Package {spacemodR}

The 'spacemodR' package.

Description

Workflow for spatially explicit environmental risk assessment: habitat, food web, dispersal, exposure and risk.

References

Stan Development Team (2020). RStan: the R interface to Stan. R package version 2.21.2. https://mc-stan.org

Bird Functional Traits Dataset (EltonTraits 1.0)

Description

Dietary category percentages, foraging strategies, and functional traits for birds.

Usage

DBFunc_BirdFuncDat

Format

A data frame containing species dietary breakdowns (summing to 100

SpecID

Unique species identifier.

PassNonPass

Indicates whether the bird is a Passerine or Non-Passerine.

IOCOrder

IOC taxonomic order.

BLFamilyLatin

BirdLife International family Latin name.

BLFamilyEnglish

BirdLife International family English name.

BLFamSequID

BirdLife International family sequence ID.

Taxo

Taxonomic grouping code.

Scientific

Scientific name of the bird species.

English

Common English name of the bird species.

Diet.Inv

Percentage of invertebrates in the diet.

Diet.Vend

Percentage of vertebrate endotherms consumed.

Diet.Vect

Percentage of vertebrate ectotherms consumed.

Diet.Vfish

Percentage of fish consumed.

Diet.Vunk

Percentage of unknown/unclassified vertebrates in the diet.

Diet.Scav

Percentage of scavenging activity.

Diet.Fruit

Percentage of fruits consumed.

Diet.Nect

Percentage of nectar/pollen consumed.

Diet.Seed

Percentage of seeds/nuts consumed.

Diet.PlantO

Percentage of other plant tissues consumed.

Diet.5Cat

Simplified 5-category diet classification.

Diet.Source

Source of the dietary information.

Diet.Certainty

Certainty score for the diet data.

Diet.EnteredBy

Identifier of the person who entered the diet data.

ForStrat.watbelowsurf

Percentage of foraging time spent underwater.

ForStrat.wataroundsurf

Percentage of foraging time spent at the water surface.

ForStrat.ground

Percentage of foraging time spent on the ground.

ForStrat.understory

Percentage of foraging time spent in the understory.

ForStrat.midhigh

Percentage of foraging time spent in the mid-high canopy.

ForStrat.canopy

Percentage of foraging time spent in the upper canopy.

ForStrat.aerial

Percentage of foraging time spent in aerial foraging.

PelagicSpecialist

Indicator of pelagic specialization.

ForStrat.Source

Source of the foraging strategy data.

ForStrat.SpecLevel

Specificity level of the foraging strategy record.

ForStrat.EnteredBy

Identifier of the person who entered the foraging data.

Nocturnal

Indicator of nocturnal activity.

BodyMass.Value

Body mass value in grams.

BodyMass.Source

Source of the body mass information.

BodyMass.SpecLevel

Specificity level of the body mass record.

BodyMass.Comment

Notes on body mass data.

Record.Comment

General comments about the record.

Source

EltonTraits 1.0 database.

Mammal Functional Traits Dataset (EltonTraits 1.0)

Description

Dietary category percentages, foraging strategies, and functional traits for mammals.

Usage

DBFunc_MamFuncDat

Format

A data frame containing species dietary breakdowns (summing to 100

MSW3_ID

Mammal Species of the World (3rd ed.) identifier.

Scientific

Scientific name of the mammal species.

MSWFamilyLatin

Mammal Species of the World (3rd ed.) family name.

Diet.Inv

Percentage of invertebrates in the diet.

Diet.Vend

Percentage of vertebrate endotherms consumed (birds and mammals).

Diet.Vect

Percentage of vertebrate ectotherms consumed (reptiles and amphibians).

Diet.Vfish

Percentage of fish consumed.

Diet.Vunk

Percentage of unknown/unclassified vertebrates in the diet.

Diet.Scav

Percentage of scavenging activity.

Diet.Fruit

Percentage of fruits consumed.

Diet.Nect

Percentage of nectar/pollen consumed.

Diet.Seed

Percentage of seeds/nuts consumed.

Diet.PlantO

Percentage of other plant tissues consumed (leaves, stems, roots).

Diet.Source

Source of the dietary information.

Diet.Certainty

Certainty score for the diet data.

ForStrat.Value

Primary foraging stratum/habitat.

ForStrat.Certainty

Certainty score for the foraging stratum.

ForStrat.Comment

Notes on foraging strategy.

Activity.Nocturnal

Binary flag for nocturnal activity.

Activity.Crepuscular

Binary flag for crepuscular activity.

Activity.Diurnal

Binary flag for diurnal activity.

Activity.Source

Source of the activity information.

Activity.Certainty

Certainty score for the activity pattern.

BodyMass.Value

Body mass value in grams.

BodyMass.Source

Source of the body mass information.

BodyMass.SpecLevel

Specificity level of the body mass record.

Source

EltonTraits 1.0 database.

Field Metabolic Rates Database (FmrBT)

Description

A comprehensive database containing Field Metabolic Rates (FMR), body mass, and ambient temperature across more than 700 species.

Usage

FmrBT

Format

A data frame with variables detailing energetic properties and taxonomy:

Kingdom

Taxonomic kingdom.

Phylum

Taxonomic phylum.

Class

Taxonomic class.

Order

Taxonomic order.

Family

Taxonomic family.

Genus

Taxonomic genus.

SpeciesVerbatim

Original species name as recorded in the source.

SpeciesAcceptedName

Standardized accepted species name.

FMR_kJ_d

Field metabolic rate per individual in kJ/day.

Mass_g

Body mass of the individual in grams.

Temp_C

The ambient temperature recorded during the study in Celsius.

Endotherm

Indicator of thermal status (1/TRUE for endotherm, 0/FALSE for ectotherm).

Reference

Source literature reference.

Comment

Additional notes from the dataset creators.

Outlier

Flag indicating if the record is considered a statistical outlier.

