Package 'dissmapr' reference manual

Title:	Workflow for Compositional Dissimilarity and Biodiversity Turnover Analysis
Description:	Tools to compute and map biodiversity turnover at the macroscale.
Authors:	Sandra MacFadyen [aut, cre]
Maintainer:	Sandra MacFadyen <[email protected]>
License:	MIT + file LICENSE
Version:	0.1.0
Built:	2026-05-31 07:16:12 UTC
Source:	https://github.com/b-cubed-eu/dissmapr

Calculate Species Abundance

Description

Sums total individuals per site; reports absolute pairwise differences and dispersion metrics for higher orders.

Usage

abund(vec_from, vec_to = NULL)
abund(vec_from, vec_to = NULL)

Arguments

vec_from

A numeric vector representing species counts at the first site.

vec_to

(Optional) A numeric vector for pairwise or higher-order comparisons.

Value

The species abundance value.

Add Nearest Mapsheet Code and Center Coordinates

Description

This function takes an existing data frame with coordinate columns and appends:

the nearest mapsheet code (format "E|WDDDN|SDDBB"), and
the center longitude/latitude of that mapsheet.

Usage

assign_mapsheet(
  data,
  x_col = "x",
  y_col = "y",
  cell_size = 1,
  unit = c("deg", "min", "sec", "m")
)
assign_mapsheet(
  data,
  x_col = "x",
  y_col = "y",
  cell_size = 1,
  unit = c("deg", "min", "sec", "m")
)

Arguments

data

A data.frame containing at least the coordinate columns.

x_col

Character. Name of the longitude (x) column. Default "x".

y_col

Character. Name of the latitude (y) column. Default "y".

cell_size

Numeric. Cell size: e.g. 1 for 1deg cells, 15 for 15' cells.

unit

Character. Unit of cell_size; one of:

"deg" for decimal degrees (default)
"min" for arc-minutes
"sec" for arc-seconds
"m" for metres (projected coords).

Details

It divides the world into regular grid cells (in degrees, minutes, seconds, or metres), finds which cell each point falls into, and then formats the sheet code based on the cell centre.

Value

The input data with three new columns:

mapsheet: the 7-character sheet code
center_lon: centre longitude of that sheet
center_lat: centre latitude of that sheet

Examples

bird_obs = data.frame(
  site_id = 1:6,
  x = c(22.71862, 20.40034, 18.51368, 18.38477, 23.56160, 18.87285),
  y = c(-33.98912, -34.45408, -34.08271, -34.25946, -33.97620, -34.06225),
  species = c("Fulica cristata", "Numida meleagris coronatus",
              "Anas undulata", "Oenanthe familiaris",
              "Cyanomitra veroxii", "Gallinula chloropus"),
  value = rep(1, 6),
  year = c(2023, 2022, 2023, 2016, 2016, 2019),
  month = c(5, 12, 10, 8, 9, 2),
  day = c(12, 4, 3, 1, 4, 21)
)

# For 1deg mapsheet cells:
bird_obs2 = assign_mapsheet(bird_obs, cell_size = 1, unit = "deg")
head(bird_obs2)

bird_obs = data.frame(
  site_id = 1:6,
  x = c(22.71862, 20.40034, 18.51368, 18.38477, 23.56160, 18.87285),
  y = c(-33.98912, -34.45408, -34.08271, -34.25946, -33.97620, -34.06225),
  species = c("Fulica cristata", "Numida meleagris coronatus",
              "Anas undulata", "Oenanthe familiaris",
              "Cyanomitra veroxii", "Gallinula chloropus"),
  value = rep(1, 6),
  year = c(2023, 2022, 2023, 2016, 2016, 2019),
  month = c(5, 12, 10, 8, 9, 2),
  day = c(12, 4, 3, 1, 4, 21)
)

# For 1deg mapsheet cells:
bird_obs2 = assign_mapsheet(bird_obs, cell_size = 1, unit = "deg")
head(bird_obs2)

Compute Order-wise Metrics

Description

This function computes metrics for ecological data across specified order levels. It supports single-site, pairwise, and higher-order calculations, and allows for parallel processing for efficiency.

Usage

compute_orderwise(
  df,
  func,
  site_col,
  sp_cols = NULL,
  order = 2,
  sample_no = NULL,
  sample_portion = 1,
  parallel = TRUE,
  n_workers = parallel::detectCores() - 1
)
compute_orderwise(
  df,
  func,
  site_col,
  sp_cols = NULL,
  order = 2,
  sample_no = NULL,
  sample_portion = 1,
  parallel = TRUE,
  n_workers = parallel::detectCores() - 1
)

Arguments

df

A data frame containing the ecological data.

func

A function to compute metrics. It must accept inputs in the form of species vectors or site information depending on the order.

site_col

A character string specifying the column name in df representing site IDs.

sp_cols

A vector of column names in df representing species data (default: NULL).

order

An integer or vector of integers specifying the order(s) of computation.

1: Single-site computations.
2: Pairwise computations.
⁠>= 3⁠: Higher-order computations.

sample_no

An integer specifying the maximum number of combinations to sample for higher-order computations (default: NULL for all combinations).

sample_portion

A numeric value between 0 and 1 indicating the proportion of combinations to sample for higher-order computations (default: 1, meaning 100%).

parallel

A logical value indicating whether to enable parallel computation (default: TRUE).

n_workers

An integer specifying the number of parallel workers to use (default: one less than the number of available cores).

Value

A data.table containing the results of computations. Columns include:

site_from: The source site.
site_to: The target site(s) (NA for order = 1).
order: The computation order.
value: The computed metric value.

Examples

# Example usage with a custom metric function
metric_func = function(vec1, vec2 = NULL) {
  if (is.null(vec2)) {
    return(sum(vec1))  # Example: species richness
  } else {
    return(sum((vec1 - vec2)^2))  # Example: pairwise dissimilarity
  }
}

data = data.frame(
  site = rep(letters[1:3], each = 3),
  sp1 = c(1, 0, 2, 1, 2, 0, 0, 1, 1),
  sp2 = c(0, 1, 1, 2, 0, 0, 1, 0, 2)
)

# SPECIES RICHNESS
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
richness = function(vec_from, vec_to = NULL) {
 if (is.null(vec_to)) {
   # Handle single-site calculations (order = 1)
   return(sum(vec_from != 0, na.rm = TRUE))
 } else if (length(vec_from) > 1 && length(vec_to) > 1) {
   # Handle pairwise or higher-order comparisons
   return(abs(sum(vec_from != 0, na.rm = TRUE) - sum(vec_to != 0, na.rm = TRUE)))
 } else {
   # Invalid input case
   return(NA)
 }
}

rich_o12 = compute_orderwise(
 df = block_sp,
 func = richness,
 site_col = 'grid_id',
 sp_cols = sp_cols,
 # sample_no = 1000,
 # sample_portion = 0.5,  # Default is 1 (100%)
 order = 1:2,  # Compute for pairwise and higher-order comparisons
 parallel = TRUE,
 n_workers = 4
)
head(rich_o12)

