diff --git a/tier_1/.DS_Store b/tier_1/.DS_Store
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diff --git a/tier_1/code/sample_code.R b/tier_1/code/sample_code.R
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+++ b/tier_1/code/sample_code.R
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+# this script includes 6 main steps:
+# 1. load the climate data
+# 2. create a grid from the data
+# 3. convert the species' geographic range data to the gridded format
+# 4. convert climate data to the gridded format
+# 5. estimate thermal limits
+# 6. estimate exposure (0 and 1 matrices)
+
+# let's start:
+
+if(!requireNamespace("pacman", quietly = TRUE)) install.packages("pacman")
+pacman::p_load(terra, tidyverse, sf, here, stars, tidyterra, furrr, exactextractr, parallel, pbapply)
+
+
+################## step 1: read climate data ####################
+
+# the climate data comes in three main formats: netcdfs, rasters (e.g. geotiffs), and arrays
+# netctdfs and rasters are the most common formats.
+# arrays are a peculiar type of data used by the folks at the Barcelona Computing Centre
+
+# first, an example with a raster (the code would be similar for a netcdf)
+# load climate data (mean annual temperature)
+# historical (1850-2014) and future high-emissions scenarios ssp5-8.5 (2015-2100)
+historical <- rast(here("tier_1/data/climate/historical.tif"))
+future <- rast(here("tier_1/data/climate/ssp585.tif"))
+
+plot(historical[[1]])
+plot(future[[1]])
+
+
+# when the climate data is an array, we have to create the rasters ourselves
+# here is an example using historical climate data for Africa
+
+array_data <- readRDS(here("tier_1/data/climate/historical_array.rds"))
+dim(array_data) # the data spans 54 years (1960-2014). in this case, data is monthly, not yearly. member = 10 means that the array has data from 10 different climate models.
+
+
+# top build the raster, we first need to get longitude and latitude range
+lon_range <- range(attributes(array_data)$Variables$common$lon[1:length(attributes(array_data)$Variables$common$lon)])
+lat_range <- range(attributes(array_data)$Variables$common$lat[1:length(attributes(array_data)$Variables$common$lat)])
+my_extent <- c(lon_range, lat_range)
+
+# array_to_raster returns a list where each element represents a year,
+# and contains a raster stack with 12 rasters, one for each month (jan-dec)
+array_to_raster <- function(data, start_year, members = F, raster.extent){
+
+  n_years <- 1:dim(data)[["year"]]
+  n_months <- 1:dim(data)[["month"]]
+
+  if(isFALSE(members)){
+
+    r <- map(n_years, function(year){
+
+      month_list <- map(n_months, function(month){
+
+        r <- flip(
+          rast(
+            t(
+              data[,,year,month]), extent = ext(raster.extent),
+            crs = "+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0"
+          ))
+
+        # names(r) <- lubridate::month(month, label = T)
+        names(r) <- month
+        r
+      })
+
+      r <- rast(month_list)
+      r <- r - 273.15
+      r
+
+    })
+
+    names(r) <- (start_year - 1) + n_years
+
+    return(r)
+
+  } else {
+
+    n_members <- 1:dim(data)[["member"]]
+
+
+    r_final <- map(n_members, function(member){
+
+      year_list <- map(n_years, function(year){
+
+        month_list <- map(n_months, function(month){
+
+          r <- flip(
+            rast(
+              t(
+                data[,,year,month,member]), extent = ext(raster.extent),
+              crs = "+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0"
+            ))
+
+          names(r) <- month
+          r
+
+
+        })
+
+        r <- rast(month_list)
+        r <- r - 273.15
+        r
+
+      })
+
+      names(year_list) <- (start_year - 1) + n_years
+      return(year_list)
+    })
+
+
+    names(r_final) <- paste0("member_", n_members)
+    return(r_final)
+
+  }
+
+}
+
+historical_array <- array_to_raster(array_data, start_year = 1961, members = T, raster.extent = my_extent)
+plot(historical_array$member_1$`1961`[[1]])
+
+################## step 2: create a grid from the data ####################
+
+# climate data always comes in gridded formats. biodiversity data no. therefore, we have to
+# create a grid so we can transform the biodiversity data to the gridded format.