Source

De Castro et al. (2025) FmrBT database.

Eco-SSL Toxicity Data for Multiple Taxonomic Groups

Description

A comprehensive dataset combining toxicity values used for the derivation of Ecological Soil Screening Levels (Eco-SSL). It includes ecotoxicological data (NOAEL and LOAEL) for mammals, birds, invertebrates, and plants exposed to various chemical compounds.

Usage

SSD_ecoSSL_all

Format

A tibble (data frame) with 9 variables:

ERE

Character. Abbreviation for the Ecological Receptor Endpoint or effect category (e.g., "BIO" for Biochemical, "BEH" for Behavior).

order_tox_value

Integer. The sequential order or index of the toxicity value within its specific group.

tox_value_NOAEL

Numeric. The No Observed Adverse Effect Level (NOAEL), typically expressed in mg/kg/day or mg/kg soil.

tox_value_LOAEL

Numeric. The Lowest Observed Adverse Effect Level (LOAEL), typically expressed in mg/kg/day or mg/kg soil.

compound

Character. The chemical compound or trace element evaluated (e.g., "Antimony", "Cadmium").

species_group

Character. The broad taxonomic or functional group of the tested species (e.g., "Mammalian Wildlife").

test_organism

Character. The common and/or scientific name of the specific organism tested (e.g., "Rat (Rattus norvegicus)").

tox_value

Numeric. The primary toxicity value retained for the assessment (usually corresponds to the NOAEL or a derived threshold).

ERE_full

Character. The full, unabbreviated name of the endpoint or effect category (e.g., "Biochemical", "Behavior").

Source

Derived from the United States Environmental Protection Agency (US EPA) Ecological Soil Screening Levels (Eco-SSL) database and documentation.

Examples

data(SSD_ecoSSL_all)
head(SSD_ecoSSL_all)
table(SSD_ecoSSL_all$species_group)

Subset method for trophic_tbl

Description

Ensures that any subsetting or modification preserves the validity of the trophic table.

Usage

## S3 method for class 'trophic_tbl'
x[...]

Arguments

Value

A validated trophic_tbl object.

Add a Flux Rule

Description

Adds a specific flux rule to a trophic link or target in a trophic table.

Usage

add_flux(tbl, from = NULL, to, value)

Arguments

Value

The updated 'trophic_tbl'.

Add habitat zones to a Habitat object

Description

Add habitat zones to a Habitat object

Usage

add_habitat(hab, sf_data, weight = 1, priority = NULL, touches = TRUE, ...)

Arguments

Details

touches controls which pixels a feature occupies, not how hard it is to cross: the permeability of a structure is carried by its weight (or by add_nohabitat for an absolute barrier).

• touches = TRUE keeps thin structures (hedgerows, rivers, roads, whether drawn as lines or as narrow polygons) continuous: every pixel they cross is filled, so they cannot vanish between pixel centres nor be crossed diagonally between two pixels. The price is a footprint larger than the feature: it grows by up to one pixel in width.

• touches = FALSE keeps areas unbiased, which is preferable for a layer made of contiguous polygons covering the whole landscape (a land cover map): with touches = TRUE, each polygon would spread on the pixels of its neighbours and the border pixels would go to the feature on top of the stack.

A common set-up is therefore a land cover layer added with touches = FALSE and a low priority, with the thin structures that must stay continuous added on top with touches = TRUE.

Value

A Habitat object with new geometries added as habitat

Examples

library(sf)
square <- function(a, b) {
  st_sf(geometry = st_sfc(st_polygon(list(
    cbind(c(a, b, b, a, a), c(a, a, b, b, a))))))
}
hedge <- st_sf(geometry = st_sfc(st_linestring(cbind(c(0, 10), c(0, 10)))))

# the hedge is declared first but kept on top of the field with `priority`
hab <- habitat() |>
  add_habitat(hedge, weight = 1, priority = 10) |>
  add_habitat(square(2, 8), weight = 0.2, priority = 1)
hab

Add links to a trophic table

Description

Adds one or several directed links to a trophic_tbl object.

Usage

add_link(tbl, from, to, weight = 1)

Arguments

Details

The function performs several checks:

• from must be a scalar character string

• to must be a character vector

• Links must be unique

• Self-loops (from == to) are forbidden

• The resulting graph must remain acyclic

Value

A validated trophic_tbl object with the new links added.

Examples

net <- trophic() |>
  add_link("a", "b", weight = 1)

Add non-habitat zones to a Habitat object

Description

Add non-habitat zones to a Habitat object

Usage

add_nohabitat(hab, sf_data, priority = NULL, touches = TRUE, ...)

Arguments

Value

A Habitat object with new geometries added as non-habitat

Apply trophic flux from a resource layer

Description

Computes the contribution of a resource raster to a consumer layer using the normalized weight and a flux function stored in the trophic table.

Usage

apply_flux(raster, fluxes, from, to)

Arguments

Details

The function extracts the link corresponding to from -> to from the trophic table and applies:

1. the associated flux function to the raster values

2. the normalized weight of the link

An error is thrown if no such link exists.

Value

A raster object with transformed values.

Data concentration Cd Soil - Vegetation extracted from BAPPET

Description

Data concentration Cd Soil - Vegetation extracted from BAPPET

Usage

data(bappet_cd)

Format

An object of class data.frame with 3040 rows and 80 columns.

Examples

data(bappet_cd)

Calibrate direct transfer model

Description

Fits a Bayesian direct transfer model using Stan. The model handles continuous and categorical explanatory variables to predict a response variable.

Usage

calibrate_direct(data, ...)

Arguments

Value

A stanfit object containing the posterior samples.

Calibrate simple transfer model

Description

Fits a Bayesian simple transfer model using Stan. The model handles continuous and categorical explanatory variables to predict a response variable.

Usage

calibrate_simple(data, ...)