# Example usage with a custom metric function
metric_func = function(vec1, vec2 = NULL) {
  if (is.null(vec2)) {
    return(sum(vec1))  # Example: species richness
  } else {
    return(sum((vec1 - vec2)^2))  # Example: pairwise dissimilarity
  }
}

data = data.frame(
  site = rep(letters[1:3], each = 3),
  sp1 = c(1, 0, 2, 1, 2, 0, 0, 1, 1),
  sp2 = c(0, 1, 1, 2, 0, 0, 1, 0, 2)
)

# SPECIES RICHNESS
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
richness = function(vec_from, vec_to = NULL) {
 if (is.null(vec_to)) {
   # Handle single-site calculations (order = 1)
   return(sum(vec_from != 0, na.rm = TRUE))
 } else if (length(vec_from) > 1 && length(vec_to) > 1) {
   # Handle pairwise or higher-order comparisons
   return(abs(sum(vec_from != 0, na.rm = TRUE) - sum(vec_to != 0, na.rm = TRUE)))
 } else {
   # Invalid input case
   return(NA)
 }
}

rich_o12 = compute_orderwise(
 df = block_sp,
 func = richness,
 site_col = 'grid_id',
 sp_cols = sp_cols,
 # sample_no = 1000,
 # sample_portion = 0.5,  # Default is 1 (100%)
 order = 1:2,  # Compute for pairwise and higher-order comparisons
 parallel = TRUE,
 n_workers = 4
)
head(rich_o12)

Calculate Pearson's Correlation

Description

Pearson's correlation for linear abundance relationships; aggregated across sites.

Usage

cor_pears(vec_from, vec_to)
cor_pears(vec_from, vec_to)

Arguments

vec_from

A numeric vector representing species abundances at the first site.

vec_to

A numeric vector representing species abundances at the second site.

Value

Pearson's correlation coefficient.

Calculate Spearman's Rank Correlation

Description

Spearman's rank correlation for abundances; pairwise and averaged multi-site.

Usage

cor_spear(vec_from, vec_to)
cor_spear(vec_from, vec_to)

Arguments

vec_from

A numeric vector representing species abundances at the first site.

vec_to

A numeric vector representing species abundances at the second site.

Value

Spearman's rank correlation coefficient.

Calculate Bray-Curtis Dissimilarity

Description

Bray-Curtis dissimilarity on abundances; 0-1 for pairwise, averages or totals for multi-site.

Usage

diss_bcurt(vec_from, vec_to)
diss_bcurt(vec_from, vec_to)

Arguments

vec_from

A numeric vector representing species abundances at the first site.

vec_to

A numeric vector for species abundances at the second site.

Value

The Bray-Curtis dissimilarity value.

dissmapr: Workflow for Compositional Dissimilarity & Biodiversity Turnover Analysis

Description

dissmapr is an R package for analysing compositional dissimilarity and biodiversity turnover across spatial gradients.

Format Biodiversity Records to Long / Wide

Description

Converts a table of biodiversity observations between long and wide layouts while standardising key column names.

Long format - one row per observation with columns site_id, x, y, species, value (+ optional extra_cols).
Wide format - one row per site with species as individual columns.

Usage

format_df(
  data,
  format = NULL,
  x_col = NULL,
  y_col = NULL,
  site_id_col = NULL,
  species_col = NULL,
  value_col = NULL,
  sp_col_range = NULL,
  extra_cols = NULL
)
format_df(
  data,
  format = NULL,
  x_col = NULL,
  y_col = NULL,
  site_id_col = NULL,
  species_col = NULL,
  value_col = NULL,
  sp_col_range = NULL,
  extra_cols = NULL
)

Arguments

data

A data frame containing biodiversity records.

format

Character; target layout "long" or "wide". If NULL the format is inferred automatically.

x_col, y_col

Character. Names of the longitude (x) and latitude (y) columns. If NULL, common alternatives are searched.

site_id_col

Character. Column giving a unique site identifier. If NULL, a new site_id is generated from the coordinate pair.

species_col

Character. Column containing species names (required for format = "long").

value_col

Character. Column with numeric values such as presence/absence (0/1) or abundance. If NULL, each record is assigned a value of 1.

sp_col_range

Integer vector giving the index of species columns when format = "wide". If NULL all non-coordinate / non-metadata columns are treated as species.

extra_cols

Character vector of additional columns to carry through to the output (e.g. sampling metadata or environmental covariates).

Details

If column names are not supplied, the function attempts to detect common variants (e.g. "lon", "longitude" for x). When converting long -> wide, duplicate observations of the same species at a site are aggregated by summing value. When converting wide -> long, species columns are inferred either from sp_col_range or by excluding coordinate / metadata columns.

Value

A named list with up to two elements

site_obs - a long-format data frame (returned only when format = "long").
site_spp - a wide site x species data frame.

Dependencies

Relies on dplyr, tidyr, and rlang (loaded with requireNamespace()).

Examples

## --- Example 1: long  ->  wide --------------------------------------------
ex_long <- data.frame(
  lon     = c(23.10, 23.10, 23.25, 23.25),
  lat     = c(-34.00, -34.00, -34.05, -34.05),
  species = c("sp1",  "sp2",  "sp1",  "sp3"),
  count   = c(1, 2, 3, 1)
)

out_long <- format_df(
  data        = ex_long,
  format      = "long",
  x_col       = "lon",
  y_col       = "lat",
  species_col = "species",
  value_col   = "count"
)

head(out_long$site_spp)

## --- Example 2: wide ->  long --------------------------------------------
ex_wide <- out_long$site_spp

out_wide <- format_df(
  data   = ex_wide,
  format = "wide"
)

head(out_wide$site_spp)

## --- Example 1: long  ->  wide --------------------------------------------
ex_long <- data.frame(
  lon     = c(23.10, 23.10, 23.25, 23.25),
  lat     = c(-34.00, -34.00, -34.05, -34.05),
  species = c("sp1",  "sp2",  "sp1",  "sp3"),
  count   = c(1, 2, 3, 1)
)

out_long <- format_df(
  data        = ex_long,
  format      = "long",
  x_col       = "lon",
  y_col       = "lat",
  species_col = "species",
  value_col   = "count"
)

head(out_long$site_spp)

## --- Example 2: wide ->  long --------------------------------------------
ex_wide <- out_long$site_spp

out_wide <- format_df(
  data   = ex_wide,
  format = "wide"
)

head(out_wide$site_spp)

Generate Spatial Grid and Gridded Summaries

Description

Builds a regular lattice over a point-occurrence data set, assigns every record a grid_id, and returns grid-level summaries in both abundance and presence/absence formats.