+# one approach is use the climate data to create the grid.
+
+sf_use_s2(FALSE)
+
+grid <- historical %>%
+  st_as_stars() %>%
+  st_as_sf() %>%
+  mutate(world_id = 1:nrow(.)) %>%
+  select(world_id)
+
+# check if the grid overlaps with the climate data
+ggplot() +
+  geom_spatraster(data = historical[[1]]) +
+  geom_sf(data = grid, fill = NA, linewidth = 0.2) +
+  scale_fill_viridis_c(na.value = NA)
+
+
+################## step 3: convert the geographic range data to the gridded format ####################
+
+# now we transform the species' range polygons into a gridded format.
+# for this, I use the function below.
+# the function returns a list, where each element represent a different species.
+# each element is a vector containing the ids of the grid cells where the species occur.
+
+prepare_range <- function(range_data, grid, realm){
+
+
+  # filter range data
+  # presence = 1: extant
+  # origin = 1: native, 2: reintroduced,
+  # seasonal = 1: resident, 2: breeding
+
+  range_filtered <- range_data %>%
+    dplyr::filter(presence == 1,
+                  origin %in% c(1,2),
+                  seasonal %in% c(1,2))
+
+  # realm is used to separate terrestrial an marine species
+  if(realm == "terrestrial"){
+
+    range_filtered <- range_filtered %>%
+      dplyr::filter(terrestial == "true") #codespell:ignore terrestial
+
+  }
+
+  if(realm == "marine"){
+
+    range_filtered <- range_filtered %>%
+      dplyr::filter(marine == "true")
+
+  }
+
+  # i usually run this in parallel
+  plan("multisession", workers = availableCores() - 1)
+
+  # this function intersects the grid with the individual ranges
+  # identify which grid cells overlap with the range
+  # an return the ids of the grid cells
+  res <- future_map(st_geometry(range_filtered), possibly(function(x){
+    y <- st_intersects(x, grid)
+    y <- unlist(y)
+    y <- grid %>%
+      slice(y) %>%
+      pull(world_id)
+
+    return(y)
+
+  }, quiet = T), .progress = TRUE)
+
+
+  names(res) <- range_filtered$sci_name
+
+  # if a range don't overlap with the grid, it gets removed
+  res <- discard(res, is.null)
+
+  # combining elements with same name
+  res_final <- tapply(unlist(res, use.names = FALSE), rep(names(res), lengths(res)), FUN = c)
+
+  return(res_final)
+
+}
+
+# load geographic range data
+# these are range maps of 120 amphibians species in shapefile format
+species_ranges <- st_read(here("tier_1/data/species_ranges/subset_amphibians.shp"))
+
+# run
+gridded_ranges <- prepare_range(species_ranges, grid, realm = "terrestrial")
+
+
+################## step 4: extract climate data to the gridded format ####################
+
+# i use tibbles as the main data format for the analyses.
+# therefore, one of the step of our workflow is to transform the climate data into a tibble.
+# this is done by extracting the climate data using the grid.
+
+historical_grid <- exact_extract(historical, grid, fun = "mean") - 273.15 # - 273.15 transforms the climate data from Kelvin to Celsius
+future_grid <- exact_extract(future, grid, fun = "mean") - 273.15
+
+# rename and make a tibble
+historical_grid <- historical_grid %>%
+  rename_with(~ as.character(1850:2014)) %>%
+  mutate(world_id = grid$world_id) %>%
+  relocate(world_id) %>%
+  as_tibble()
+
+future_grid <- future_grid %>%
+  rename_with(~ as.character(2015:2100)) %>%
+  mutate(world_id = grid$world_id) %>%
+  relocate(world_id) %>%
+  as_tibble()
+
+
+# in these tibbles, each row represent a grid cell, and each column represents a time step
+# this works pretty well when time steps are years and  grid cells have 100 x 100 km resolution.
+# but if the climate data had 1 x 1 km resolution, these tibbles would have over 177 million rows (!)