Arguments

Value

A stanfit object containing the posterior samples.

Calibrate trophic transfer model

Description

Fits a Bayesian trophic transfer model using Stan. This model calibrates relationships across multiple trophic compartments.

Usage

calibrate_trophic(data, ...)

Arguments

Value

A stanfit object containing the posterior samples.

Compute dispersal or spread map (Generic Engine)

Description

Low-level function to apply spatial processing (Convolution or External algorithms) to a raster. It handles NA masking and method dispatch to Julia if necessary.

Usage

compute_dispersal(x, method = "convolution", options = list(), mask = NULL)

Arguments

Value

A SpatRaster object containing the dispersed values.

Create a 2D Gaussian motion kernel as a SpatRaster

Description

Create a 2D Gaussian motion kernel as a SpatRaster

Usage

compute_kernel(radius, GSD, size_std = 1.5)

Arguments

Value

A matrix defining the kernel

Compute trophic levels of nodes

Description

Determines the trophic level of each node in an acyclic directed graph.

Usage

compute_levels(edges)

Arguments

Value

A named numeric vector giving the trophic level of each node.

Cross-validate a soil interpolation method

Description

Estimates the prediction error of an interpolation method by k-fold cross-validation: the points are split in 'nfold' groups; each group is in turn removed, the model is re-fitted (including the variogram) on the remaining points and used to predict the removed ones.

With 'block_size', folds are made of square spatial blocks instead of random points (*spatial cross-validation*). This is more demanding: it mimics the prediction of areas located far from any sample, where interpolators that simply copy neighbours are penalised.

Usage

cv_soil(
  points,
  value,
  method = c("kriging", "kernel"),
  covariates = NULL,
  model = "Exp",
  log10 = TRUE,
  nfold = 10,
  block_size = NULL,
  seed = 1,
  radius = 200,
  size_std = 3,
  kappa = 0.5,
  cutoff = NULL,
  width = NULL
)

Arguments

Value

A 'data.frame' of class 'soil_cv' with the observed value ('obs'), the prediction ('pred') and the 'fold' of each point (on the log10 scale if 'log10 = TRUE'). Use [summary()] on it to get RMSE, MAE, bias and R2.

See Also

[krige_soil()], [kernel_smooth_soil()]

Examples

data(soil_metaleurop)
cv <- cv_soil(soil_metaleurop, "cd", model = "Exp")
summary(cv)

Disperse a species or variable over the landscape

Description

Applies a dispersal mechanism to a specific layer of the 'spacemodel' object. This function acts as a wrapper around compute_dispersal to handle the 'spacemodel' class structure.

Usage

dispersal(
  spacemodel,
  layer = 1,
  method = "convolution",
  method_option = list()
)

Arguments

Value

The spacemodel object with the specified layer updated with dispersed values.

See Also

compute_dispersal

Examples

## Not run: 
# 1. Convolution example
my_kernel <- matrix(1, nrow=3, ncol=3)
sm_updated <- dispersal(sm, layer = "Predator", method = "convolution",
                        method_option = list(kernel = my_kernel))

# 2. Omniscape example (requires Julia)
sm_updated <- dispersal(sm, layer = "Predator", method = "omniscape",
                        method_option = list(resistance = res_map, radius = 10))

## End(Not run)

Data concentration Cd Soil - Earthworm

Description

Data concentration Cd Soil - Earthworm

Usage

data(earthworm_cd)

Format

An object of class data.frame with 91 rows and 4 columns.

Examples

data(earthworm_cd)

Species habitat dictionary for EUNIS Level 2 land cover classes

Description

Habitat suitability, breeding, foraging and movement scores of 935 European terrestrial vertebrates (amphibians, reptiles, birds, mammals) for each of the 45 EUNIS Level 2 classes of the EEA Ecosystem Type Map v3.1 (terrestrial part), as returned by get_eunis_data with level = "L2". It is the EUNIS counterpart of ocsge_species_dict, with an improved schema: scores follow an anchored rubric, breeding is separated from foraging, and resistance is derived from permeability on the 1-100 log scale expected by connectivity models such as Omniscape.

Usage

data(eunis_species_dict)

Format

A data frame with 42,075 rows (935 species x 45 EUNIS classes) and 13 columns:

code_eunis

Character. EUNIS Level 2 code (e.g. "G1"); matches ref_eunis$code for level == "L2".

species_name

Character. Scientific name, genus and species separated by "_".

sppID

Character. Species identifier from the EEA source table; the first letter gives the group (A, R, B, M).

taxon_group

Character. "amphibian", "reptile", "bird" or "mammal".

suitability

Integer (0-10). Overall habitat suitability: 0 unused or avoided, 2 marginal, 5 secondary, 8 main, 10 core habitat.

weight_breeding

Integer (0-10). Value of the class as a breeding, nesting, denning, roosting or larval site.

weight_foraging

Integer (0-10). Value of the class for feeding.

permeability

Integer (0-10). Ease of movement through the class: 10 moves freely, 7 readily, 4 reluctantly, 1 strong barrier, 0 impassable. Accounts for flight, swimming or fossorial habits.

resistance

Numeric (1-100). Derived from permeability as 10^(2 * (1 - permeability / 10)): 10 gives 1, 5 gives 10, 0 gives 100.

habitat_breadth

Integer. Species-level: number of EUNIS classes with suitability >= 5.

specialization

Numeric (0-1). Species-level: 1 minus the normalised Shannon evenness of the suitability profile (0 generalist, 1 specialist).

confidence

Integer (1-3). Species-level: 1 vague or missing source description, 2 medium, 3 detailed.

in_france

Integer (0/1). Species-level: 1 if the species occurs natively and regularly in metropolitan France (expert knowledge).

