Scripts for creating the spatial DB and cutting spatial variables in UTM for each jaguar

R/DataPrepFunctions.R

@@ -3,14 +3,19 @@ if(!require(install.load)) install.packages('install.load'); library(install.loa
 install.load::install_load("maptools",'move',"circular","RCurl","dplyr","readr","caTools","adehabitatLT","rgl",
                            "lubridate","raster","amt","tibble","ezknitr","lattice","rgdal","sp")
 
-## Add 2000 to years
+## WE SHOULD CHECK AND KEEP ONLY THE PACKAGES REALLY USED HERE!!
+
+#' Get year from the original dates
+#' @param time.stamp string with the timestamp (date and time) of each location
 get.year <- function(time.stamp) {
   init <- gregexpr('/', time.stamp, fixed = T)[[1]][2] + 1
   end <- gregexpr(' ', time.stamp, fixed = T)[[1]][1] - 1
   substr(time.stamp, init, end)
 }
 
-## New dates
+#' Function to add 2000 to years and define new dates
+#' @param time.stamp string with the timestamp (date and time) of each location
+#' @param year string with new to be replaced to the original year (e.g. 2014 to replace 14)
 set.year <- function(time.stamp, year) {
   init <- gregexpr('/', time.stamp, fixed = T)[[1]][2]
   end <- gregexpr(' ', time.stamp, fixed = T)[[1]][1]