Usage

generate_grid(
  data,
  x_col = "x",
  y_col = "y",
  grid_size = 0.5,
  sum_cols = NULL,
  extra_cols = NULL,
  crs_epsg = 4326,
  unit = c("deg", "min", "sec", "m")
)
generate_grid(
  data,
  x_col = "x",
  y_col = "y",
  grid_size = 0.5,
  sum_cols = NULL,
  extra_cols = NULL,
  crs_epsg = 4326,
  unit = c("deg", "min", "sec", "m")
)

Arguments

data

Data frame of points with x-y coordinates.

x_col

y_col Column names for longitude and latitude.

grid_size

Cell size (degrees or metres, depending on CRS).

sum_cols

Character or numeric vector of columns to aggregate. Note: Numeric indices are converted to names internally.

extra_cols

Additional columns to keep (optional).

crs_epsg

EPSG code of the coordinate reference system.

unit

One of "deg", "min", "sec", or "m".

Details

The function:

Tiles the study extent with square cells of size grid_size.
Computes cell centroids and, for geographic CRS data, optional mapsheet codes (1:250 000 "BB" series).
Aggregates user-specified columns (sum_cols) per cell, producing
- grid_spp - counts / abundances
- grid_spp_pa - binary 0 / 1 table for dissimilarity analyses.
Calculates helper metrics (obs_sum, spp_rich) for each cell.
Rasterises key layers (grid_id, obs_sum, spp_rich) and preserves any extra metadata supplied via extra_cols.

Value

A list containing

grid_r - multi-layer SpatRaster
grid_sf - polygon lattice with centroids & metrics
grid_spp - abundance summary (data.frame)
grid_spp_pa - presence/absence summary (data.frame)

Examples

set.seed(123)
data = data.frame(
  x = runif(100, -10, 10),
  y = runif(100, -10, 10),
  species1 = rpois(100, 5),
  species2 = rpois(100, 3),
  recordedBy = sample(LETTERS, 100, replace = TRUE)
)
grid_result = generate_grid(data, x_col = "x", y_col = "y",
                             grid_size = 1, sum_cols = 3:4,
                             extra_cols = c("recordedBy"))
print(grid_result$block_sp)
plot(grid_result$grid_sf["grid_id"])
set.seed(123)
data = data.frame(
  x = runif(100, -10, 10),
  y = runif(100, -10, 10),
  species1 = rpois(100, 5),
  species2 = rpois(100, 3),
  recordedBy = sample(LETTERS, 100, replace = TRUE)
)
grid_result = generate_grid(data, x_col = "x", y_col = "y",
                             grid_size = 1, sum_cols = 3:4,
                             extra_cols = c("recordedBy"))
print(grid_result$block_sp)
plot(grid_result$grid_sf["grid_id"])

Calculate geographic distance via Haversine formula (helper)

Description

Computes the geographic (Haversine) distance in meters between two coordinate pairs, or returns 0 for single-site (order=1) calculations when vec_to is NULL.

Usage

geodist_helper(
  vec_from,
  vec_to = NULL,
  coord_cols = c("centroid_lon", "centroid_lat")
)
geodist_helper(
  vec_from,
  vec_to = NULL,
  coord_cols = c("centroid_lon", "centroid_lat")
)

Arguments

vec_from

Numeric vector of length 2, or a named vector containing coordinates. If named, should include names given by coord_cols.

vec_to

Optional numeric vector of length 2 (destination coordinates); if NULL (default), the function returns 0.

coord_cols

Character vector of length 2 giving the names of the longitude and latitude elements in vec_from/vec_to when those are named vectors. Defaults to c("x", "y").

Value

Numeric distance in meters between the two points, or 0 if vec_to is NULL.

Retrieve, crop, resample, and link environmental rasters to sampling sites

Description

get_enviro_data() automates six steps:

AOI build - buffers the convex hull of all input points.
Raster acquisition - downloads or loads a multi-layer stack (WorldClim, SoilGrids, footprint, population, or user-supplied).
Cropping - trims the stack to the buffered AOI.
Optional resampling - resamples the cropped stack to a user-defined grid resolution.
Extraction & gap fill - pulls raster values at each site and linearly interpolates isolated NAs.
Assembly - returns a tidy site x environment table plus the cropped (and resampled) raster and an sf layer of sites.

Usage

get_enviro_data(
  data,
  buffer_km = 10,
  source = "geodata",
  var = "bio",
  res = 2.5,
  grid_r = NULL,
  path = "data/",
  year = NULL,
  depth = NULL,
  stat = "mean",
  model = NULL,
  ssp = NULL,
  time = NULL,
  sp_cols = NULL,
  ext_cols = NULL
)
get_enviro_data(
  data,
  buffer_km = 10,
  source = "geodata",
  var = "bio",
  res = 2.5,
  grid_r = NULL,
  path = "data/",
  year = NULL,
  depth = NULL,
  stat = "mean",
  model = NULL,
  ssp = NULL,
  time = NULL,
  sp_cols = NULL,
  ext_cols = NULL
)

Arguments

data

Data frame of spatial points; must include lon/lat columns such as ⁠"x","y"⁠ or ⁠"centroid_lon","centroid_lat"⁠.

buffer_km

Buffer width (km) for the AOI. Default 10.

source

"geodata" (default) to fetch layers via geodata or "local" to supply a local SpatRaster or file path.

var

Raster product to download (see details) or ignored when source = "local".

res

Resolution (arc-min) for WorldClim/WorldPop layers (geodata).

grid_r

Optional grid resolution (in same CRS units) to which the cropped raster is resampled before extraction. If NULL, no resampling is performed.

path

Cache folder for downloaded rasters (created if absent).

year

model, ssp, time Optional arguments for time-stamped products (human footprint, population, CMIP6, etc.).

depth

stat Arguments passed to geodata::soil_world().

sp_cols

Columns to drop from the final table (e.g. a large species matrix). Accepts names or numeric indices relative to data.

ext_cols

Columns to append verbatim (e.g. ⁠"obs_sum","spp_rich"⁠).