Details

Scores were assigned by a large language model (Claude Opus 5.5) reading the EEA habitat and avoided-habitat descriptions of each species (raw_data/spp_EEA_habitats_cleaned.csv) against short ecological descriptions of each EUNIS class. They are expert-judgement scores and have not been validated against occurrence data. Four species had an empty description and were scored from general knowledge (confidence = 1). The scoring files, the comparison with ocsge_species_dict and the build scripts are in raw_data/opus_scoring/ of the source repository.

See Also

get_eunis_data, ref_eunis, ocsge_species_dict

Examples

data(eunis_species_dict)
subset(eunis_species_dict, species_name == "Apodemus_sylvaticus")

Fit a variogram model to an empirical soil variogram

Description

Fits one or several theoretical variogram models to an empirical semi-variogram (from [soil_variogram()]) by weighted least squares, using the weights N_j / h_j^2 (number of pairs over squared distance), which give more importance to the short distances that matter most for kriging (same default as 'gstat::fit.variogram(fit.method = 7)').

Available models (the 'range' parameter has the same meaning as in gstat):

• '"Exp"': exponential, \gamma(h) = c_0 + c (1 - e^{-h/a});

• '"Sph"': spherical, reaches the sill exactly at h = a;

• '"Gau"': Gaussian, very smooth at the origin;

• '"Mat"': Matérn with smoothness 'kappa' ('kappa = 0.5' is '"Exp"').

Usage

fit_soil_variogram(variogram, model = "Exp", kappa = 0.5)

Arguments

Value

An object of class 'soil_vgm': a list with 'model', 'nugget', 'psill', 'range', 'kappa', 'sse' (weighted sum of squared errors), 'empirical' (the input variogram) and 'comparison' (a table of all fitted models).

See Also

[soil_variogram()], [krige_soil()]

Examples

data(soil_metaleurop)
v <- soil_variogram(soil_metaleurop, "cd")
m <- fit_soil_variogram(v, model = c("Exp", "Sph", "Gau"))
m
m$comparison

Constructor for Flux Parameters

Description

Initializes the trophic flux table for a spacemodel.

Usage

flux(x, default = 1, normalize = TRUE)

Arguments

Value

A 'trophic_tbl' with an initialized 'flux' column.

Identify codes of departments intersecting a region of interest

Description

This function takes a region of interest (ROI) as an 'sf' or 'sfc' object and returns the codes of departments intersecting this region.

Usage

get_departements_for_roi(roi)

Arguments

Value

A character vector containing the codes of departments intersecting the region of interest.

Examples

library(sf)
roi <- sf::st_as_sfc(sf::st_bbox(
  c(xmin = 600000, ymin = 6600000, xmax = 650000, ymax = 6650000),
  crs = 2154)
)
departments <- get_departements_for_roi(roi)

Efficiently retrieve EUNIS land cover data from the EEA Ecosystem Type Map

Description

This function retrieves EUNIS (European Nature Information System) land cover data for a Region of Interest (ROI), as an alternative to the French OCS-GE nomenclature used by get_ocsge_data_fgb. It queries the European Environment Agency (EEA) "Ecosystem Type Map v3.1, terrestrial part" ArcGIS MapServer, a 100 m resolution, EEA-39-wide classified raster available at EUNIS Level 1 (9 broad classes) or Level 2 (46 detailed classes).

Usage

get_eunis_data(
  roi,
  level = c("L1", "L2"),
  resolution = 100,
  service_url = NULL
)

Arguments

Details

Unlike the OCS-GE FlatGeobuf source, this EUNIS layer has no raw-value raster endpoint (no ArcGIS ImageServer is published for it) – only a symbolized MapServer export. This function requests that export image at the native resolution with nearest-neighbor resampling (so pixel colors are not blended at class boundaries), then decodes each pixel's exact RGB color back into an EUNIS code using the fixed legend palette shipped as ref_eunis.

Limitations:

• The service caps a single export at 4096 x 4096 pixels. At the default 100 m resolution that is about 410 x 410 km; larger ROIs must use a coarser resolution or be split and fetched in tiles.

• At level = "L2", two pairs of classes are rendered with the identical color by the source service and cannot be told apart from the image alone: D5/F6 and D6/F7. Affected pixels are returned with a combined code (e.g. "D5/F6") rather than silently assigned to one of the two. level = "L1" has no such collision.

• Areas outside the mapped terrestrial extent (e.g. open sea) are detected from the export's alpha channel and returned as NA, not guessed from color.

Value

A categorical SpatRaster in EPSG:3035, with cell values pointing to a factor level table (levels) giving the EUNIS code for that level (see ref_eunis for the matching labels and colors). Cells outside the mapped terrestrial extent (e.g. sea) are NA.

Note

This function requires a working internet connection.

See Also

ref_eunis for the EUNIS code/label/color lookup table, and get_ocsge_data_fgb for the equivalent OCS-GE fetcher.

Examples

## Not run: 
  library(sf)

  my_roi <- st_as_sf(data.frame(
    lon = c(2.3, 2.4, 2.4, 2.3, 2.3),
    lat = c(48.8, 48.8, 48.9, 48.9, 48.8)
  ), coords = c("lon", "lat"), crs = 4326) |> st_combine() |> st_cast("POLYGON")

  eunis_data <- get_eunis_data(roi = my_roi, level = "L1")
  terra::plot(eunis_data)

## End(Not run)

Retrieve species occurrences from GBIF

Description

This function retrieves the occurrence records of a species from the Global Biodiversity Information Facility (GBIF), with the public occurrence search API. No account and no API key are needed.

Only records with coordinates and without known geospatial issue are returned.

Usage

get_gbif_data(species, roi = NULL, limit = 1000, ..., api_url = NULL)

Arguments

Details

The name is first matched against the GBIF taxonomy (species/match), then the records are downloaded by pages of 300 (occurrence/search).

Limitations:

• The search API stops at 100,000 records. For a larger request, split the region of interest, or the years with year.