R/landscape_metrics.R

@@ -0,0 +1,145 @@
+#' ---
+#' title: 'Calculating landscape metrics for jaguars in the Neotropics'
+#' author: Bernardo Niebuhr <bernardo_brandaum@yahoo.com.br>
+#' ---
+
+#' This script aims at calculating landscape metrics for the landscapes inhabited by
+#' jaguars in the Neotropics. These jaguars have been monitored with GPS collars.
+
+# packages
+library(install.load)
+install.load::install_load("tidyverse")
+install.load::install_load("landscapemetrics", "landscapetools")
+
+# data
+env.data <- read.csv2(file = "maps/table_akde.csv", header = T) %>% 
+  dplyr::as_tibble() %>% 
+  dplyr::mutate(kernel = as.numeric(kernel),
+                id = as.character(id) %>% ifelse(grepl("J", .), paste(., "per", sep = "_"), .))
+
+jaguar.data <- read.csv2(file = "HR_year_2d.csv", header = T) %>% 
+  dplyr::as_tibble() %>% 
+  tidyr::pivot_longer(cols = starts_with("area_sq_km"), 
+                      names_to = "kernel", 
+                      names_prefix = "area_sq_km_",
+                      values_to = "akde_area_sq_km") %>% 
+  dplyr::mutate(kernel = as.numeric(kernel),
+                id = as.character(id), 
+                ID = as.numeric(as.character(ID)))
+
+jaguar.data$ID[(length(jaguar.data$ID)-3):length(jaguar.data$ID)] <- c(118, 118, 119, 119)
+
+# merge
+jaguar.env.data <- jaguar.data %>% 
+  dplyr::left_join(env.data, by = c("id", "kernel")) %>% 
+  dplyr::mutate(ID = as.numeric(as.character(ID))) %>% 
+  dplyr::arrange(ID, kernel)
+
+# check
+jaguar.env.data %>% print(n = 300)
+
+unique(jaguar.env.data$id)
+
+# landscape metrics
+maps.names <- list.files("maps", pattern = "forest_year", full.names = T)
+
+maps <- list()
+
+for(i in 1:nrow(jaguar.env.data)) {
+
+  print(i)
+
+  id <- str_remove(jaguar.env.data$id[i], pattern = "_per")
+  kern <- jaguar.env.data$kernel[i]
+  id_kern <- paste0("ind", id, "_", kern, sep = "")
+
+  map.open <- maps.names %>% grep(pattern = id_kern, value = T)
+
+  if(length(map.open) == 0) {
+    maps[[i]] <- NULL
+  } else {
+    maps[[i]] <- raster::raster(map.open)
+  }
+
+}
+names(maps) <- paste(jaguar.env.data$id, jaguar.env.data$kernel, sep = "_")
+
+maps
+
+la01 <- landscapetools::show_landscape(maps$`01_95`, discrete = TRUE) +
+  scale_fill_manual(values = c("blue", "orange", "forestgreen")) +
+  labs(title = "Paisagem 01") +
+  theme(legend.position = "none",
+        axis.text.x = element_text(size = 7),
+        axis.text.y = element_text(size = 7, angle = 90, hjust = .5))
+la01
+
+# function to convert latlong to UTM zone
+long2UTM <- function(rast) {
+  center.long <- sum(raster::extent(rast)[1:2])/2
+  zone <- (floor((center.long + 180)/6) %% 60) + 1
+
+  ifelse(zone > 19, 
+         paste0("+proj=utm +zone=", zone, " +south +ellps=WGS84 +datum=WGS84 +units=m +no_defs"),
+         paste0("+proj=utm +zone=", zone, " +ellps=WGS84 +datum=WGS84 +units=m +no_defs"))
+}
+
+# test to find the utm zone
+long2UTM(maps[[97]])
+
+# for one landscape
+metric <- raster::projectRaster(maps[[2]], crs = long2UTM(maps[[2]]))
+metric[] <- ifelse(metric[] != 0, 1, 0)
+plot(metric)
+
+landscapemetrics::check_landscape(metric)
+
+class_metrics <- landscapemetrics::list_lsm() %>%
+  dplyr::filter(level == "class") %>% 
+  dplyr::arrange(type)
+class_metrics %>%  
+  print(n = 100)
+
+landscapemetrics::calculate_lsm(landscape = metric, 
+                                what = c("lsm_c_area_mn", "lsm_c_area_sd", "lsm_c_area_cv",
+                                         "lsm_c_pland", "lsm_c_te", "lsm_c_ed",
+                                         "lsm_c_para_mn", "lsm_c_para_sd", "lsm_c_para_cv")) %>% 
+  dplyr::filter(class == 1)
+
+# for all landscapes
+jaguar.env.data <- jaguar.env.data %>% 
+  dplyr::mutate(patch_area_hectares_CV = NA,
+                patch_area_hectares_avg = NA,
+                patch_area_hectares_sd = NA,
+                edge_density_m_sqm = NA,
+                perimeter_area_ratio_m_sqm_CV = NA,
+                perimeter_area_ratio_m_sqm_avg = NA,
+                perimeter_area_ratio_m_sqm_sd = NA,
+                p_landscape = NA,
+                total_edge_m = NA)
+
+for(i in 1:length(maps)) {
+
+  print(i)
+
+  if(!is.null(maps[[i]])) {
+    metric <- raster::projectRaster(maps[[i]], crs = long2UTM(maps[[i]]))
+    metric[] <- ifelse(metric[] != 0, 1, 0)
+
+    vals <- landscapemetrics::calculate_lsm(landscape = metric, 
+                                    what = c("lsm_c_area_mn", "lsm_c_area_sd", "lsm_c_area_cv",
+                                             "lsm_c_pland", "lsm_c_te", "lsm_c_ed",
+                                             "lsm_c_para_mn", "lsm_c_para_sd", "lsm_c_para_cv")) %>% 
+      dplyr::filter(class == 1) %>% 
+      pull(value)
+
+    jaguar.env.data[i,20:ncol(jaguar.env.data)] <- vals
+  } 
+
+}
+
+jaguar.env.data
+
+# export
+jaguar.env.data %>% 
+  readr::write_csv2(path = "jaguar_env_data_complete.csv")

