ecoregions <- vect("data/shapefiles/MarineRealms.shp") + + # Split the dataset + fit_pts <- split_ds(species_data) + eval_pts <- split_ds(species_data, "eval") + + treg <- obissdm::.get_time(treg, "Data loading") + + + + # PART 2: DATA PREPARING ---- + if (verbose) cli::cli_alert_info("Preparing data") + # Check which eco-regions are covered by the points + ecoreg_unique <- ecoregions$Realm[is.related(ecoregions, + vect(bind_rows(fit_pts, eval_pts), + geom = coord_names), + "intersects")] + + model_log$model_details$ecoregions <- ecoreg_unique + ecoreg_occ <- ecoregions[ecoregions$Realm %in% ecoreg_unique,] + + # Apply a buffer to ensure that all areas are covered + sf::sf_use_s2(FALSE) + ecoreg_occ_buff <- suppressMessages( + suppressWarnings(vect(sf::st_buffer(sf::st_as_sf(ecoreg_occ), 0.2))) + ) + + adj_ecoreg <- ecoregions$Realm[is.related(ecoregions, ecoreg_occ_buff, + "intersects")] + + # Mask areas + ecoreg_sel <- ecoregions[ecoregions$Realm %in% unique( + c(ecoreg_unique, adj_ecoreg) + ),] + model_log$model_details$ecoregions_included <- unique(ecoreg_sel$Realm) + + # Apply a buffer to ensure that all areas are covered + sf::sf_use_s2(FALSE) + ecoreg_sel <- suppressMessages( + suppressWarnings(vect(sf::st_buffer(sf::st_as_sf(ecoreg_sel), 0.5))) + ) + + # Limit to max extension based on distance to the points + max_ext <- ext(vect(rbind(fit_pts, eval_pts), geom = coord_names)) + max_ext <- ext(as.vector(max_ext) + c(-20, 20, -20, 20)) + ecoreg_sel <- crop(ecoreg_sel, max_ext) + + # Limit by depth if TRUE + if (limit_by_depth) { + if (verbose) cli::cli_alert_info("Limiting by depth") + # To decide if this step will be kept: + # Load bathymetry layer + bath <- rast("data/env/terrain/bathymetry_mean.tif") + bath <- mask(crop(bath, ecoreg_sel), ecoreg_sel) + + bath_pts <- terra::extract(bath, bind_rows(fit_pts, eval_pts)) + + bath_range <- range(bath_pts[,2]) + bath_range[1] <- bath_range[1] - depth_buffer + bath_range[2] <- ifelse((bath_range[2] + depth_buffer) > 0, + 0, bath_range[2] + depth_buffer) + + bath[bath < bath_range[1] | bath > bath_range[2]] <- NA + + if ("coastal" %in% names(env$hypothesis)) { + bath <- crop(bath, europe_starea) + env$layers <- mask(crop(env$layers, ecoreg_sel), bath) + env$layers <- mask(env$layers, env$layers$wavefetch) + } else { + env$layers <- mask(crop(env$layers, ecoreg_sel), bath) + } + + model_log$model_details$limited_by_depth <- TRUE + model_log$model_details$depth_buffer <- depth_buffer + + } else { + if ("coastal" %in% names(env$hypothesis)) { + ecoreg_sel <- crop(ecoreg_sel, europe_starea) + env$layers <- mask(env$layers, ecoreg_sel) + } else { + env$layers <- mask(env$layers, ecoreg_sel) + } + } + + # Assess SAC + fit_pts_sac <- try(obissdm::outqc_sac(fit_pts, + env_layers = subset(env$layers, env$hypothesis[[1]]), + plot_result = FALSE, + verbose = verbose)) + + if (inherits(fit_pts_sac, "try-error")) { + fit_pts_sac <- fit_pts + } + + # Make data object + quad_n <- ifelse(nrow(as.data.frame(env$layers, xy = T, na.rm = T)) < 50000, + round(nrow(as.data.frame(env$layers, xy = T, na.rm = T))), 50000) + + sp_data <- mp_prepare_data(fit_pts_sac, eval_data = eval_pts, + species_id = species_name, + env_layers = env$layers, + quad_number = quad_n, + verbose = verbose) + + block_grid <- get_block_grid(sp_data, env$layers, + sel_vars = env$hypothesis$basevars, + verbose = verbose) + + sp_data <- mp_prepare_blocks(sp_data, + method = "manual", + block_types = "spatial_grid", + n_iterate = 300, + retry_if_zero = TRUE, + manual_shp = block_grid, + verbose = verbose) + + model_log$model_details$block_size <- unname(sp_data$blocks$grid_resolution) + model_log$model_fit_points <- sum(sp_data$training$presence) + model_log$model_eval_points <- sum(sp_data$eval_data$presence) + + treg <- obissdm::.get_time(treg, "Data preparing") + + # PART 3: ASSESS SPATIAL BIAS ---- + if (assess_bias) { + if (verbose) cli::cli_alert_info("Assessing spatial bias") + # Add some way of assessing spatial bias + model_log$model_details$control_bias <- correct_bias + + # Get spatstat statistics and point process for control + spat_im <- as.im(as.data.frame(aggregate(env$layers[[1]], 10, na.rm = T), xy = T)) + spat_window <- as.owin(spat_im) + # Define ppp object based on point locations + spat_ppp <- ppp(x = sp_data$coord_training$decimalLongitude[sp_data$training$presence == 1], + y = sp_data$coord_training$decimalLatitude[sp_data$training$presence == 1], + window = spat_window) + # calculate envelope around L-hat estimates. + spat_env_k <- envelope(spat_ppp, Kest, verbose = F) + spat_env_l <- envelope(spat_ppp, Lest, verbose = F) + + # Point process model (?) + spat_quad <- ppp(x = sp_data$coord_training$decimalLongitude[sp_data$training$presence == 0], + y = sp_data$coord_training$decimalLatitude[sp_data$training$presence == 0], + window = spat_window) + spat_quad_sc <- quadscheme(data = spat_ppp, dummy = spat_quad, method = "dirichlet") + spat_data_obj <- lapply(1:nlyr(env$layers), function(x){ + as.im(as.data.frame(aggregate(env$layers[[x]], 4, na.rm = T), xy = T)) + }) + names(spat_data_obj) <- names(env$layers) + spat_ppm <- ppm(spat_quad_sc, + trend = as.formula(paste("~", + paste(names(spat_data_obj)[ + names(spat_data_obj) %in% env$hypothesis[[1]] + ], collapse = "+"))), + covariates = spat_data_obj) + + spat_pred <- predict(spat_ppm, covariates = spat_data_obj, ngrid = c(spat_data_obj[[1]]$dim[1], spat_data_obj[[1]]$dim[2])) + + if (correct_bias) { + bias_layer <- density(spat_ppp, sigma = 1) + bias_layer <- rast(bias_layer) + bias_layer <- disagg(bias_layer, 10) + crs(bias_layer) <- crs("EPSG:4326") + bias_layer <- project(bias_layer, env$layers[[1]]) + bias_layer <- mask(bias_layer, env$layers[[1]]) + } + + treg <- obissdm::.get_time(treg, "Sample bias assessment") + } + + + # PART 4: MODEL SELECTION ---- + if (verbose) cli::cli_alert_info("Model selection") + multi_mod_max <- sdm_multhypo(sdm_data = sp_data, sdm_method = "maxent", + variables = env$hypothesis, + verbose = verbose) + + if (correct_bias) { + if (verbose) cli::cli_alert_info("Testing bias correction") + temp_data <- sp_data + temp_data$training <- temp_data$training[, c("presence", multi_mod_max$best_variables)] + temp_data$eval_data <- temp_data$eval_data[, c("presence", multi_mod_max$best_variables)] + + temp_data$training$biasgrid <- extract(bias_layer, temp_data$coord_training, ID = F)[,1] + temp_data$eval_data$biasgrid <- extract(bias_layer, temp_data$coord_eval, ID = F)[,1] + + not_na <- !is.na(temp_data$training$biasgrid) + not_na_eval <- !is.na(temp_data$eval_data$biasgrid) + + temp_data$training <- temp_data$training[not_na,] + temp_data$coord_training <- temp_data$coord_training[not_na,] + + temp_data$eval_data <- temp_data$eval_data[not_na_eval,] + temp_data$coord_eval <- temp_data$coord_eval[not_na_eval,] + + max_bias <- sdm_fit(temp_data) + + bias_metrics <- apply(max_bias$cv_metrics, 2, mean, na.rm = T) + model_metrics <- apply(multi_mod_max$model$cv_metrics, 2, mean, na.rm = T) + + if (bias_metrics[["cbi"]] > (model_metrics[["cbi"]] + 0.1)) { + # TODO: continue here + # TODO: bias layer needs to be other, not from the single species + # group or something - to see + } + } + + # Prepare data for the best model + sp_data$training <- sp_data$training[, c("presence", multi_mod_max$best_variables)] + sp_data$eval_data <- sp_data$eval_data[, c("presence", multi_mod_max$best_variables)] + + treg <- obissdm::.get_time(treg, "Model selection") + + + # PART 4: FIT MODELS ---- + if (verbose) cli::cli_alert_info("Starting model fitting") + model_fits <- list() + for (i in 1:length(algorithms)) { + if (verbose) cli::cli_alert_info("Fitting {algorithms[i]}") + model_fits[[i]] <- try(do.call(paste0("sdm_module_", algorithms[i]), list(sp_data, verbose = verbose))) + if (inherits(model_fits[[i]], "try-error")) { + model_log$model_result[[algorithms[i]]] <- "failed" + model_fits[[i]] <- NULL + } else { + model_log$model_result[[algorithms[i]]] <- "succeeded" + } + } + + treg <- obissdm::.get_time(treg, "Model fit") + + + if (any(unlist(model_log$model_result) == "succeeded")) { + if (verbose) cli::cli_alert_info("Model fitting concluded. Checking if models are good") + # PART 5: PREDICTION ---- + + # Define thresholds + tg_metrics <- "cbi" + tg_threshold <- 0.3 + + # Get what models are above threshold + good_models <- lapply(model_fits, function(model){ + if (!is.null(model)) { + cv_res <- model$cv_metrics + cv_res <- apply(cv_res, 2, mean, na.rm = T) + the_metric <- cv_res[[tg_metrics]] + if (the_metric >= tg_threshold) { + if (sum(is.na(model$cv_metrics[[tg_metrics]])) >= 4) { + return(FALSE) + } else { + return(TRUE) + } + } else { + return(FALSE) + } + } else { + return(FALSE) + } + }) + + good_models <- which(unlist(good_models)) + + if (length(good_models) > 0) { + if (verbose) cli::cli_alert_info("Good models available, predicting.") + # Predict models + model_predictions <- lapply(good_models, function(id) { + model_name <- model_fits[[id]]$name + + best_hyp <- multi_mod_max$best_model + + outpath <- paste0(outfolder, "/taxonid=", species, "/model=", outacro, "/predictions/") + + if (!dir.exists(outpath)) fs::dir_create(outpath) + + outmess <- paste0(outpath, "taxonid=", species, "_model=", outacro, "_what=mess.tif") + + if (!file.exists(gsub("mess", "mess_cog", outmess))) {do_mess <- TRUE} else {do_mess <- FALSE} + + scenarios <- data.frame( + scenario = c("current", rep(c("ssp116", "ssp245", "ssp370", "ssp460", "ssp585"), + each = 2)), + year = c(NA, rep(c("dec50", "dec100"), 5)) + ) + + for (k in 1:nrow(scenarios)) { + + if (is.na(scenarios$year[k])) { + period <- NULL + } else { + period <- scenarios$year[k] + } + + if (verbose) cli::cli_alert_info("Predicting scenario {k} of {nrow(scenarios)}.") + + env_to_pred <- obissdm::get_envofgroup(group, + depth = hab_depth, load_all = F, + scenario = scenarios$scenario[k], + period = period, + hypothesis = best_hyp, + env_folder = "data/env", + conf_file = "sdm_conf.yml", + verbose = verbose) + + pred <- predict(model_fits[[id]], env_to_pred) + + names(pred) <- paste0(scenarios$scenario[k], ifelse(is.null(period), "", paste0("_", period))) + + if (do_mess) { + # Save MESS + if (!exists("to_mess")) { + maxpt <- nrow(as.data.frame(env_to_pred, xy = T, na.rm = T)) + to_mess <- sample(1:maxpt, + ifelse(maxpt > 10000, 10000, maxpt)) + } + mess_map <- ecospat::ecospat.mess( + na.omit(as.data.frame(env_to_pred, xy = T)[to_mess,]), + cbind(sp_data$coord_training, sp_data$training[,2:ncol(sp_data$training)])) + mess_map_t <- env_to_pred[[1]] + mess_map_t[] <- NA + mess_map_t[cellFromXY(mess_map_t, mess_map[,1:2])] <- mess_map[,5] + mess_map <- mask(mess_map_t, env_to_pred[[1]]) + + names(mess_map) <- names(pred) <- paste0(scenarios$scenario[k], ifelse(is.null(period), "", paste0("_", period))) + + if (k == 1) { + pred_mess <- mess_map + } else { + pred_mess <- c(pred_mess, mess_map) + } + } + + + if (k == 1) { + pred_f <- pred + } else { + pred_f <- c(pred_f, pred) + } + } + + writeRaster(pred_f, paste0(outpath, "taxonid=", species, "_model=", outacro, "_method=", model_name,".tif"), + overwrite = T) + + if (do_mess) { + writeRaster(pred_mess, outmess, overwrite = T) + cogeo_optim(outmess) + } + + return(pred_f[["current"]]) + + }) + + treg <- obissdm::.get_time(treg, "Model prediction") + + + + # PART X: GET RESPONSE CURVES ---- + if (verbose) cli::cli_alert_info("Getting response curves") + # Predict models + model_respcurves <- lapply(good_models, function(id) { + + model_name <- model_fits[[id]]$name + + outpath <- paste0(outfolder, "/taxonid=", species, "/model=", outacro, "/metrics/") + if (!dir.exists(outpath)) fs::dir_create(outpath) + outfile <- paste0(outpath, + "taxonid=", species, "_model=", outacro, "_method=", model_name, + "_what=respcurves.parquet") + + rcurves <- resp_curves(model_fits[[id]], + subset(env$layers, multi_mod_max$best_variables)) + + write_parquet(rcurves, outfile) + + outpath <- paste0(outfolder, "/taxonid=", species, "/model=", outacro, "/figures/") + if (!dir.exists(outpath)) fs::dir_create(outpath) + outfile <- paste0(outpath, + "taxonid=", species, "_model=", outacro, "_method=", model_name, + "_what=responsecurves.png") + + p <- plot(rcurves) + ggtitle(paste("AphiaID:", species, "| Model:", outacro, "| Algo:", model_name)) + ggplot2::ggsave(filename = outfile, plot = p, + width = 10, height = 8) + + return(invisible(NULL)) + + }) + + treg <- obissdm::.get_time(treg, "Response curves") + + + + # PART X: ENSEMBLE ---- + if (length(good_models) > 1) { + if (verbose) cli::cli_alert_info("Enough number of models, ensembling") + + to_ensemble <- list.files(paste0(outfolder, "/taxonid=", species, "/model=", outacro, "/predictions/"), + pattern = "\\.tif$", full.names = T) + to_ensemble <- to_ensemble[grepl(paste0(substr(algorithms, 1, 3)[good_models], collapse = "|"), to_ensemble)] + to_ensemble <- lapply(to_ensemble, rast) + lays <- nlyr(to_ensemble[[1]]) + + ensemble <- lapply(1:lays, function(l){ + pull_l <- lapply(to_ensemble, function(r){ + r[[l]] + }) + pull_l <- rast(pull_l) + ensemble_models("median", pull_l) + }) + + ensemble_cv <- rast(lapply(ensemble, function(x) x[[2]])) + ensemble <- rast(lapply(ensemble, function(x) x[[1]])) + + names(ensemble) <- names(ensemble_cv) <- names(to_ensemble[[1]]) + names(ensemble_cv) <- paste0(names(ensemble_cv), "_sd") + + outens <- paste0(outfolder, "/taxonid=", species, "/model=", outacro, "/predictions/", + "taxonid=", species, "_model=", outacro, "_method=ensemble.tif") + + writeRaster(c(ensemble, ensemble_cv), outens, + #wopt=list(verbose=TRUE), + overwrite = T) + + model_predictions <- c(model_predictions, ensemble[[1]]) + + res_ens <- cogeo_optim(outens) + + # Evaluate ensemble + ensemble_curr <- ensemble[[1]] + + fit_ens <- extract(ensemble_curr, sp_data$coord_training, ID = F) + + fit_ens_eval <- obissdm::eval_metrics(sp_data$training$presence, fit_ens[,1]) + + if (!is.null(sp_data$eval_data)) { + eval_ens <- extract(ensemble_curr, sp_data$coord_eval, ID = F) + + eval_ens_eval <- obissdm::eval_metrics(sp_data$eval_data$presence, eval_ens[,1]) + + ensemble_metrics <- rbind(cbind(as.data.frame(t(fit_ens_eval)), origin = "fit_data"), + cbind(as.data.frame(t(eval_ens_eval)), origin = "eval_data")) + } else { + ensemble_metrics <- cbind(as.data.frame(t(fit_ens_eval)), origin = "fit_data") + } + + # Get average of models + avg_fit_metrics <- as.data.frame(t(apply(do.call("rbind", lapply(good_models, function(id){ + t(apply(model_fits[[id]]$cv_metrics, 2, mean, na.rm = T)) + })), 2, mean, na.rm = T))) + avg_fit_metrics$origin <- "avg_fit" + + if (!is.null(sp_data$eval_data)) { + avg_eval_metrics <- as.data.frame(t(apply(do.call("rbind", lapply(good_models, function(id){ + t(model_fits[[id]]$eval_metrics) + })), 2, mean, na.rm = T))) + avg_eval_metrics$origin <- "avg_eval" + avg_fit_metrics <- rbind(avg_fit_metrics, avg_eval_metrics) + } + + ensemble_metrics <- rbind(ensemble_metrics, avg_fit_metrics) + + # TODO: add SD? + + write_parquet(ensemble_metrics, + paste0(outfolder, "/taxonid=", species, "/model=", outacro, "/metrics/", + "taxonid=", species, "_model=", outacro, + "_method=ensemble_what=metrics.parquet")) + } + + # Ensemble response curves + if (length(good_models) > 1) { + to_ensemble <- list.files(paste0(outfolder, "/taxonid=", species, "/model=", outacro, "/metrics/"), + pattern = "what=resp", full.names = T) + to_ensemble <- to_ensemble[grepl(paste0(substr(algorithms, 1, 3)[good_models], collapse = "|"), to_ensemble)] + to_ensemble <- lapply(to_ensemble, read_parquet) + + init_data <- to_ensemble[[1]] + + for (k in 2:length(to_ensemble)) { + tdf <- data.frame(to_ensemble[[2]][,c("response")]) + colnames(tdf) <- paste0("response_", k) + init_data <- cbind(init_data, tdf) + } + + ens_resp <- apply(init_data[,startsWith(colnames(init_data), "resp")], 1, "median") + + ens_resp_curves <- cbind(init_data[,c("variable", "base")], response = ens_resp) + + outpath <- paste0(outfolder, "/taxonid=", species, "/model=", outacro, "/metrics/") + outfile <- paste0(outpath, + "taxonid=", species, "_model=", outacro, "_method=ensemble", + "_what=respcurves.parquet") + + write_parquet(ens_resp_curves, outfile) + + } + + + + # PART X: SAVE BINARIZATION INFO ---- + if (verbose) cli::cli_alert_info("Saving binarization info") + # Get thresholds + # P10 threshold + thresh_p10_mtp <- lapply(model_predictions, function(pred) { + predv <- raster::extract(pred, sp_data$coord_training[sp_data$training$presence == 1,], ID = F)[,1] + p10 <- ceiling(length(predv) * 0.9) + data.frame(p10 = rev(sort(predv))[p10], mtp = min(na.omit(predv))) + }) + + # Max Specificity + Sensitivity + thresh_maxss <- lapply(model_predictions, function(pred) { + pred_p <- raster::extract(pred, sp_data$coord_training[sp_data$training$presence == 1,], ID = F)[,1] + pred_a <- raster::extract(pred, sp_data$coord_training[sp_data$training$presence == 0,], ID = F)[,1] + + pred_eval <- predicts::pa_evaluate(p = pred_p, a = pred_a) + + pred_thresh <- predicts::threshold(pred_eval) + + pred_thresh + }) + + if (length(model_predictions) > length(good_models)) { + names(thresh_p10_mtp) <- names(thresh_maxss) <- c(algorithms[good_models], "ensemble") + } else { + names(thresh_p10_mtp) <- names(thresh_maxss) <- algorithms[good_models] + } + + thresh_p10_mtp <- bind_rows(thresh_p10_mtp, .id = "model") + thresh_maxss <- bind_rows(thresh_maxss, .id = "model") + + thresh <- left_join(thresh_p10_mtp, thresh_maxss, by = "model") + + outpath <- paste0(outfolder, "/taxonid=", species, "/model=", outacro, "/metrics/") + write_parquet(thresh, paste0(outpath, + "taxonid=", species, "_model=", outacro, + "_what=thresholds.parquet")) + + + + # PART X: OPTIMIZE GEOTIFF (COG FORMAT) ---- + if (verbose) cli::cli_alert_info("Optimizing rasters (COG)") + + to_optimize <- list.files(paste0(outfolder, "/taxonid=", species, "/model=", outacro, "/predictions"), full.names = T) + to_optimize <- to_optimize[!grepl("aux", to_optimize)] + to_optimize <- to_optimize[!grepl("cog", to_optimize)] + + optimize_result <- lapply(to_optimize, cogeo_optim) + optimize_result <- do.call("rbind", optimize_result) + + + + # PART