+# including the ocean in the analyses would add an extra 400 million rows (at 1 km resolution).
+
+
+
+
+################## step 5: estimate thermal limits ####################
+
+# now that we have both the climate data and range data in gridded formats,
+# we can now estimate the thermal limits of the species.
+
+# this is one of the functions we use to estimate niche limits.
+
+get_niche_limits <- function(spp_data, climate_data){
+
+
+  res <- climate_data %>%
+    filter(world_id %in% spp_data) %>%
+    select(-world_id) %>%
+    summarise(niche_max = max(c_across(everything())),
+              niche_99th = quantile(c_across(everything()), probs = .99, type = 7),
+              niche_95th = quantile(c_across(everything()), probs = .95, type = 7))
+
+  return(res)
+
+}
+
+plan("multisession", workers = 2)
+niche_limits <- future_map_dfr(gridded_ranges, ~ get_niche_limits(.x, historical_grid), .id = "species")
+niche_limits
+
+################## step 6: estimate exposure ####################
+
+# from this point, i am using the same code i shared previously, which is in the repository already
+
+# function to calculate exposure
+exposure <- function(data, species_range, climate_data, niche){
+
+  spp_data <- species_range[[data]]
+  spp_name <- names(species_range)[[data]]
+
+  spp_matrix <- climate_data %>%
+    filter(world_id %in% spp_data) %>%
+    na.omit()
+
+  spp_niche <- niche %>%
+    filter(species %in% spp_name)
+
+
+  spp_matrix <- spp_matrix %>%
+    mutate(across(2:ncol(spp_matrix), ~ case_when(. <= spp_niche$niche_max ~ 1,
+                                                  . > spp_niche$niche_max ~ 0)))
+
+  spp_matrix$species <- spp_name
+  spp_matrix <- spp_matrix %>%
+    relocate(species)
+
+  return(spp_matrix)
+
+}
+
+# run
+plan("multisession", workers = availableCores() - 1)
+exposure_list <- future_map(1:length(gridded_ranges), ~ exposure(.x, gridded_ranges, future_grid, niche_limits), .progress = T)
+names(exposure_list) <- names(gridded_ranges)
+exposure_list
+
+# calculate exposure times
+exposure_times <- function(data, original.state, consecutive.elements){
+
+  species <- data[1]
+  world_id <- data[2]
+
+  n <- as.numeric(data[-c(1,2)])
+
+  # Calculate shift sequences
+  rle_x <- data.frame(unclass(rle(n)))
+
+  # Add year
+  rle_x$year <- 2015 + cumsum(rle_x$lengths) - rle_x$lengths
+
+  # Select only shifts with n or more consecuitve elements
+  rle_x <- rle_x[rle_x$lengths >= consecutive.elements,]
+
+  # Add line with original state
+  rle_x <- rbind(c(1, original.state, 2000), rle_x)
+
+  # Remove lines with shifts that are not separated for n consecutive elements
+  rle_x <- rle_x[c(TRUE, diff(rle_x$values) != 0),]
+
+  # Remove first line because the first line is either the original state
+  # or the same value as the original state
+  rle_x <- rle_x[-1,]
+
+  # if there are no rows in rle_x, it means no exposure
+  if(nrow(rle_x) == 0) {
+
+    exposure <- NA
+    deexposure <- NA
+    duration <- NA
+
+    return(tibble(species, world_id, exposure, deexposure, duration))
+
+  }
+
+
+  # if the only value in x$values is 0, it means that there was a single exposure event
+  # with no de-exposure
+
+  if(length(unique(rle_x$values)) == 1){
+    if(unique(rle_x$values) == 0){
+
+      exposure <- rle_x$year[1]
+      deexposure <- 2101 # if deexposure = 2101 it means that deexposure did not occur
+      duration <- deexposure - exposure
+      return(tibble(species, world_id, exposure, deexposure, duration))
+    }
+  }
+
+  # the remaining data will always have 0 and 1 on rle_x$values
+  if(length(unique(rle_x$values)) == 2){
+
+    exposure <- rle_x %>%
+      filter(values == 0) %>%
+      pull(year)
+
+    deexposure <- rle_x %>%
+      filter(values == 1) %>%
+      pull(year)
+
+    # if(length(deexposure) == 0) deexposure <- 2201
+    if(length(exposure) > length(deexposure))  deexposure[length(exposure)] <- 2101
+
+    duration <- deexposure - exposure
+
+    return(tibble(species, world_id, exposure, deexposure, duration))
+
+  }
+}
+
+# run
+exposure_df <- exposure_list %>%
+  bind_rows() %>%
+  mutate(sum = rowSums(select(., starts_with("2")))) %>%
+  filter(sum < 82) %>%  # Select only cells with < 82 suitable years (>= 82 years means no exposure).This is done to improve computational time by avoiding calcluting exposure for species that are not exposed.