• The search API gives no DOI. For a publication, GBIF asks to cite a download: see https://www.gbif.org/citation-guidelines and the function occ_download() of the 'rgbif' package.

• GBIF records are opportunistic observations. They show where the species was seen, not where it is absent, and the position can be uncertain by several kilometers.

Value

An sf object of points in EPSG:4326, one row for each record, with the columns:

key

GBIF identifier of the record (https://www.gbif.org/occurrence/<key>).

species

Scientific name of the species.

coordinateUncertaintyInMeters

Uncertainty of the position, in meters. Often NA.

eventDate, year

Date and year of the observation.

basisOfRecord

Type of record, for instance "HUMAN_OBSERVATION" or "PRESERVED_SPECIMEN".

datasetKey

GBIF identifier of the dataset (https://www.gbif.org/dataset/<datasetKey>).

license

License of the record.

The total number of records matching the search in GBIF is stored in the attribute "gbif_count".

Note

This function requires a working internet connection.

See Also

get_eunis_data and get_ocsge for the land cover of the same region of interest.

Examples

## Not run: 
  data("roi_metaleurop")
  occ <- get_gbif_data("Apodemus sylvaticus", roi = roi_metaleurop)
  plot(sf::st_geometry(occ))

  # recent records with a position known within 100 m
  occ <- get_gbif_data("Apodemus sylvaticus", roi = roi_metaleurop,
                       year = "2000,2024",
                       coordinateUncertaintyInMeters = "0,100")

## End(Not run)

Retrieve OCS GE data for a region of interest

Description

Generic entry point to get OCS GE (Occupation du Sol à Grande Echelle) polygons, with their cover (code_cs) and use (code_us) codes, for a Region of Interest (ROI). The source argument selects where the data come from:

"ign"

the official per-department deliveries of the IGN Géoplateforme, see get_ocsge_data_ign. Nothing to host, but each department is downloaded in full the first time.

"fgb"

a remote FlatGeobuf file that you host, see get_ocsge_data_fgb. Only the features of the ROI are downloaded.

Usage

get_ocsge(roi, source = c("ign", "fgb"), ...)

Arguments

Value

An sf object containing the OCS GE polygons clipped to the ROI.

See Also

join_ocsge_species to score the polygons for a species, and get_eunis_data for the EUNIS land cover.

Examples

## Not run: 
  data(roi_metaleurop)

  # from the IGN Géoplateforme (latest vintage of each department)
  ocsge_data <- get_ocsge(roi_metaleurop)
  ocsge_data <- get_ocsge(roi_metaleurop, source = "ign", year = 2021)

  # from a FlatGeobuf file
  ocsge_data <- get_ocsge(roi_metaleurop, source = "fgb",
                          fgb_url = "https://example.com/data/ocsge_france.fgb")

## End(Not run)

Efficiently retrieve OCS GE data from a remote FlatGeobuf

Description

This function retrieves OCS GE (Land Cover) data for a specific Region of Interest (ROI) directly from a remote FlatGeobuf (.fgb) file hosted on a server (e.g., S3).

It leverages GDAL's virtual file system ('/vsicurl/') and the spatial indexing capabilities of FlatGeobuf to download only the data chunks intersecting the bounding box of the ROI, making it highly efficient for large datasets.

Usage

get_ocsge_data_fgb(roi, fgb_url)

Arguments

Details

The function performs the following steps:

1. Transforms the roi to Lambert-93 (EPSG:2154).

2. calculates the bounding box of the roi.

3. Uses sf::st_read with a WKT filter to fetch only relevant features from the remote file.

4. Applies a precise geometric intersection ('st_intersection') to clip the data to the exact shape of the roi.

Value

An sf object containing the OCS GE polygons intersected by the ROI, projected in Lambert-93 (EPSG:2154).

Note

This function requires a working internet connection and GDAL support for the FlatGeobuf driver and network capabilities (vsicurl).

See Also

get_ocsge, get_ocsge_data_ign

Examples

## Not run: 
  library(sf)

  # 1. Define a Region of Interest (ROI)
  # Example: A small bounding box in France
  my_roi <- st_as_sf(data.frame(
    lon = c(2.3, 2.4, 2.4, 2.3, 2.3),
    lat = c(48.8, 48.8, 48.9, 48.9, 48.8)
  ), coords = c("lon", "lat"), crs = 4326)

  # 2. URL to the remote FlatGeobuf file
  # (Replace with the actual URL of your OCS GE bucket)
  url_fgb <- "https://example.com/data/ocsge_grand_est.fgb"

  # 3. Fetch data
  ocsge_data <- get_ocsge_data_fgb(roi = my_roi, fgb_url = url_fgb)

  # 4. Check result
  print(ocsge_data)
  plot(st_geometry(ocsge_data))

## End(Not run)

Retrieve OCS GE data from the IGN Géoplateforme

Description

This function retrieves OCS GE (Land Cover) data for a specific Region of Interest (ROI) from the download service of the IGN Géoplateforme (https://data.geopf.fr/telechargement/resource/OCSGE), which needs no API key.

IGN delivers the OCS GE as one compressed GeoPackage per department and vintage (15 to 270 MB each). The departments intersecting the ROI are downloaded in full, extracted and kept in cache_dir, so that later calls on the same departments read the local file only.

Usage

get_ocsge_data_ign(
  roi,
  year = NULL,
  cache_dir = tools::R_user_dir("spacemodR", "cache"),
  quiet = FALSE
)

Arguments

Details

The function performs the following steps:

1. Finds the departments intersecting the roi with get_departements_for_roi.

2. Lists the OCS GE deliveries of these departments (version 2, GeoPackage) and selects the vintage.

3. Downloads the archives missing from cache_dir, checks their MD5 sum and extracts the OCCUPATION_SOL layer.

4. Reads the features in the bounding box of the roi and clips them to its exact shape.

Corrective patches published separately by IGN (PATCHCORRECTIF) and the layers of differences between two vintages are not used.