X: CREATE MASKS AND SAVE ---- + if (verbose) cli::cli_alert_info("Saving masks") + + # Get base raster + base_layer <- rast("data/env/terrain/bathymetry_mean.tif") + base_layer[!is.na(base_layer)] <- 1 + + # Get mask for study area species occurrence + ecoreg_occ_mask <- mask(base_layer, ecoreg_occ) + + # Get mask for study area ecoregions + ecoreg_mask <- mask(base_layer, ecoreg_sel) + + # Get area used for fitting + fit_mask <- terra::extend(env$layers[[1]], model_predictions[[1]]) + fit_mask[!is.na(fit_mask)] <- 1 + + masks <- c(ecoreg_occ_mask, ecoreg_mask, fit_mask) + names(masks) <- c("native_ecoregions", "fit_ecoregions", "fit_region") + + outmask <- paste0(outfolder, "/taxonid=", species, "/model=", outacro, "/predictions/", + "taxonid=", species, "_model=", outacro, "_mask.tif") + terra::writeRaster(masks, outmask, overwrite = T) + mask_opt <- cogeo_optim(outmask) + if (file.exists(paste0(outmask, ".aux.json"))) fs::file_delete(paste0(outmask, ".aux.json")) + + treg <- obissdm::.get_time(treg, "File optimization") + + + + # PART X: EVALUATE MODELS USING OTHER TECHNIQUES ---- + if (verbose) cli::cli_alert_info("Performing post-evaluation") + + # TEST 1: ENVIRONMENTAL SPACE + # Make PCA + # pca_env <- princomp(scale(subset(env$layers, multi_mod_max$best_variables)), + # maxcell = (ncell(env$layers[[1]]) * .5)) + # pca_env_pred <- predict(scale(subset(env$layers, multi_mod_max$best_variables)), pca_env, index = 1:2) + # + # # Extract at data points + # pca_fit <- terra::extract(pca_env_pred, sp_data$coord_training[sp_data$training$presence == 1,], ID = F) + # + # # Extract at SAMPLED data points + # pca_pred <- lapply(model_predictions, function(pred){ + # pred <- mask(pred, masks$fit_region) + # + # samp_pred <- spatSample(pred, size = sum(sp_data$training$presence), method = "weights", values = F, xy = T, na.rm = T) + # + # terra::extract(pca_env_pred, samp_pred, ID = F) + # }) + # + # plot(NULL, xlim = minmax(pca_env_pred)[,1], ylim = minmax(pca_env_pred)[,2]) + # + # points(pca_fit, pch = 20, cex = .5, col = "blue") + # points(pca_pred[[1]], pch = 20, cex = .5, col = "red") + # + # chul1 <- chull(pca_fit) + # chul1 <- c(chul1, chul1[1]) + # chul1 <- vect(as.matrix(pca_fit[chul1,]), type = "polygons") + # + # chul2 <- chull(pca_pred[[1]]) + # chul2 <- c(chul2, chul2[1]) + # chul2 <- vect(as.matrix(pca_pred[[1]][chul2,]), type = "polygons") + # + # lines(chul1, col = "blue") + # lines(chul2, col = "red") + + + # Add Hypervolume option, # TODO + + # TEST 2 - temperature kernel + # Load temperature information + temp_layer <- list.files("data/env/", recursive = T, + pattern = "thetao_baseline", + full.names = T) + temp_layer <- temp_layer[grepl(hab_depth, temp_layer)] + temp_layer <- temp_layer[grepl("mean\\.tif$", temp_layer)] + temp_layer <- rast(temp_layer) + + if (ext(temp_layer) != ext(model_predictions[[1]])) { + temp_layer <- crop(temp_layer, model_predictions[[1]]) + } + + data_fit <- extract(temp_layer, + dplyr::bind_rows(sp_data$coord_training, + sp_data$coord_eval), ID = F) + + kd <- ks::kde(data_fit[,1], h = 8) + percentiles <- ks::qkde(c(0.01, 0.99), kd) + + masked <- temp_layer + masked[temp_layer >= percentiles[1] & temp_layer <= percentiles[2]] <- 3 + masked[temp_layer < percentiles[1] | temp_layer > percentiles[2]] <- 0 + + binary_maps <- lapply(1:length(model_predictions), function(x){ + pred <- model_predictions[[x]] + pred[pred < thresh_p10_mtp[x,]$p10] <- 0 + pred[pred > 0] <- 1 + pred + }) + + plot(masked) + plot(binary_maps[[1]]) + + diff_maps <- lapply(binary_maps, function(x){ + x + masked + }) + + diff_perc <- lapply(diff_maps, function(x){ + x[x == 0] <- NA + x[x == 3] <- NA + result <- as.data.frame(x) + result <- as.data.frame(table(result[,1])) + colnames(result) <- c("status", "frequency") + result$status <- ifelse(result$status == 1, "outside_tenv", "inside_tenv") + result$percentage <- round((result$frequency * 100) / sum(result$frequency), 2) + result + }) + + trange_maps <- lapply(binary_maps, function(x){ + x[x != 1] <- NA + x_pts <- as.data.frame(x, xy = T)[,1:2] + quantile(extract(temp_layer, x_pts, ID = F)[,1], + c(0.01, 0.05, 0.5, 0.95, 0.99), na.rm = T) + }) + + for (gm in 1:length(good_models)) { + salg <- algorithms[good_models[gm]] + model_log$model_posteval[[salg]] <- list( + thermal_range = quantile(data_fit[,1], c(0.01, 0.05, 0.5, 0.95, 0.99)), + thermal_range_binary = trange_maps[[gm]], + thermal_envelope = diff_perc[[gm]] + ) + } + + # jacc <- function(raster1.bin, raster2.bin) { + # combination <- sum(raster1.bin, raster2.bin, na.rm= T) + # intersection <- combination == 2 + # + # # Union is all the area covered by the both rasters + # union <- combination >= 1 + # + # union <- as.data.frame(union) + # inter <- as.data.frame(intersection) + # + # length(inter[inter[,1],]) / length(union[union[,1],]) + # } + # + # overlap_metric <- jacc(masked, crop(binary_maps[[1]], masked)) + # overlap_metric_b <- dismo::nicheOverlap(raster::raster(masked), raster::raster(crop(binary_maps[[1]], masked))) + # + # + # Test 4: niche overlap ecospat + + + + # PART X: SAVE MODELS OBJECTS ---- + if (verbose) cli::cli_alert_info("Saving models objects") + + # Save metrics + save_metrics <- lapply(good_models, function(id){ + model_name <- model_fits[[id]]$name + + outpath <- paste0(outfolder, "/taxonid=", species, "/model=", outacro, "/metrics/") + outfile <- paste0(outpath, + "taxonid=", species, "_model=", outacro, "_method=", model_name) + + write_parquet(model_fits[[id]]$cv_metrics, paste0(outfile, "_what=cvmetrics.parquet")) + + other_metrics <- list(model_fits[[id]]$full_metrics, + NULL)#model_fits[[id]]$eval_metrics) + names(other_metrics) <- c("full", "eval") + other_metrics <- dplyr::bind_rows(other_metrics, .id = "what") + + write_parquet(other_metrics, paste0(outfile, "_what=fullmetrics.parquet")) + }) + + # Update log with best parameters + for (al in 1:length(algorithms)) { + if (!is.null(model_fits[[al]])) { + model_log$model_bestparams[[al]] <- model_fits[[al]]$parameters + } + } + + # Save models + save_models <- lapply(good_models, function(id) { + + model_name <- model_fits[[id]]$name + + outpath <- paste0(outfolder, "/taxonid=", species, "/model=", outacro, "/models/") + if (!dir.exists(outpath)) fs::dir_create(outpath) + outfile <- paste0(outpath, + "taxonid=", species, "_model=", outacro, "_method=", model_name, + "_what=model.rds") + + saveRDS(model_fits[[id]], file = outfile) + + return(invisible(NULL)) + + }) + + # Save fit points + write_parquet(sp_data$coord_training[sp_data$training$presence == 1,], + paste0(outfolder, "/taxonid=", species, "/model=", outacro, + "/taxonid=", species, "_model=", outacro, "_", + "what=fitocc.parquet")) + + + # Save log and return object + outpath <- paste0(outfolder, "/taxonid=", species, "/model=", outacro, "/") + outfile <- paste0(outpath, + "taxonid=", species, "_model=", outacro, "_what=log.json") + save_log(model_log, outfile) + + cli::cli_alert_success("Model concluded for species {species}.") + + st_status <- "succeeded" + } else { + if (verbose) cli::cli_alert_warning("No good model available") + st_status <- "no_good_model" + } + + } else { + if (verbose) cli::cli_alert_warning("All models failed") + st_status <- "all_failed" + } + + } else { + if (nrow(species_data) < 15) { + if (verbose) cli::cli_alert_warning("Low number of points, failed") + # If data is low, annotate storr + st_status <- "low_data" + } else { + if (verbose) cli::cli_alert_warning("No data available, failed") + # If data is not available, annotate storr + st_status <- "no_data" + } + } + + # Return nothing, results are saved + return(st_status) + +} + + + +# Auxiliary functions ---- +# Convert eco info to habitat depth +hab_to_depth <- function(hab, default = "depthsurf") { + + if (grepl("benthic|demersal|bottom", hab)) { + dstrata <- "depthmax" + } else if (grepl("pelagic_surface", hab)) { + dstrata <- "depthsurf" + } else if (grepl("NOT_FOUND", hab)) { + dstrata <- default + } else { + dstrata <- "depthmean" + } + + return(dstrata) +} + +# Split dataset into eval and fit +split_ds <- function(sp_occ, + what = "fit", + only_coords = TRUE) { + + if (grepl("fit", what)) { + to_get <- "fit_points" + } else if (grepl("eval", what)) { + to_get <- "eval_points" + } else { + stop("What should be one of `fit_points` or `eval_points`") + } + + pts <- sp_occ %>% + filter(data_type == to_get) %>% + as.data.frame() + + if (nrow(pts) == 0) { + pts <- NULL + } else if (only_coords) { + pts <- pts[,c("decimalLongitude", "decimalLatitude")] + } + + return(pts) +} \ No newline at end of file diff --git a/plots_experimental.R b/plots_experimental.R new file mode 100644 index 0000000..a5ec533 --- /dev/null +++ b/plots_experimental.R @@ -0,0 +1,169 @@ +#################### eDNA expeditions - scientific analysis #################### +########################## Environmental data download ######################### +# January of 2024 +# Author: Silas Principe, Mike Burrows +# Contact: s.principe@unesco.org, michael.burrows@sams.ac.uk +# +########## Read and analyse eDNA data