+  select(-sum)
+
+cl <- makeCluster(availableCores() - 1)
+clusterEvalQ(cl, library(dplyr))
+clusterExport(cl, "exposure_times")
+
+
+
+res_final <- pbapply(X = exposure_df,
+                     MARGIN = 1,
+                     FUN = function(x) exposure_times(data = x, original.state = 1, consecutive.elements = 5),
+                     cl = cl)
+
+
+res_final <- res_final %>%
+  bind_rows() %>%
+  na.omit()
+
+
+stopCluster(cl)
+
+# final tibble with exposure times for each species at each grid cell
+res_final
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diff --git a/tier_1/data/species_ranges/subset_amphibians.csv b/tier_1/data/species_ranges/subset_amphibians.csv
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@@ -0,0 +1,2401 @@
+species,lon,lat
+Afrixalus weidholzi,-7.245817209982505,12.726827498005472
+Afrixalus weidholzi,14.962842165436594,8.357700870710872
+Afrixalus weidholzi,11.492647439946936,10.109996982476503
+Afrixalus weidholzi,-2.8770733107884254,8.003989671875942
+Afrixalus weidholzi,0.6677237146291581,10.689493964400882
+Afrixalus weidholzi,-9.54560342932562,9.506607161062734
+Afrixalus weidholzi,7.2142662674071545,8.704701160354901
+Afrixalus weidholzi,-3.7094651010056747,9.866521933065476
+Afrixalus weidholzi,0.7533138648858184,10.83568262535423
+Afrixalus weidholzi,-16.261616733792238,12.577584624861343
+Afrixalus weidholzi,14.34487770080381,6.9837714363166
+Afrixalus weidholzi,-1.290946048067596,7.812038796454279
+Afrixalus weidholzi,21.08924740405905,7.550457581983926
+Afrixalus weidholzi,10.317372506739925,9.283747202777846
+Afrixalus weidholzi,20.09398721768411,6.8193503362958765
+Afrixalus weidholzi,13.80021312931541,9.191217379430215
+Afrixalus weidholzi,-4.709485655472289,11.698267228880864
+Afrixalus weidholzi,0.24019196078801386,9.238547724273811
+Afrixalus weidholzi,3.1233005785329304,10.116471903713132
+Afrixalus weidholzi,-11.417009787010501,13.232444099972527
+Alcalus tasanae,98.9470182630945,15.441623986279891
+Alcalus tasanae,97.12277659198728,23.952559535239722
+Alcalus tasanae,98.86816937581521,15.289501592064887
+Alcalus tasanae,98.94112739112066,15.63652471305114
+Alcalus tasanae,97.52208618905306,25.071007232416267
+Alcalus tasanae,98.9557584985233,15.845665860446722
+Alcalus tasanae,98.82124691798688,9.9839542385413
+Alcalus tasanae,96.09763813253532,25.14875226847374
+Alcalus tasanae,96.53718849370456,25.58108341879963
+Alcalus tasanae,99.1827777487571,14.179490105815377
+Alcalus tasanae,95.68823438660186,26.58073943623443
+Alcalus tasanae,96.7873273028725,26.63296095464967
+Alcalus tasanae,97.49534194905911,26.615800333450935
+Alcalus tasanae,96.32960480178983,26.455781299891825
+Alcalus tasanae,96.80528664449027,26.58130495683066
+Alcalus tasanae,97.74250201862824,26.18497043120642
+Alcalus tasanae,97.12104167292176,24.883888941971122
+Alcalus tasanae,95.25853205265557,25.289685207799966
+Alcalus tasanae,97.01353461604101,26.73795724020766
+Alcalus tasanae,95.38992861523658,25.34441654508412
+Allobates femoralis,-56.70880533106913,-2.9914889569176824
+Allobates femoralis,-71.69326226671365,1.3432843186715124
+Allobates femoralis,-52.0533238363077,-0.8712018505566818
+Allobates femoralis,-73.64134370159351,-1.1025286683758762
+Allobates femoralis,-54.824345743756865,0.7890488049811815
+Allobates femoralis,-51.577708806302475,3.34772834858928
+Allobates femoralis,-71.70600389008293,-5.569064179118606
+Allobates femoralis,-48.573487099692315,-2.754392585882847
+Allobates femoralis,-52.71188888046926,-1.7755319644717273
+Allobates femoralis,-60.045504058147074,5.5791606104745926