Value

A list with

env_rast SpatRaster - cropped (and optionally resampled) environmental stack
sites_sf sf POINT layer (WGS-84) of the input sites
env_df Tibble with site ID, coordinates, every raster variable, plus any columns requested in ext_cols

Import and harmonise biodiversity-occurrence data

Description

get_occurrence_data() reads occurrence records from a local CSV/file path, an in-memory data.frame, or a GBIF download (ZIP) and returns a tidy data frame in either long (one row = one record) or wide (one row = one site, one column = one species) form.

Usage

get_occurrence_data(
  data = NULL,
  source_type = c("local_csv", "data_frame", "gbif"),
  gbif_zip_url = NULL,
  download_dir = tempdir(),
  sep = ",",
  site_id_col = NULL,
  x_col = NULL,
  y_col = NULL,
  sp_name_col = NULL,
  pa_col = NULL,
  abund_col = NULL,
  species_cols = NULL
)
get_occurrence_data(
  data = NULL,
  source_type = c("local_csv", "data_frame", "gbif"),
  gbif_zip_url = NULL,
  download_dir = tempdir(),
  sep = ",",
  site_id_col = NULL,
  x_col = NULL,
  y_col = NULL,
  sp_name_col = NULL,
  pa_col = NULL,
  abund_col = NULL,
  species_cols = NULL
)

Arguments

data

File path (when source_type = "local_csv"), an in-memory data.frame ("data_frame"), or NULL ("gbif").

source_type

"local_csv", "data_frame", or "gbif".

gbif_zip_url

URL to a GBIF download ZIP (required when source_type = "gbif").

download_dir

Folder to save the ZIP/extracted file (default: tempdir()).

sep

Field separator for CSVs (default ",").

site_id_col, x_col, y_col, sp_name_col, pa_col, abund_col

Optional custom column names.

species_cols

Optional numeric or character vector specifying the species columns in a wide input (e.g. 4:11 or c("Sp1","Sp2")). Overrides the default "sp_*" detection.

Details

Column names are auto-detected from common patterns ("site_id", "x", "y", "sp_name", "pa" or "abund"). Supply ⁠*_col⁠ arguments only when your data use different names.

For wide data the helper normally looks for columns that start with "sp_". Set species_cols to a numeric range (e.g. 4:11) or a character vector of column names when the species columns do not follow the "sp_*" convention.

Value

A data.frame:

Long format: Columns site_id, x, y, sp_name, plus pa or abund.
Wide - long: Same columns after stacking the specified or auto-detected species columns.

Workflow

Read the data from source_type.
Detect / insert compulsory columns (site, coords, species, value).
Validate coordinates (-180 <= lon <= 180, -90 <= lat <= 90).
Return
- a long table (site_id, x, y, sp_name, pa|abund) when species name + value columns are present; or
- a long table reshaped from wide species columns.

Examples

# 1. Local CSV example -----------------------------------------------
tmp <- tempfile(fileext = ".csv")
df_local <- data.frame(
  site_id = 1:10,
  x = runif(10), y = runif(10),
  sp_name = c("plant1", "plant2","plant3", "plant4","plant5", "plant1", "plant2","plant3", "plant4","plant5"),
  abun = sample(0:20, size = 10, replace = TRUE)
)
write.csv(df_local, tmp, row.names = FALSE)
local_test = get_occurrence_data(data = tmp, source_type = "local_csv", sep = ",")

# 2. Existing wide-format data.frame -----------------------------------------------
df_wide <- df_local %>%
  pivot_wider(
    names_from  = sp_name,   # these become column names
    values_from = abun,  # fill these cell values
    values_fn   = sum,       # sum duplicates
    values_fill = 0          # fill missing with 0
  )
wide_test = get_occurrence_data(data = df_wide, source_type = "data_frame", species_cols = 4:11)

# 3. Custom names ----------------------------------------------------------
names(sim_dat)[1:5] <- c("plot_id", "lon", "lat", "taxon", "presence")
occ_long2 <- get_occurrence_data(
  data           = sim_dat,
  source_type    = "data_frame",
  site_id_col    = "plot_id",
  x_col          = "lon",
  y_col          = "lat",
  sp_name_col    = "taxon",
  pa_col         = "presence"
)
head(occ_long2)

# 4. GBIF download (requires internet) -----------------------------------------------
## Not run: 
gbif_test = get_occurrence_data(
  source_type   = "gbif",
  gbif_zip_url  = "https://api.gbif.org/v1/occurrence/download/request/0038969-240906103802322.zip"
)

## End(Not run)
# 1. Local CSV example -----------------------------------------------
tmp <- tempfile(fileext = ".csv")
df_local <- data.frame(
  site_id = 1:10,
  x = runif(10), y = runif(10),
  sp_name = c("plant1", "plant2","plant3", "plant4","plant5", "plant1", "plant2","plant3", "plant4","plant5"),
  abun = sample(0:20, size = 10, replace = TRUE)
)
write.csv(df_local, tmp, row.names = FALSE)
local_test = get_occurrence_data(data = tmp, source_type = "local_csv", sep = ",")

# 2. Existing wide-format data.frame -----------------------------------------------
df_wide <- df_local %>%
  pivot_wider(
    names_from  = sp_name,   # these become column names
    values_from = abun,  # fill these cell values
    values_fn   = sum,       # sum duplicates
    values_fill = 0          # fill missing with 0
  )
wide_test = get_occurrence_data(data = df_wide, source_type = "data_frame", species_cols = 4:11)

# 3. Custom names ----------------------------------------------------------
names(sim_dat)[1:5] <- c("plot_id", "lon", "lat", "taxon", "presence")
occ_long2 <- get_occurrence_data(
  data           = sim_dat,
  source_type    = "data_frame",
  site_id_col    = "plot_id",
  x_col          = "lon",
  y_col          = "lat",
  sp_name_col    = "taxon",
  pa_col         = "presence"
)
head(occ_long2)

# 4. GBIF download (requires internet) -----------------------------------------------
## Not run: 
gbif_test = get_occurrence_data(
  source_type   = "gbif",
  gbif_zip_url  = "https://api.gbif.org/v1/occurrence/download/request/0038969-240906103802322.zip"
)

## End(Not run)

Raster-based Clustering and Interpolation of Bioregional Data

Description

map_bioreg performs clustering (k-means, PAM, hierarchical, GMM) on spatial point data, computes modal or user-specified interpolation (none, nearest-neighbour, TPS), aligns cluster labels by overlap, and returns both data tables and raster stacks.