_gitignore

@@ -0,0 +1,3 @@
+.RData
+*.RHistory
+*~

src/YEARS_JAGUAR_FINAL.csv

@@ -0,0 +1,121 @@
+ID;Sex;Year;nPoints;Year_2d
+1;Female;2010;279;10
+2;Female;2010;84;10
+3;Male;2009;148;9
+4;Female;2011;500;11
+5;Female;2009;280;9
+6;Male;2009;983;9
+7;Male;2011;621;11
+8;Male;2009;332;9
+9;Female;2014;1018;14
+10;Female;2014;1134;14
+11;Female;2014;631;14
+12;Female;2015;2058;15
+13;Male;2015;4576;15
+14;Male;2012;185;12
+15;Male;2013;1221;13
+16;Male;2011;1843;11
+17;Male;2014;537;14
+18;Male;2015;1654;15
+19;Female;2011;704;11
+20;Male;2015;2115;15
+21;Female;2011;237;11
+22;Male;2014;1719;14
+23;Male;2014;572;14
+24;Male;2012;124;12
+25;Female;2012;842;12
+25_2015;female;2015;2232;15
+26;Female;2015;3811;15
+27;Female;2010;303;10
+28;Female;2010;205;10
+29;Female;2011;67;11
+30;Female;2012;461;12
+31;Female;2013;69;13
+32;Female;2012;121;12
+33;Female;2013;99;13
+34;Female;2003;92;3
+35;Female;2001;10;1
+36;Female;2000;16;0
+37;Female;2002;211;2
+38;Female;2002;18;2
+39;Female;2005;169;5
+40;Female;2004;183;4
+41;Female;2015;4577;15
+42;Male;2009;7667;9
+43;Female;2013;574;13
+44;Female;2012;88;12
+45;Female;2012;39;12
+46;Female;2012;230;12
+47;Male;2012;561;12
+48;Male;2012;72;12
+49;Male;2015;544;15
+50;Male;2014;1127;14
+51;Male;2010;727;10
+52;Female;2014;615;14
+53;Male;2010;132;10
+53_2013;Male;2013;167;13
+54;Male;2010;128;10
+55;Male;2011;141;11
+56;Male;2011;109;11
+57;Male;2011;28;11
+58;Male;2003;159;3
+59;Male;2013;301;13
+60;Male;2013;610;13
+61;Male;2013;109;13
+62;Male;2003;156;3
+63;Male;2005;609;5
+64;Male;2012;1613;12
+65;Female;2014;1010;14
+66;Female;2013;53;13
+67;Female;2014;131;14
+68;Male;2011;996;11
+69;Female;2013;1022;13
+70;Male;2002;594;2
+71;Female;2003;614;3
+72;Male;2003;556;3
+73;Male;2004;620;4
+74;Female;2005;713;5
+75;Female;2006;940;6
+76;Female;2006;887;6
+77;Male;2006;861;6
+78;Female;2008;404;8
+79;Female;2015;2202;15
+80;Male;2010;479;10
+81;Male;2014;6821;14
+82;Male;2015;687;15
+83;Male;2012;112;12
+84;Female;2013;4295;13
+85;Male;2014;671;14
+86;Female;2013;1111;13
+87;Female;2012;398;12
+88;Female;2014;650;14
+89;Male;2013;1152;13
+89_2015;Male;2015;799;15
+90;Female;2012;188;12
+91;Male;2011;50;11
+92;Female;2012;59;12
+93;Male;2014;903;14
+94;Male;2015;516;15
+95;Male;2013;61;13
+96;Female;2013;491;13
+97;Male;2014;260;14
+98;Female;2011;746;11
+99;Female;2013;3216;13
+100;Female;2015;42;15
+101;Male;2015;296;15
+102;Female;2016;151;16
+103;Female;2014;20;14
+104;Male;2015;118;15
+105;Female;2009;1355;9
+106;Male;2009;225;9
+107;Male;2008;287;8
+108;Male;2008;481;8
+109;Female;2008;165;8
+110;Male;2010;166;10
+111;Female;2009;1051;9
+112;Female;2008;202;8
+113;Female;2008;481;8
+114;Female;2008;873;8
+115;Female;2008;848;8
+116;Male;2015;1549;15
+117;Female;2015;1888;15