+Allobates femoralis,-60.00072317523893,0.19174284484395843
+Allobates femoralis,-58.18315861501433,-5.43654866483332
+Allobates femoralis,-68.0131449934546,-7.049324584744106
+Allobates femoralis,-52.42427208981924,-7.989349195598411
+Allobates femoralis,-55.73955360421189,-0.3458806987575909
+Allobates femoralis,-57.515744649168674,3.5562471267469444
+Allobates femoralis,-64.87680394774591,-1.3881506527303618
+Allobates femoralis,-54.063539193487905,-8.618366312709188
+Allobates femoralis,-71.14454549905605,1.7220160182093323
+Allobates femoralis,-54.79397684933531,1.9934983894156846
+Ambystoma californiense,-121.04088547031189,37.883106976737444
+Ambystoma californiense,-121.43199918609689,37.78229042600327
+Ambystoma californiense,-119.71375665528987,36.99549882400097
+Ambystoma californiense,-120.93572768975882,36.49914256287292
+Ambystoma californiense,-121.93409570479771,37.66286701000728
+Ambystoma californiense,-121.41358973926157,38.06463213015905
+Ambystoma californiense,-121.54398183627964,36.929565997359056
+Ambystoma californiense,-121.02432898221669,37.58742814164703
+Ambystoma californiense,-121.7097424431173,36.74704826232754
+Ambystoma californiense,-120.90871534904788,37.23620751977646
+Ambystoma californiense,-121.69259100899968,37.730395418836146
+Ambystoma californiense,-121.86621868902014,37.42717273843808
+Ambystoma californiense,-121.53739958059911,36.5915711930079
+Ambystoma californiense,-122.30470411672577,37.69406908884155
+Ambystoma californiense,-121.76638585949127,37.11183063587978
+Ambystoma californiense,-122.06351668780825,37.48884213707018
+Ambystoma californiense,-120.76299269417959,35.363567810486856
+Ambystoma californiense,-119.85100557978417,37.05361940526403
+Ambystoma californiense,-121.01251234551505,36.9377668222391
+Ambystoma californiense,-120.25698885192834,35.82547272153734
+Anaxyrus retiformis,-111.8961846984574,31.080512874626688
+Anaxyrus retiformis,-110.46584856498937,28.19243603113514
+Anaxyrus retiformis,-110.54979222602827,28.024638820564814
+Anaxyrus retiformis,-111.5327688957699,29.25985149509539
+Anaxyrus retiformis,-111.93438987758974,31.22514193698545
+Anaxyrus retiformis,-111.64603917598919,31.99874709967263
+Anaxyrus retiformis,-111.4050155270791,29.555902102325533
+Anaxyrus retiformis,-111.63591354521901,31.96916835512608
+Anaxyrus retiformis,-111.85948303214818,30.86150735543947
+Anaxyrus retiformis,-111.30341718280332,29.362610647556725
+Anaxyrus retiformis,-110.60333378773765,28.31474439500343
+Anaxyrus retiformis,-111.97823455112949,31.09976558155336
+Anaxyrus retiformis,-111.74756241422871,31.143609893755585
+Anaxyrus retiformis,-111.21954078900252,29.65686822958546
+Anaxyrus retiformis,-112.19237923556673,30.739298067186795
+Anaxyrus retiformis,-110.96183749010576,29.4994360065416
+Anaxyrus retiformis,-111.65842708860053,30.10999601086272
+Anaxyrus retiformis,-110.62216682447878,28.25572345976232
+Anaxyrus retiformis,-111.14266278033749,30.264152906323233
+Anaxyrus retiformis,-111.6201710168004,31.901248019542706
+Andinobates bombetes,-76.53775806705856,3.9328738382144124
+Andinobates bombetes,-75.59797254490903,4.692445172853299
+Andinobates bombetes,-76.01118025701238,4.973675162743524
+Andinobates bombetes,-76.51341422182415,3.986099418849131
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+Ichthyophis paucidentulus,102.70128884633012,-3.647996592558817
+Ichthyophis paucidentulus,102.75254641105234,-3.501665528594025
+Ichthyophis paucidentulus,102.65260846373997,-3.49145157232155