from UNESCO World Heritage Sites ######### + +# Load packages ---- +library(dplyr) +library(stringr) +library(purrr) +library(arrow) +library(data.table) +library(ggplot2) +fs::dir_create("figures") +set.seed(2023) + +# Load files and convert to data.table ---- +# Species summaries +speciestherm <- read_parquet("results/species_tsummaries.parquet") +speciesthermdt <- data.table(speciestherm) + +# Site summaries +speciesthermsite <- read_parquet("results/tsummaries_aggregated.parquet") +speciesthermsitedt <- data.table(speciesthermsite) + +# Species list +specieslist <- read_parquet("results/species_list.parquet") + + +# Select relevant depth/variant ---- +speciesthermsst <- speciesthermdt[depth == "depthsurf" & variant == "mean", , ] +speciesthermsstc <- dcast(speciesthermdt, + species ~ metric, + value.var = "value", + fun = mean) + +# Get community thermal index +ctihighergeog <- speciesthermsitedt[, list( + ctiavg = mean(q_0.5), + sdcti = sd(q_0.5), + str = mean(q_0.9 - q_0.1), + sstavg = mean(site_current), + nspp = .N +), by = c("higherGeography", "where")] + +for (i in 1:100) { + + resampled <- speciesthermsitedt[, list( + q_0.5 = sample(q_0.5, .N, replace = T), + q_0.9 = sample(q_0.9, .N, replace = T), + q_0.1 = sample(q_0.1, .N, replace = T), + site_current = sample(site_current, .N, replace = T) + #resample = 1:.N#sample(1:.N, .N, replace = T) + ), by = c("higherGeography", "where")] + + ctihighergeog_temp <- resampled[, list( + ctiavg = mean(q_0.5), + sdcti = sd(q_0.5), + str = mean(q_0.9 - q_0.1), + sstavg = mean(site_current), + nspp = .N + ), by = c("higherGeography", "where")] + + if (i == 1) { + ctihighergeog_boot <- ctihighergeog_temp + } else { + ctihighergeog_boot <- rbind(ctihighergeog_boot, ctihighergeog_temp) + } + +} + +# Make plots ---- +plot(data=ctihighergeog,ctiavg~sstavg,pch=16,col=unclass(as.factor(where)),cex=1.5, + xlab="Sea surface temperature (°C)", ylab="Community Temperature Index (°C)",main="Fish") + +ggplot(ctihighergeog) + + geom_point(aes(x = sstavg, y = ctiavg, color = where), shape = 16, size = 2) + + xlab("Sea surface temperature (°C)") + + ylab("Community Temperature Index (°C)") + + theme_light() + + theme(panel.grid = element_blank(), + legend.title = element_blank()) + + +### Bootstrap version +ctihighergeog$higherGeography <- substr(ctihighergeog$higherGeography, 1, 10) +ctihighergeog$higherGeography <- factor(ctihighergeog$higherGeography, + levels = unique(ctihighergeog$higherGeography[order(ctihighergeog$sstavg)])) + +ctihighergeog_boot$higherGeography <- substr(ctihighergeog_boot$higherGeography, 1, 10) +ctihighergeog_boot$higherGeography <- factor(ctihighergeog_boot$higherGeography, + levels = levels(ctihighergeog$higherGeography)) + +ctihighergeog_boot_sum <- ctihighergeog_boot[, list( + ctiavg_low = quantile(ctiavg, 0.25), + ctiavg_median = median(ctiavg), + ctiavg_high = quantile(ctiavg, 0.75), + sdcti_low = quantile(sdcti, 0.25), + sdcti_median = median(sdcti), + sdcti_high = quantile(sdcti, 0.75), + str_low = quantile(str, 0.25), + str_median = median(str), + str_high = quantile(str, 0.75) +), by = c("higherGeography", "where")] + +ctihighergeog_sst <- ctihighergeog_boot[,list( + sstavg = mean(sstavg) +), by = c("higherGeography", "where")] +ctihighergeog_sst$what = "Average site SST" + +ggplot() + + geom_point(data = ctihighergeog_sst, aes(x = higherGeography, y = sstavg, fill = what), + shape = "|", size = 6, alpha = .1) + + geom_jitter(data = ctihighergeog_boot, aes(x = higherGeography, y = ctiavg, color = where), + shape = 16, size = 1, alpha = .05, + position = position_jitterdodge(dodge.width = 0.5, + jitter.height = 0, jitter.width = 0.1)) + + geom_linerange(data = ctihighergeog_boot_sum, + aes(x = higherGeography, ymin = ctiavg_low, ymax = ctiavg_high, + color = where), linewidth = 1, + position = position_dodge(width = 0.5)) + + geom_point(data = ctihighergeog, aes(x = higherGeography, y = ctiavg, color = where), + shape = 16, size = 2.5, position = position_dodge(width = 0.5)) + + scale_color_manual(values = c("#00c3c9", "#5151db", "#f58000")) + + scale_fill_manual(values = c("grey70")) + + guides(fill = guide_legend(override.aes = list(alpha = 1))) + + ylab("Temperature (°C)") + + xlab(NULL) + + theme_light() + + coord_flip() + + theme(panel.grid = element_blank(), + panel.grid.major.y = element_line(colour = "grey90", linewidth = 0.3, linetype = 2), + legend.title = element_blank()) + +ggsave("figures/test_newcti.png", width = 7, height = 10) + +ctihighergeog_sst_b <- ctihighergeog_sst[,list( + sstavg = mean(sstavg) +), by = c("higherGeography")] +ctihighergeog_sst_b$what <- ctihighergeog_sst$what[1] + +ggplot() + + geom_col(data = ctihighergeog_sst_b, aes(x = higherGeography, y = sstavg, fill = what), + alpha = .3, width = 0.8) + + geom_jitter(data = ctihighergeog_boot, aes(x = higherGeography, y = ctiavg, color = where), + shape = 16, size = 1, alpha = .05, + position = position_jitterdodge(dodge.width = 0.5, + jitter.height = 0, jitter.width = 0.1)) + + geom_linerange(data = ctihighergeog_boot_sum, + aes(x = higherGeography, ymin = ctiavg_low, ymax = ctiavg_high, + color = where), linewidth = 1, + position = position_dodge(width = 0.5)) + + geom_point(data = ctihighergeog, aes(x = higherGeography, y = ctiavg, color = where), + shape = 16, size = 2.5, position = position_dodge(width = 0.5)) + + scale_color_manual(values = c("#00c3c9", "#5151db", "#f58000")) + + scale_fill_manual(values = c("grey70")) + + guides(fill = guide_legend(override.aes = list(alpha = 1))) + + scale_y_continuous(expand = c(0,0,0,2)) + + ylab("Temperature (°C)") + + xlab(NULL) + + theme_light() + + coord_flip() + + theme(panel.grid = element_blank(), + 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z*#$`8t(TiwXpg2)vwbSQ;vwzN_6`ytu21jTND{$$*Vd7fZdSD_)`k5Erm1suTn7B& z1@en4M#67{F?U5+@>6Wh-e%Wp9mCwrMvUS zFtFB@)Z!(qa5oLB%0l^D^^%A(+y_kg%n-kq`cVBP@k%Z~r~}&{x`c3!{ofIh_aAwt z|BZ+`Y8D_CIZ1s%GjldGGd5FpGa#EOh>s5h_HBs|8hmtSk$e)S&;JbvS|Meq;in{ji>$>sORPWC)P7f3n;*dVZm*A z0~RX?>IDi0Aus2j7mt2KL-+KGiy?_IM4DIt;=m(nc1(;FjCQuRuP|?@pFZh}DKKR4 zLL4(<`^M5*StTUr>@CA?8c@x6(O*pPsab<)$o~txB0PNn literal 0 HcmV?d00001 diff --git a/sdm_demonstration.qmd b/sdm_demonstration.qmd new file mode 100644 index 0000000..d82c8b1 --- /dev/null +++ b/sdm_demonstration.qmd @@ -0,0 +1,75 @@ +--- +title: "SDMs MHS" +format: pdf +execute: + echo: false +editor: visual +--- + +## Example of application + +Considering only two species to see if its worthwhile: + +1. `r worrms::wm_id2name(217352)` +2. `r worrms::wm_id2name(217426)` + +Load maps and points: + +```{r} +suppressPackageStartupMessages(library(terra)) +suppressPackageStartupMessages(library(arrow)) +terraOptions(progress=0) + +species_a <- rast("results/sdms/taxonid=217352/model=mhs/predictions/taxonid=217352_model=mhs_method=maxnet_cog.tif") +species_b <- rast("results/sdms/taxonid=217426/model=mhs/predictions/taxonid=217426_model=mhs_method=maxnet_cog.tif") + +sp_a_pts <- read_parquet("results/sdms/taxonid=217352/model=mhs/taxonid=217352_model=mhs_what=fitocc.parquet") +sp_b_pts <- read_parquet("results/sdms/taxonid=217426/model=mhs/taxonid=217426_model=mhs_what=fitocc.parquet") + +par(mfrow=c(1,2)) +plot(species_a[[1]], range = c(0,1));points(sp_a_pts, pch = 20, cex = .5) +plot(species_b[[1]], range = c(0,1));points(sp_b_pts, pch = 20, cex = .5) +``` + +Apply masks to isolate to "native" areas: + +```{r} +sp_a_mask <- rast("results/sdms/taxonid=217352/model=mhs/predictions/taxonid=217352_model=mhs_mask_cog.tif") +sp_b_mask <- rast("results/sdms/taxonid=217426/model=mhs/predictions/taxonid=217426_model=mhs_mask_cog.tif") + +species_a <- mask(species_a, sp_a_mask$native_ecoregions) +species_b <- mask(species_b, sp_b_mask$native_ecoregions) + +par(mfrow=c(1,2)) +plot(species_a[[1]], range = c(0,1));points(sp_a_pts, pch = 20, cex = .5) +plot(species_b[[1]], range = c(0,1));points(sp_b_pts, pch = 20, cex = .5) +``` + +Convert to binary format (maybe we can do without it also - in fact, it may be a better choice, but interpretation is a little bit more tricky): + +```{r} +sp_a_th <- read_parquet("results/sdms/taxonid=217352/model=mhs/metrics/taxonid=217352_model=mhs_what=thresholds.parquet") +sp_b_th <- read_parquet("results/sdms/taxonid=217426/model=mhs/metrics/taxonid=217426_model=mhs_what=thresholds.parquet") + +species_a[species_a < mean(sp_a_th$p10[sp_a_th$model == "maxent"])] <- 0 +species_b[species_b < mean(sp_b_th$p10[sp_b_th$model == "maxent"])] <- 0 + +species_a[species_a > 0] <- 1 +species_b[species_b > 0] <- 1 + +par(mfrow=c(1,2)) +plot(species_a[[1]], range = c(0,1));points(sp_a_pts, pch = 20, cex = .5) +plot(species_b[[1]], range = c(0,1));points(sp_b_pts, pch = 20, cex = .5) +``` + +Sum results to get a "richness" indicator: + +```{r} +multi_sp <- sum(species_a, species_b, na.rm = T) + +par(mfrow = c(1,2)) +plot(multi_sp[[1]], main = "Current period") +plot(multi_sp$ssp245_dec50, main = "SSP2 (2050)") +``` + +Once we aggregate multiple species, we can assess how much richness will change in the WHSs.

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