Value

An sf object containing the OCS GE polygons intersected by the ROI, with the columns of the IGN delivery (notably code_cs, code_us and millesime), in the projection of the delivery: Lambert-93 (EPSG:2154) in mainland France and Corsica, the local UTM projection in overseas departments.

Note

This function requires a working internet connection and a way to extract 7z archives: either the archive package, or the 7z command line tool (7-Zip, p7zip).

See Also

get_ocsge, get_ocsge_data_fgb

Examples

## Not run: 
  data(roi_metaleurop)

  # latest vintage
  ocsge_data <- get_ocsge_data_ign(roi_metaleurop)

  # a given vintage
  ocsge_2021 <- get_ocsge_data_ign(roi_metaleurop, year = 2021)

  habitat_vole <- join_ocsge_species(ocsge_2021, "Microtus_arvalis")

## End(Not run)

Create a Habitat object

Description

Create a spatial Habitat object based on an optional sf data.frame. If no geometry is provided, creates an empty Habitat object. The object has columns:

• habitat: logical, TRUE/FALSE

• weight: numeric

• priority: numeric, stacking order used by habitat_raster

• touches: logical, pixels filled by the feature in habitat_raster

• geometry: sfc geometry

Usage

habitat(
  geometry = NULL,
  habitat = NULL,
  weight = NULL,
  priority = NULL,
  touches = NULL
)

Arguments

Value

An object of class habitat, inheriting from sf and data.frame.

Examples

library(sf)

# Empty habitat
h <- habitat()
h

# Habitat with geometries
geom <- st_sfc(
  st_point(c(0, 0)),
  st_point(c(1, 1)),
  crs = 4326
)

hab <- habitat(
  geometry = geom,
  habitat = c(TRUE, FALSE),
  weight = c(0.8, 0)
)
hab

Rasterize a Habitat Object

Description

Converts a spatial habitat object into a terra::SpatRaster. The resulting raster stores the weight values of the habitat geometries. Areas explicitly defined as non-habitat (habitat = FALSE) are assigned NA.

Usage

habitat_raster(ground_raster, habitat)

Arguments

Details

The function maps the continuous or categorical weights of the habitat geometries onto a raster grid.

Background handling: By default, pixels not covered by any geometry are assigned a weight of 0 and are considered passable (habitat = TRUE).

Exclusion zones: Geometries added via add_nohabitat() act as absolute barriers. Any pixel intersecting these geometries will be assigned NA, overriding any underlying weight.

Stacking order: Where several features overlap, the pixel takes the value of the feature with the highest priority (see add_habitat); features are stacked from the lowest to the highest priority and the top one is read. With equal priorities, the order of the features in the habitat object decides (last on top). As add_habitat() and add_nohabitat() give by default an increasing priority to each new layer, layers stack in the order they were added unless priority is set explicitly. The same rule applies to exclusion zones: a non-habitat feature excludes a pixel only if no feature of higher priority covers it.

Pixels filled by a feature: The touches value of each feature (see add_habitat) sets which pixels it occupies. With touches = TRUE (the default of add_habitat() and add_nohabitat()) a line or a polygon fills every pixel it touches. A thin structure such as a hedgerow, a river or a road is then a chain of pixels sharing an edge: a corridor stays connected for 4-neighbour movements and a barrier cannot be crossed diagonally. With touches = FALSE, a polygon fills only the pixels whose centre it covers and a line only the pixels of a thin line, which keeps areas unbiased but can break thin structures. Combine a high priority with touches = TRUE to keep a thin structure continuous above the layers it runs through. Points, lines and polygons can be mixed in the same habitat object.

Note that a raster holds one value per pixel: a structure narrower than a pixel is either widened to a full pixel or dropped, and two structures sharing a pixel cannot both be kept. When this matters, the only real solution is a finer ground_raster.

Value

A terra::SpatRaster containing habitat weights, with NA values in explicit non-habitat zones.

Examples

library(terra)
library(sf)

# Create a dummy reference raster
ref_grid <- rast(nrows=10, ncols=10, xmin=0, xmax=10, ymin=0, ymax=10)

# Create habitat zones
good_hab <- st_sf(geometry = st_sfc(st_polygon(list(cbind(c(1, 9, 9, 1, 1), c(1, 1, 9, 9, 1))))))
bad_hab  <- st_sf(geometry = st_sfc(st_polygon(list(cbind(c(4, 6, 6, 4, 4), c(4, 4, 6, 6, 4))))))

# Build habitat object
my_habitat <- habitat() |>
  add_habitat(good_hab, weight = 0.8) |>
  add_nohabitat(bad_hab)

# Rasterize
hab_rast <- habitat_raster(ref_grid, my_habitat)
plot(hab_rast)

# A hedgerow kept continuous across the exclusion zone, whatever the order
# in which the layers are added
hedge <- st_sf(geometry = st_sfc(st_linestring(cbind(c(0, 10), c(0.2, 9.7)))))
my_habitat <- habitat() |>
  add_habitat(hedge, weight = 1, priority = 10) |>
  add_habitat(good_hab, weight = 0.8, priority = 1, touches = FALSE) |>
  add_nohabitat(bad_hab, priority = 2)
plot(habitat_raster(ref_grid, my_habitat))

Test if a directed graph is cyclic

Description

Implements Kahn's algorithm to detect cycles in a directed graph.

Usage

is_cyclic(df)

Arguments

Value

Logical. TRUE if the graph contains at least one cycle.

Examples

df <- data.frame(from=c("A","B"), to=c("B","A"))
is_cyclic(df)

Join OCSGE Spatial Data with Species Traits

Description

This function merges a spatial 'sf' object containing OCSGE (Occupation du Sol à Grande Échelle) polygons with a dictionary of species-specific traits (e.g., habitat suitability, resistance). Polygons are matched on their cover code (CS) and, when the object also carries the land use (US), on the (cover, use) pair.