+Ichthyophis paucidentulus,102.62555150039228,-3.437716144743255
+Ingerana charlesdarwini,92.6491004643256,11.70145623398206
+Ingerana charlesdarwini,92.64219303276913,11.67424346183333
+Ingerana charlesdarwini,92.69886285445119,11.6744273733374
+Ingerana charlesdarwini,92.6435162862339,11.67338804158354
+Ingerana charlesdarwini,93.00450542104815,13.144563444818864
+Ingerana charlesdarwini,92.680389747659,11.693828492645537
+Ingerana charlesdarwini,92.69133926048367,11.684224996399658
+Ingerana charlesdarwini,93.02258394497719,13.166556076598317
+Ingerana charlesdarwini,92.67724377083152,11.680312877721915
+Ingerana charlesdarwini,93.03577249008694,13.147259706083886
+Ingerana charlesdarwini,92.64772346549024,11.66052924874472
+Ingerana charlesdarwini,92.68815680902345,11.703755290245757
+Ingerana charlesdarwini,92.68368033054803,11.673039086916575
+Ingerana charlesdarwini,93.0212707491986,13.144687596924532
+Ingerana charlesdarwini,93.02786026656628,13.136925703949894
+Ingerana charlesdarwini,92.99735160091053,13.140131051803143
+Ingerana charlesdarwini,92.68755356538946,11.661231532101787
+Ingerana charlesdarwini,92.66428447022335,11.661202601275473
+Ingerana charlesdarwini,92.66495245668176,11.667806840952057
+Ingerana charlesdarwini,93.02788230104309,13.163668609278261
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+Ischnocnema gualteri,-43.01343020412961,-22.17411168189278
+Ischnocnema gualteri,-43.084554817519745,-22.44915673959074
+Ischnocnema gualteri,-43.1098133338509,-22.359042817996762
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+Ischnocnema nasuta,-43.910365684846056,-22.640381185586676
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+Kassina cassinoides,-8.552489125465833,14.493666595704783
+Kassina cassinoides,9.200096824397678,10.540653285868324
+Kassina cassinoides,-6.643199221963087,10.982439636831597
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+Kassina cassinoides,13.271917906429072,10.373612151572738
+Kassina cassinoides,0.037744659891977506,13.758497535909786
+Kassina cassinoides,-10.547772621037254,11.316991124672883
+Leptobrachella dringi,117.5052053649747,1.466235410223238
+Leptobrachella dringi,112.37914941009528,0.21607999991576354
+Leptobrachella dringi,116.45192095284719,3.6286124249112786
+Leptobrachella dringi,113.63950267013544,0.176430294563475
+Leptobrachella dringi,115.62047315970898,4.1516234811929325
+Leptobrachella dringi,116.67600689773126,3.740865954285764
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+Leptobrachella dringi,111.86672992673643,-1.070147886904521
+Leptobrachella dringi,112.85199936349302,0.21292400949658086
+Leptobrachella dringi,110.12584721504307,-0.24894158437225103
+Leptobrachella dringi,116.73459648607422,3.044321776695826
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+Leptobrachella dringi,109.87696630474659,0.7522604508496106
+Leptobrachella dringi,112.24679289469609,0.3785914840793175
+Leptobrachella dringi,110.1373903303422,0.3087161556194868
+Leptobrachella dringi,113.93435522872578,0.1464716359738524
+Leptobrachella dringi,114.47485644924711,-0.2663365762119959
+Leptobrachella dringi,116.62379694197969,3.2514619675947745
+Leptodactylodon axillaris,10.067467651158296,5.630244667382798
+Leptodactylodon axillaris,10.090678109706994,5.630322520648346
+Leptodactylodon axillaris,10.089930283362891,5.667524234357317
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+Leptodactylodon axillaris,10.115767608264422,5.665068607275538
+Leptodactylodon axillaris,10.104994568267202,5.648365413525322
+Leptodactylodon axillaris,10.059190269386939,5.6743418781501465