Usage

map_bioreg(
  data,
  scale_cols,
  method = c("kmeans", "pam", "hclust", "gmm", "all"),
  k_override = NULL,
  x_col = "x",
  y_col = "y",
  interpolate = c("none", "nn", "tps", "all"),
  res = 0.5,
  crs = "EPSG:4326",
  plot = TRUE,
  bndy_fc = NULL
)
map_bioreg(
  data,
  scale_cols,
  method = c("kmeans", "pam", "hclust", "gmm", "all"),
  k_override = NULL,
  x_col = "x",
  y_col = "y",
  interpolate = c("none", "nn", "tps", "all"),
  res = 0.5,
  crs = "EPSG:4326",
  plot = TRUE,
  bndy_fc = NULL
)

Arguments

data

A single data.frame of point observations or a named list of such data.frames (e.g. different scenarios). Each data.frame must contain coordinate columns and variables to scale.

scale_cols

Character vector of column names in data to standardize before clustering.

method

Character vector of clustering methods to apply; choices of "kmeans", "pam", "hclust", "gmm", or "all".

k_override

Integer. If provided, fixes the number of clusters rather than computing via silhouette.

x_col, y_col

Strings giving the names of the longitude (x) and latitude (y) columns for spatial mapping.

interpolate

One of "none", "nn", "tps", or "all"; selects which interpolation(s) to compute.

res

Numeric resolution of output rasters (in the same units as coordinates).

crs

Coordinate reference system string for raster outputs (e.g. "EPSG:4326").

plot

Logical; if TRUE, displays spatial tile plots of clusters/modes.

bndy_fc

Optional sf or SpatVector polygon to overlay.

Details

Internally, the function standardizes scale_cols, runs requested clustering(s), computes a modal consensus label, and then aligns each algorithm's cluster numbers to the k-means reference by maximal cell overlap. Three interpolation functions (fill_none_full, fill_nn_full, fill_tps_full) generate rasters of raw and aligned labels. Progress bars display per scenario.

Value

A named list with elements:

none, nn, tps: Named lists of Terra SpatRaster stacks for each scenario, each layer labelled "algn".
table: A data.frame: original points, cluster columns, modal label, aligned labels.
plots: List of ggplot objects (invisible) when plot = TRUE.
methods: Character vector of methods actually computed.

Examples

library(terra)
# simulate a single data.frame
set.seed(42)
df = data.frame(
    centroid_lon = runif(100, 16, 33),
  centroid_lat = runif(100, -35, -22),
  pred_zetaExp = rnorm(100)
)
# single scenario clustering
out1 = map_bioreg(
    data = df,
  scale_cols = c("pred_zetaExp", "centroid_lon", "centroid_lat"),
  method = "all",
  k_override = 4,
  x_col = "centroid_lon",
  y_col = "centroid_lat",
  plot = FALSE
)

# simulate multiple scenarios
df2 = df
df2$centroid_lon = df2$centroid_lon + 5
scen_list = list(current = df, future = df2)
out2 = map_bioreg(
    data = scen_list,
  scale_cols = c("pred_zetaExp", "centroid_lon", "centroid_lat"),
  method = "kmeans",
  k_override = 3,
  x_col = "centroid_lon",
  y_col = "centroid_lat",
  plot = FALSE
)
library(terra)
# simulate a single data.frame
set.seed(42)
df = data.frame(
    centroid_lon = runif(100, 16, 33),
  centroid_lat = runif(100, -35, -22),
  pred_zetaExp = rnorm(100)
)
# single scenario clustering
out1 = map_bioreg(
    data = df,
  scale_cols = c("pred_zetaExp", "centroid_lon", "centroid_lat"),
  method = "all",
  k_override = 4,
  x_col = "centroid_lon",
  y_col = "centroid_lat",
  plot = FALSE
)

# simulate multiple scenarios
df2 = df
df2$centroid_lon = df2$centroid_lon + 5
scen_list = list(current = df, future = df2)
out2 = map_bioreg(
    data = scen_list,
  scale_cols = c("pred_zetaExp", "centroid_lon", "centroid_lat"),
  method = "kmeans",
  k_override = 3,
  x_col = "centroid_lon",
  y_col = "centroid_lat",
  plot = FALSE
)

Map Bioregional Change Metrics Between Categorical Raster Layers

Description

Calculates five complementary indices that quantify how the categorical (e.g. bioregion / cluster) label of each raster cell changes across a temporal or scenario stack of rasters:

Difference count - total number of times a cell's label differs from the first layer.
Shannon entropy - information-theoretic diversity of labels within the cell's time-series.
Stability - proportion of layers in which the label is identical to the first layer (1 = always unchanged, 0 = always different).
Transition frequency - sum of binary change maps between successive layers (how often a change occurs between any pair of neighbours).
Weighted change index - cumulative dissimilarity-weighted change where the weight is derived from the empirical frequency of transitions between all pairs of labels.

Usage

map_bioregDiff(raster_input, approach = "all")
map_bioregDiff(raster_input, approach = "all")

Arguments

raster_input

A multi-layer SpatRaster or a list of single-layer SpatRaster objects representing the same spatial extent/resolution.

approach

Character string specifying the metric to return: "difference_count", "shannon_entropy", "stability", "transition_frequency", "weighted_change_index", or "all" (default) for a five-layer stack containing every metric.

Details

The dissimilarity weights for the weighted change index are built from the observed transition table of successive layers, normalised to lie between 0 and 1 (larger = rarer transition). The function accepts either a multi-layer SpatRaster or a plain list of single-layer SpatRasters, which is internally concatenated with terra.

Value

A SpatRaster:

single-layer if approach is one of the named metrics;
five-layer (names: Difference_Count, Shannon_Entropy, Stability, Transition_Frequency, Weighted_Change_Index) if approach = "all".

Examples

## -------------------------------------------------------------
## Minimal reproducible example with three random categorical
## rasters (four classes, identical geometry)
## -------------------------------------------------------------
if (requireNamespace("terra", quietly = TRUE)) {
  set.seed(42)

  r1 <- terra::rast(nrows = 40, ncols = 40,
                    vals  = sample(1:4, 40 * 40, TRUE))
  r2 <- terra::rast(r1, vals = sample(1:4, ncell(r1), TRUE))
  r3 <- terra::rast(r1, vals = sample(1:4, ncell(r1), TRUE))

  r_stack <- terra::rast(list(r1, r2, r3))
  names(r_stack) <- paste0("t", 1:3)

  ## 1. All five metrics
  diff_all <- map_bioregDiff(r_stack, approach = "all")
  print(diff_all)

  ## 2. Just the Shannon-entropy layer
  ent <- map_bioregDiff(r_stack, approach = "shannon_entropy")
  terra::plot(ent, main = "Shannon entropy of label sequence")
}