Usage

join_ocsge_species(
  sf_obj,
  species_pattern,
  code_col = "code_cs",
  species_dict = spacemodR::ocsge_species_dict,
  ref_dict = spacemodR::ref_ocsge,
  us_col = "code_us"
)

Arguments

Details

Cover codes are compared without spaces, so "CS2.2.1" and "CS 2.2.1" are the same class.

In ocsge_species_dict, rows with code_us = NA hold the scores of a cover whatever its use, and rows with a code_us hold the scores of a (cover, use) pair. A polygon takes the scores of its (cover, use) pair when the dictionary has one, and the scores of its cover otherwise. The logical column use_matched of the result tells which of the two was used.

Value

An sf object enriched with the species traits. Returns NULL if the species is not found.

Gaussian kernel smoothing of soil concentrations

Description

A simple, assumption-light alternative to kriging: the value of each cell is the weighted mean of the sampled values, the weights being a Gaussian function of the distance (Nadaraya-Watson estimator):

\hat z(s) = \frac{\sum_i K(s - s_i) z_i}{\sum_i K(s - s_i)}

It re-uses [compute_kernel()] (the dispersal kernel of the package): the points are first rasterised (sum of values and number of points per cell), both rasters are convolved with the kernel using [terra::focal()], and the ratio gives the smoothed map.

Unlike kriging, the result does not honour the data and the smoothing strength ('radius') must be chosen by the user, e.g. by cross-validation with [cv_soil()].

Usage

kernel_smooth_soil(
  points,
  value,
  radius,
  template = NULL,
  log10 = TRUE,
  res = 25,
  margin = 1000,
  size_std = 3,
  mask = NULL
)

Arguments

Value

A 'SpatRaster' with the layer '<value>_log10' (or '<value>' if 'log10 = FALSE') and, if 'log10 = TRUE', the back-transformed layer '<value>'.

See Also

[compute_kernel()], [krige_soil()], [cv_soil()]

Examples

data(soil_metaleurop)
r <- kernel_smooth_soil(soil_metaleurop, "cd", radius = 200, res = 50)
terra::plot(r)

Krige soil concentrations into a raster

Description

Interpolates a soil concentration measured at sampling points onto a regular raster grid by kriging. Two flavours are available:

• **Ordinary kriging (OK)** when 'covariates = NULL': the mean is unknown but constant over the area.

• **Kriging with external drift (KED)** when 'covariates' is a 'SpatRaster': the mean varies linearly with the covariate layers (e.g. the log-distance to a contamination source, see [source_covariates()]) and the kriging interpolates the residuals.

By default the interpolation is done on 'log10(value)', which is the natural scale for metal concentrations (log-normal distribution).

Usage

krige_soil(
  points,
  value,
  template = NULL,
  covariates = NULL,
  model = "Exp",
  vgm = NULL,
  log10 = TRUE,
  res = 25,
  margin = 1000,
  mask = NULL,
  cutoff = NULL,
  width = NULL,
  kappa = 0.5,
  clamp = TRUE,
  verbose = TRUE
)

Arguments

Value

A 'SpatRaster' with three layers:

'<value>_log10'

the kriging prediction on the log10 scale (named '<value>' if 'log10 = FALSE');

'<value>_var'

the kriging variance (same scale as the prediction);

'<value>'

(only if 'log10 = TRUE') the back-transformed concentration '10^prediction', i.e. the median concentration.

The fitted variogram is returned as the attribute '"vgm"' when available.

References

Pebesma, E. J. (2004). Multivariable geostatistics in S: the gstat package. Computers & Geosciences, 30(7), 683-691.

Hengl, T., Heuvelink, G. B. M., & Rossiter, D. G. (2007). About regression-kriging: From equations to case studies. Computers & Geosciences, 33(10), 1301-1315.

See Also

[soil_variogram()], [fit_soil_variogram()], [cv_soil()], [source_covariates()], [kernel_smooth_soil()]

Examples

data(soil_metaleurop)
# Ordinary kriging of log10(Cd) at 100 m
r <- krige_soil(soil_metaleurop, "cd", res = 100, margin = 500)
terra::plot(r)

load raster file from internal data

Description

A raster of the (example).

Usage

load_raster_extdata(path_name)

Arguments

Format

An object 'SpatRaster' (package terra).

Value

A SpatRaster object

Source

Internal data package.

See Also

rast

Get resource layers for a given trophic layer

Description

Returns the upstream neighbors (prey/resources) of a given layer in a trophic graph.

Usage

lower_neighbors(trophic_tbl, layer)

Arguments

Value

A character vector of names of layers that are resources for layer.

Normalize weights of a trophic table

Description

Adds a new column normalized_weight to a trophic_tbl object so that, for each target node (to), the sum of incoming weights equals 1.

Usage

normalize_weights(tbl)

Arguments

Details

For every unique value in the to elements of the link column, the function divides each corresponding weight by the total weight of all links pointing to that same node.

Nodes with no incoming links are left unchanged.

Value

A trophic_tbl object with an additional column normalized_weight.

Examples

net <- trophic() |>
  add_link("a", "b", weight = 2) |>
  add_link("c", "b", weight = 3)

net_norm <- normalize_weights(net)

SF object defining very simplified OCS-GE soil cover metaleurop

Description

Simple feature collection with 9 features and 11 fields. Projected CRS: RGF93 v1 / Lambert-93.

Usage

data(ocsge_metaleurop)

Format

An object of class sf (inherits from data.frame) with 9 rows and 12 columns.