+Leptodactylodon axillaris,10.071266744290279,5.627342123912695
+Leptodactylodon axillaris,10.108060484143643,5.649065592112871
+Leptodactylodon axillaris,10.112338324004677,5.663268316140551
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+Leptodactylodon axillaris,10.058524355679793,5.611860948067495
+Leptodactylodon axillaris,10.108839145987336,5.634225827058178
+Leptodactylodon axillaris,10.070017323783327,5.6766934706631345
+Leptodactylodon axillaris,10.105154135908077,5.659623096784369
+Leptodactylodon axillaris,10.106868440447418,5.653161670428975
+Leptodactylodon axillaris,10.08503224246805,5.609820537571371
+Leptodactylodon axillaris,10.054038064966004,5.63822882536738
+Leptodactylodon axillaris,10.050180434367462,5.654928671047798
+Leptodactylodon axillaris,10.09308033379756,5.672981594678105
+Leptodactylus chaquensis,-61.79015099570404,-27.587927294620638
+Leptodactylus chaquensis,-55.576566509071306,-18.634927818014738
+Leptodactylus chaquensis,-56.667189488435284,-20.488091882796063
+Leptodactylus chaquensis,-66.146513706198,-12.684428678178852
+Leptodactylus chaquensis,-57.635946267676815,-19.0163916087749
+Leptodactylus chaquensis,-61.507363212590064,-24.048426569930143
+Leptodactylus chaquensis,-59.25652434530129,-27.768473962059183
+Leptodactylus chaquensis,-57.29808030073792,-30.874100894653235
+Leptodactylus chaquensis,-58.61387191334604,-30.022222895889072
+Leptodactylus chaquensis,-60.82431360319534,-28.982627949087163
+Leptodactylus chaquensis,-67.59653267886009,-12.590575641660072
+Leptodactylus chaquensis,-54.14501059797791,-21.668738160251163
+Leptodactylus chaquensis,-60.21958615103958,-16.962816874228434
+Leptodactylus chaquensis,-60.40992619598993,-30.531149396977273
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+Leptodactylus chaquensis,-58.242965473016724,-17.847791620176103
+Leptodactylus chaquensis,-60.133958955709275,-18.398938489797466
+Leptodactylus chaquensis,-65.16700499315876,-13.84791487618147
+Leptodactylus chaquensis,-57.707348363618806,-23.78535678675558
+Leptodactylus colombiensis,-73.97896926035759,2.44019137839985
+Leptodactylus colombiensis,-74.0592388995699,3.7374336034615196
+Leptodactylus colombiensis,-74.87175960179827,3.7579637056634843
+Leptodactylus colombiensis,-75.2756663313138,7.299114472028458
+Leptodactylus colombiensis,-74.53735276118167,5.294488285573105
+Leptodactylus colombiensis,-75.33344786717474,6.548380964580709
+Leptodactylus colombiensis,-74.40428042149065,5.201020338312876
+Leptodactylus colombiensis,-74.29933785886129,5.313433843148917
+Leptodactylus colombiensis,-76.06776482781494,7.67651323176651
+Leptodactylus colombiensis,-76.91095353247424,2.55376025272107
+Leptodactylus colombiensis,-74.77427669876879,4.133695227172677
+Leptodactylus colombiensis,-71.78687295808218,6.814713097781168
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+Leptodactylus colombiensis,-74.45132381299129,4.71177396930792
+Leptodactylus colombiensis,-74.85844615673551,4.791342651342807
+Leptodactylus colombiensis,-76.8221989000123,3.2560689321787333
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+Leptodactylus stenodema,-49.511316897914384,-4.0520844442323964
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+Leptodactylus stenodema,-52.10588733208273,-7.09466365847135
+Leptodactylus stenodema,-57.04608377549409,-5.243185041247615
+Litoria vocivincens,147.88958032594599,-9.783912380057934
+Litoria vocivincens,146.42720868429782,-8.43575342832703
+Litoria vocivincens,147.77933822105845,-9.972138140987786