## -------------------------------------------------------------
## Minimal reproducible example with three random categorical
## rasters (four classes, identical geometry)
## -------------------------------------------------------------
if (requireNamespace("terra", quietly = TRUE)) {
  set.seed(42)

  r1 <- terra::rast(nrows = 40, ncols = 40,
                    vals  = sample(1:4, 40 * 40, TRUE))
  r2 <- terra::rast(r1, vals = sample(1:4, ncell(r1), TRUE))
  r3 <- terra::rast(r1, vals = sample(1:4, ncell(r1), TRUE))

  r_stack <- terra::rast(list(r1, r2, r3))
  names(r_stack) <- paste0("t", 1:3)

  ## 1. All five metrics
  diff_all <- map_bioregDiff(r_stack, approach = "all")
  print(diff_all)

  ## 2. Just the Shannon-entropy layer
  ent <- map_bioregDiff(r_stack, approach = "shannon_entropy")
  terra::plot(ent, main = "Shannon entropy of label sequence")
}

Calculate Mutual Information

Description

Mutual information for dependence (linear + non-linear); pairwise or cumulative multi-site.

Usage

mutual_info(vec_from, vec_to)
mutual_info(vec_from, vec_to)

Arguments

vec_from

A numeric or categorical vector for the first variable.

vec_to

A numeric or categorical vector for the second variable.

Value

The mutual information value between the two variables.

Compute Gower dissimilarity between two site vectors

Description

Calculates the Gower dissimilarity (a bounded 0-1 measure) between two numeric vectors (e.g., species abundances) using the cluster::daisy() function. When the second vector is NULL (order 1), returns NA_real_ since a pairwise comparison is not meaningful.

Usage

orderwise_diss_gower(vec_from, vec_to = NULL)
orderwise_diss_gower(vec_from, vec_to = NULL)

Arguments

vec_from

Numeric vector representing the first site's attributes (e.g., species counts or trait values).

vec_to

Optional numeric vector for the second site; if NULL, the function returns NA_real_.

Value

A single numeric dissimilarity value between 0 and 1, or NA_real_ if vec_to is NULL.

Calculate Phi Coefficient

Description

Phi coefficient for presence-absence; -1 to +1 pairwise, summary of means/variances across combinations.

Usage

phi_coef(vec_from, vec_to)
phi_coef(vec_from, vec_to)

Arguments

vec_from

A binary presence-absence vector for the first site.

vec_to

A binary presence-absence vector for the second site.

Value

The Phi Coefficient value.

Plot facetted boxplots for all ispline basis columns

Description

Creates a multi-facet boxplot of every ispline basis column in your data, grouped by a specified order factor (e.g. zeta orders). Each spline term (columns ending in _is) gets its own facet, and uses a color-blind-friendly Viridis palette.

Usage

plot_ispline_boxplots(
  ispline_data,
  ispline_suffix = "_is",
  order_col = "zOrder",
  palette = "viridis",
  direction = -1,
  ncol = 3,
  outlier_size = 0.5
)
plot_ispline_boxplots(
  ispline_data,
  ispline_suffix = "_is",
  order_col = "zOrder",
  palette = "viridis",
  direction = -1,
  ncol = 3,
  outlier_size = 0.5
)

Arguments

ispline_data

A data frame as returned by run_ispline_models, containing raw covariates, their spline bases (suffixed _is), and an order column.

ispline_suffix

A string suffix identifying your spline columns. Default is “_is”.

order_col

The name of the grouping column (e.g. “zOrder”).

palette

One of the Viridis options (“viridis”, “magma”, “plasma”, “cividis”, etc.). Default “viridis”.

direction

Integer 1 or -1 to control palette direction. Default -1 (reversed).

ncol

Number of columns in the facet wrap. Default 3.

outlier_size

Size of the outlier points. Default 0.5.

Details

Automatically detects all columns whose names end with ispline_suffix.
Pivots the data to long format for ggplot2.
Facets a boxplot for each spline variable with independent scales.

Value

A ggplot2 object showing one boxplot per spline term.

Examples

## Not run: 
# load example data
data(bird.spec.fine)
data(bird.env.fine)

# prepare inputs
xy.bird    <- bird.spec.fine[,1:2]
spp.bird   <- bird.spec.fine[,3:102]
envir.bird <- bird.env.fine[,3:9]

# Fit & gather ispline tables
ispline_tabs_all <- run_ispline_models(
  spp_df    = spp.bird,
  env_df    = envir.bird,
  xy_df     = xy.bird,
  orders    = 2:6,
  sam       = 100,
  normalize = "Jaccard",
  reg_type  = "ispline"
)

# Facetted boxplots of all *_is columns
plot_ispline_boxplots(
  ispline_data   = ispline_tabs_all,
  ispline_suffix = "_is",
  order_col      = "zOrder",
  palette        = "viridis",
  direction      = -1,
  ncol           = 3
)

## End(Not run)

## Not run: 
# load example data
data(bird.spec.fine)
data(bird.env.fine)

# prepare inputs
xy.bird    <- bird.spec.fine[,1:2]
spp.bird   <- bird.spec.fine[,3:102]
envir.bird <- bird.env.fine[,3:9]

# Fit & gather ispline tables
ispline_tabs_all <- run_ispline_models(
  spp_df    = spp.bird,
  env_df    = envir.bird,
  xy_df     = xy.bird,
  orders    = 2:6,
  sam       = 100,
  normalize = "Jaccard",
  reg_type  = "ispline"
)

# Facetted boxplots of all *_is columns
plot_ispline_boxplots(
  ispline_data   = ispline_tabs_all,
  ispline_suffix = "_is",
  order_col      = "zOrder",
  palette        = "viridis",
  direction      = -1,
  ncol           = 3
)

## End(Not run)

Plot ispline partial effects with quantile and start-point markers

Description

Given a tidy data frame of ispline basis outputs (from run_ispline_models), this function identifies the ispline column matching a specified covariate, draws thinner ispline curves for each zOrder, and overlays:

Small symbols at user-defined quantiles of the raw covariate.
A larger symbol at each curve's starting point (minimum covariate value).