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+Litoria vocivincens,145.62301320586806,-7.706561166770692
+Litoria vocivincens,149.15827223106243,-10.184776100793338
+Mannophryne urticans,-71.22108701075776,8.954597497358824
+Mannophryne urticans,-71.5242000414485,8.627611890969778
+Mannophryne urticans,-71.4301028358992,8.793709348666695
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+Mannophryne urticans,-71.59248612639912,8.528902109816386
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+Mannophryne urticans,-71.54696722939322,8.680591124625778
+Mannophryne urticans,-71.19613453234851,8.903568688032315
+Mantella manery,48.80423972910351,-13.901418686363376
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+Oreophryne sibilans,134.6328329938197,-2.927938445187464
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+Oreophryne sibilans,134.630911962412,-2.9343537288058594
+Oreophryne sibilans,134.6378101213708,-2.929716191586475
+Oreophryne sibilans,134.6293427670166,-2.9282102574623017
+Oreophryne sibilans,134.63177571402716,-2.937800492731385
+Oreophryne sibilans,134.6336982551087,-2.9371250625102694
+Oreophryne sibilans,134.639163292185,-2.9363391527878253
+Oreophryne sibilans,134.63227061013717,-2.9287673633735167
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+Oreophryne sibilans,134.6373989179087,-2.935696725043082
+Oreophryne sibilans,134.63448633503828,-2.9381328091952215
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+Pedostibes tuberculosus,77.14283928083601,8.758877380277939
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+Pedostibes tuberculosus,77.2385899371784,8.585059451954281
+Pedostibes tuberculosus,77.24440014485154,8.645528797644559
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+Philoria loveridgei,152.99290719155692,-28.24969726205484
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+Philoria loveridgei,153.03086655575495,-28.298618281631647
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+Philoria loveridgei,152.92112591032128,-28.235597913803247
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+Philoria loveridgei,152.9809651031702,-28.29633937094549
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+Platymantis luzonensis,122.55770603494065,14.158101379079207
+Platymantis luzonensis,123.53483570784778,13.197376264468303
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+Platymantis luzonensis,121.91981424464028,14.854946592618115
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+Platymantis luzonensis,123.31745298350015,13.68858750987235
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+Platymantis luzonensis,121.50651187560862,14.13044908967545
+Platymantis luzonensis,122.68537543148369,14.020466318863246
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diff --git a/tier_1/data/species_ranges/subset_amphibians.dbf b/tier_1/data/species_ranges/subset_amphibians.dbf
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diff --git a/tier_1/data/species_ranges/subset_amphibians.prj b/tier_1/data/species_ranges/subset_amphibians.prj
new file mode 100644
index 0000000..0ae685b
--- /dev/null
+++ b/tier_1/data/species_ranges/subset_amphibians.prj
@@ -0,0 +1 @@
+GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]]
diff --git a/tier_1/data/species_ranges/subset_amphibians.shp b/tier_1/data/species_ranges/subset_amphibians.shp
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diff --git a/tier_1/data/species_ranges/subset_amphibians.shx b/tier_1/data/species_ranges/subset_amphibians.shx
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