Usage

plot_ispline_lines(
  ispline_data,
  x_var,
  orders = NULL,
  cols = NULL,
  shapes = NULL,
  probs = c(0, 0.25, 0.5, 0.75, 1),
  line_size = 0.5,
  point_size = 1.5,
  start_size = 3,
  start_stroke = 0
)
plot_ispline_lines(
  ispline_data,
  x_var,
  orders = NULL,
  cols = NULL,
  shapes = NULL,
  probs = c(0, 0.25, 0.5, 0.75, 1),
  line_size = 0.5,
  point_size = 1.5,
  start_size = 3,
  start_stroke = 0
)

Arguments

ispline_data

A data frame as returned by run_ispline_models, containing raw covariates, their ⁠_is⁠ spline bases, and a zOrder column.

x_var

A string; the name of the raw covariate to plot (e.g. “dist”). The function will look for a matching spline column named <x_var>_is.

orders

Character vector of zOrder levels (in desired legend/order). Default is unique(ispline_data$zOrder).

cols

Character vector of colours, one per order. Default uses scales::hue_pal().

shapes

Integer vector of plotting symbols (pch codes), one per order. Default is 15:(...).

probs

Numeric vector of probabilities (between 0 and 1) at which to place small quantile markers. Default c(0, .25, .5, .75, 1).

line_size

Numeric; line width for the spline curves. Default 0.5.

point_size

Numeric; size of the quantile markers. Default 1.5.

start_size

Numeric; size of the big start-point markers. Default 3.

start_stroke

Numeric; stroke width for the big start markers. Default 0.

Details

Detects all columns ending in _is and fuzzy-matches x_var against them.
Extracts the raw covariate name by stripping _is.
Computes one "start" point per zOrder at the minimum raw covariate.
Computes quantile-closest points per zOrder at the user's probs.

Value

A ggplot2 object showing:

geom_line() for each curve.
geom_point() at the specified quantiles.
A larger geom_point() at each curve's minimum.

Examples

## Not run: 
# load sample data
data(bird.spec.fine)
data(bird.env.fine)

# prepare inputs
xy.bird    <- bird.spec.fine[,1:2]
spp.bird   <- bird.spec.fine[,3:102]
envir.bird <- bird.env.fine[,3:9]

# fit & gather ispline tables
ispline_tabs_all <- run_ispline_models(
  spp_df    = spp.bird,
  env_df    = envir.bird,
  xy_df     = xy.bird,
  orders    = 2:6,
  sam       = 100,
  normalize = "Jaccard",
  reg_type  = "ispline"
)

# Line plot for "dist"
plot_ispline_lines(
  ispline_data = ispline_tabs_all,
  x_var        = "dist",            # will match "dist_is"
  orders       = paste("Order", 2:6),
  cols         = c('green','cyan','purple','blue','black'),
  shapes       = c(15,16,17,18,19)
)

## End(Not run)

## Not run: 
# load sample data
data(bird.spec.fine)
data(bird.env.fine)

# prepare inputs
xy.bird    <- bird.spec.fine[,1:2]
spp.bird   <- bird.spec.fine[,3:102]
envir.bird <- bird.env.fine[,3:9]

# fit & gather ispline tables
ispline_tabs_all <- run_ispline_models(
  spp_df    = spp.bird,
  env_df    = envir.bird,
  xy_df     = xy.bird,
  orders    = 2:6,
  sam       = 100,
  normalize = "Jaccard",
  reg_type  = "ispline"
)

# Line plot for "dist"
plot_ispline_lines(
  ispline_data = ispline_tabs_all,
  x_var        = "dist",            # will match "dist_is"
  orders       = paste("Order", 2:6),
  cols         = c('green','cyan','purple','blue','black'),
  shapes       = c(15,16,17,18,19)
)

## End(Not run)

Predict Pairwise Compositional Turnover (zeta-dissimilarity) with Richness

Description

Takes raw species and environmental data, fits a multi-site GDM model output, computes predicted pairwise turnover (zeta2) across the landscape, and returns a data frame with site-level richness, environmental covariates, distance, and predicted turnover; optionally plots a heatmap of zeta2 predictions.

Usage

predict_dissim(
  grid_spp,
  species_cols,
  env_vars,
  zeta_model,
  grid_xy,
  bndy_fc = NULL,
  x_col = "x",
  y_col = "y",
  show_plot = TRUE,
  skip_scale = FALSE
)
predict_dissim(
  grid_spp,
  species_cols,
  env_vars,
  zeta_model,
  grid_xy,
  bndy_fc = NULL,
  x_col = "x",
  y_col = "y",
  show_plot = TRUE,
  skip_scale = FALSE
)

Arguments

grid_spp

Data frame containing site IDs, coordinates, and species presence-absence/abundance columns.

species_cols

Integer or character vector giving the columns of grid_spp that hold species data.

env_vars

Data frame of raw environmental predictors (unscaled; rows must align with grid_spp).

zeta_model

Fitted object from Zeta.msgdm (order = 2, reg.type = "ispline").

grid_xy

Data frame of site coordinates, same row-order as grid_spp, with columns x_col, y_col.

bndy_fc

Optional sf or SpatVector polygon to overlay.

x_col

Name of the x (longitude) column in grid_spp/grid_xy.

y_col

Name of the y (latitude) column.

show_plot

Logical; if TRUE (default), print the turnover heatmap.

Value

A data frame with one row per site, containing:

richness: Species richness (sum across species_cols).
distance: Mean great-circle distance (km) from each site to all others.
<env_vars>: All scaled environmental predictors.
pred_zeta: Linear predictor (logit scale) from Predict.msgdm().
pred_zetaExp: Predicted turnover (0-1 scale).
log_pred_zetaExp: Natural log of pred_zetaExp.
x_col, y_col: Site coordinates (from grid_xy).

Examples

## Not run: 
result <- predict_dissim(
  grid_spp     = bird.spec.fine,
  species_cols = 3:102,
  env_vars     = bird.env.fine[,3:9],
  zeta_model   = z_mod,
  grid_xy      = bird.spec.fine[,1:2],
  bndy_fc      = rsa,
  x_col        = "x",
  y_col        = "y",
  show_plot    = FALSE
)

## End(Not run)
## Not run: 
result <- predict_dissim(
  grid_spp     = bird.spec.fine,
  species_cols = 3:102,
  env_vars     = bird.env.fine[,3:9],
  zeta_model   = z_mod,
  grid_xy      = bird.spec.fine[,1:2],
  bndy_fc      = rsa,
  x_col        = "x",
  y_col        = "y",
  show_plot    = FALSE
)

## End(Not run)

Calculate Species Richness

Description

Counts unique species per site; reports absolute differences for pairwise and summary stats (range, variance, mean) for multi-site.

Usage

richness(vec_from, vec_to = NULL)
richness(vec_from, vec_to = NULL)

Arguments

vec_from

A numeric vector representing species counts at the first site.

vec_to

(Optional) A numeric vector for pairwise or higher-order comparisons.

Value

The species richness value.

Remove Highly Correlated Predictors

Description

Detects pairs (or groups) of strongly collinear predictors and eliminates the minimum subset necessary to keep every absolute pairwise correlation below a user-defined threshold. Correlations are computed with stats::cor(); the variables to discard are chosen via the caret::findCorrelation() algorithm. An optional heat-map of the correlation matrix is produced with corrplot for rapid inspection.

Usage

rm_correlated(data, cols = NULL, threshold = 0.7, plot = TRUE)
rm_correlated(data, cols = NULL, threshold = 0.7, plot = TRUE)

Arguments

data

A data frame with candidate predictor variables.

cols

Optional numeric or character vector specifying columns to consider; defaults to all numeric columns.

threshold

Numeric in $[0,1]$ specifying the absolute correlation cut-off (default 0.7).

plot

Logical; if TRUE (default) a correlation heat-map with coefficients is drawn.

Details

Non-numeric columns are silently dropped prior to correlation calculation.
When cols is supplied (numeric or character), only those columns are tested; otherwise all numeric columns in data are used.
The names of removed and retained variables are printed to the console for transparency.

Value

A data frame containing the original rows but only the subset of predictor columns whose absolute pairwise correlations are $<$ threshold.

Examples

set.seed(99)
n  <- 200
df <- data.frame(
  a = rnorm(n),
  b = rnorm(n),
  c = rnorm(n) * 0.8 + rnorm(n) * 0.2,  # moderately corr. with 'a'
  d = rnorm(n) * 0.9 + rnorm(n) * 0.1   # moderately corr. with 'b'
)

## Remove predictors with r >= 0.75
df_reduced <- rm_correlated(df, threshold = 0.75, plot = FALSE)
names(df_reduced)

## Visualise the correlation structure & removals at a stricter threshold
rm_correlated(df, threshold = 0.6, plot = TRUE)

set.seed(99)
n  <- 200
df <- data.frame(
  a = rnorm(n),
  b = rnorm(n),
  c = rnorm(n) * 0.8 + rnorm(n) * 0.2,  # moderately corr. with 'a'
  d = rnorm(n) * 0.9 + rnorm(n) * 0.1   # moderately corr. with 'b'
)

## Remove predictors with r >= 0.75
df_reduced <- rm_correlated(df, threshold = 0.75, plot = FALSE)
names(df_reduced)

## Visualise the correlation structure & removals at a stricter threshold
rm_correlated(df, threshold = 0.6, plot = TRUE)

Run multiple Zeta.msgdm ispline models and return both models and combined ispline table

Description

Fits Zeta.msgdm models of type “ispline” for a series of zeta-orders, extracts the raw environmental covariates (plus distance) and their ispline bases, and returns both the list of fitted models and one tidy data frame combining all orders.

Usage

run_ispline_models(
  spp_df,
  env_df,
  xy_df,
  orders = 2:6,
  sam = 100,
  distance.type = "Euclidean",
  normalize = "Jaccard",
  reg_type = "ispline"
)
run_ispline_models(
  spp_df,
  env_df,
  xy_df,
  orders = 2:6,
  sam = 100,
  distance.type = "Euclidean",
  normalize = "Jaccard",
  reg_type = "ispline"
)

Arguments

spp_df

A data frame or matrix of species incidence/abundance.

env_df

A data frame of environmental covariates.

xy_df

A two-column data frame or matrix of site coordinates.

orders

Integer vector of zeta orders to fit (e.g. 2:6).

sam

Integer; number of random samples per order (passed to Zeta.msgdm).

distance.type

Character; distance metric for Zeta.msgdm (default “Euclidean”).

normalize

Character; normalization method for Zeta.msgdm (default “Jaccard”).

reg_type

Character; regression type for Zeta.msgdm (default “ispline”).

Value

A named list with:

zeta_gdm_list: A list of the fitted Zeta.msgdm() objects, named by order.
ispline_table: A tibble with one row per sample, containing all original covariates (including distance), the ispline bases suffixed _is, and a zOrder column.

Examples

## Not run: 
data(bird.spec.fine); data(bird.env.fine)
xy   <- bird.spec.fine[,1:2]
spp  <- bird.spec.fine[,3:102]
env  <- bird.env.fine[,3:9]

out <- run_ispline_models(
  spp_df        = spp,
  env_df        = env,
  xy_df         = xy,
  orders        = 2:6,
  sam           = 100,
  normalize     = "Jaccard",
  reg_type      = "ispline"
)
names(out)
head(out$ispline_table)

## End(Not run)

## Not run: 
data(bird.spec.fine); data(bird.env.fine)
xy   <- bird.spec.fine[,1:2]
spp  <- bird.spec.fine[,3:102]
env  <- bird.env.fine[,3:9]

out <- run_ispline_models(
  spp_df        = spp,
  env_df        = env,
  xy_df         = xy,
  orders        = 2:6,
  sam           = 100,
  normalize     = "Jaccard",
  reg_type      = "ispline"
)
names(out)
head(out$ispline_table)

## End(Not run)

Calculate Species Turnover or Beta Diversity

Description

Computes beta diversity as proportion unshared; 0-1 for pairwise, extended to multi-site averages or totals.

Usage

turnover(vec_from, vec_to = NULL)
turnover(vec_from, vec_to = NULL)

Arguments

vec_from

A numeric vector representing species counts at the first site.

vec_to

(Optional) A numeric vector for pairwise or higher-order comparisons.

Value

The species turnover value.

Package 'dissmapr'

Help Index

Calculate Species Abundance

Description

Usage

Arguments

Value

Add Nearest Mapsheet Code and Center Coordinates

Description

Usage

Arguments

Details

Value

Examples

Compute Order-wise Metrics

Description

Usage

Arguments

Value

Examples

Calculate Pearson's Correlation

Description

Usage

Arguments

Value

Calculate Spearman's Rank Correlation

Description

Usage

Arguments

Value

Calculate Bray-Curtis Dissimilarity

Description

Usage

Arguments

Value

dissmapr: Workflow for Compositional Dissimilarity & Biodiversity Turnover Analysis

Description

Format Biodiversity Records to Long / Wide

Description

Usage

Arguments

Details

Value

Dependencies

See Also

Examples

Generate Spatial Grid and Gridded Summaries

Description

Usage

Arguments

Details

Value

Examples

Calculate geographic distance via Haversine formula (helper)

Description

Usage

Arguments

Value

Retrieve, crop, resample, and link environmental rasters to sampling sites

Description

Usage

Arguments

Value

Import and harmonise biodiversity-occurrence data

Description

Usage

Arguments

Details

Value

Workflow

See Also

Examples

Raster-based Clustering and Interpolation of Bioregional Data

Description

Usage

Arguments

Details

Value

Examples

Map Bioregional Change Metrics Between Categorical Raster Layers