Examples

data(ocsge_metaleurop)

Species habitat dictionary for OCS-GE land cover and land use

Description

Habitat suitability, breeding, foraging and movement scores of 935 European terrestrial vertebrates (amphibians, reptiles, birds, mammals) for the French OCS-GE nomenclature: the 14 cover classes (CS), and 47 (cover, use) pairs for which the land use (US) changes what the cover stands for. It has the same columns as eunis_species_dict, plus code_us.

Usage

data(ocsge_species_dict)

Format

A data frame with 57,035 rows (935 species x 61 classes) and 14 columns:

code_cs

Character. OCS-GE cover code, as in ref_ocsge$code_cs (e.g. "CS2.2.1").

code_us

Character. OCS-GE use code (e.g. "US1.1"), or NA on the rows giving the scores of the cover whatever its use.

species_name

Character. Scientific name, genus and species separated by "_".

sppID

Character. Species identifier from the EEA source table; the first letter gives the group (A, R, B, M).

taxon_group

Character. "amphibian", "reptile", "bird" or "mammal".

suitability

Integer (0-10). Overall habitat suitability: 0 unused or avoided, 2 marginal, 5 secondary, 8 main, 10 core habitat.

weight_breeding

Integer (0-10). Value of the class as a breeding, nesting, denning, roosting or larval site.

weight_foraging

Integer (0-10). Value of the class for feeding.

permeability

Integer (0-10). Ease of movement through the class: 10 moves freely, 7 readily, 4 reluctantly, 1 strong barrier, 0 impassable. Accounts for flight, swimming or fossorial habits.

resistance

Numeric (1-100). Derived from permeability as 10^(2 * (1 - permeability / 10)): 10 gives 1, 5 gives 10, 0 gives 100.

habitat_breadth

Integer. Species-level: number of cover classes (out of 14) with suitability >= 5.

specialization

Numeric (0-1). Species-level: 1 minus the normalised Shannon evenness of the suitability profile over the 14 cover classes (0 generalist, 1 specialist).

confidence

Integer (1-3). Species-level: 1 vague or missing source description, 2 medium, 3 detailed.

in_france

Integer (0/1). Species-level: 1 if the species occurs natively and regularly in metropolitan France (expert knowledge). The dictionary covers Europe while OCS-GE covers France: filter on this column first.

Details

Cover rows (code_us = NA, 935 x 14). Scores were assigned by a large language model (Claude Opus 5.5) reading the EEA habitat and avoided-habitat descriptions of each species against an ecological description of each cover class, with the anchored rubric given above.

Cover x use rows (935 x 47). They are derived, not scored again: each (cover, use) pair is linked to one or several EUNIS Level 2 classes by ref_ocsge_eunis, and takes the mean of their eunis_species_dict scores weighted by 1 / rank. For instance herbaceous cover under agricultural use (CS2.2.1, US1.1) is mostly arable land (I1, rank 1) but may be permanent grassland (E2, rank 2). Only the pairs listed in ref_ocsge_eunis have a row; for any other pair use the cover row, which is what join_ocsge_species does.

These are expert-judgement scores and have not been validated against occurrence data. They were checked against the keywords of the EEA habitat, avoided-habitat and threat descriptions (raw_data/check_ocsge_species_dict.R of the source repository): species naming a habitat score higher on the matching class than the others, with a probability of 0.57 to 0.93 depending on the habitat for cover rows, and 0.68 to 0.93 for cover x use rows. The use helps most for gardens and parks (0.57 to 0.80), quarries (0.69 to 0.93) and arable land (0.74 to 0.77).

Change from spacemodR 0.3.0. The dictionary used to hold scores from another model for the cover classes only, in the columns weight_global, weight_movement, weight_foraging and resistance (1-10), with nom_espece and cover codes written with a space ("CS 2.2.1"). suitability replaces weight_global and permeability replaces weight_movement; values differ (Spearman correlation 0.72 for suitability, 0.22 for movement, mostly because flying species were scored as if they walked) and are lower on classes that are not habitat.

References

The species list and the habitat descriptions come from:

O'Connor, L. M. J., Renaud, J., Dou, Y., Karger, D. N., Maiorano, L., Verburg, P. H., & Thuiller, W. (2024). Habitat Suitability of European Land Systems for Terrestrial Vertebrates. Global Ecology and Biogeography, 33(11), e13903.

See Also

join_ocsge_species, ref_ocsge, ref_ocsge_eunis, eunis_species_dict

Examples

data(ocsge_species_dict)
apsy <- subset(ocsge_species_dict, species_name == "Apodemus_sylvaticus")

# scores of each cover, whatever its use
subset(apsy, is.na(code_us), c(code_cs, suitability, permeability, resistance))

# herbaceous cover, by use
subset(apsy, code_cs == "CS2.2.1", c(code_us, suitability, weight_foraging))

OCS-GE land cover and land use of the Metaleurop site

Description

OCS-GE polygons of roi_metaleurop with both the cover (CS) and the use (US), dissolved into one multipolygon per (cover, use) pair. ocsge_metaleurop is dissolved by cover only and cannot be used to study the land use.

Usage

data(ocsge_us_metaleurop)

Format

An sf object (EPSG:2154) with 68 multipolygons and 3 columns:

code_cs

Character. OCS-GE cover code (e.g. "CS2.2.1").

code_us

Character. OCS-GE use code (e.g. "US1.1").

area_ha

Numeric. Area of the (cover, use) pair on the site, in hectares.

Details

Built from the 2021 OCS-GE of the Nord and Pas-de-Calais departments, as returned by get_ocsge_data_fgb. Outlines are simplified with a 3 m tolerance to keep the dataset small.

See Also

join_ocsge_species, ocsge_species_dict

Examples

data(ocsge_us_metaleurop)
head(ocsge_us_metaleurop)

Plot a Habitat object

Description

Visually inspect a habitat object. Favorable habitat zones are colored in green, and absolute barriers (non-habitat) are colored in red.

Usage

## S3 method for class 'habitat'
plot(x, ...)

Arguments

Value

x, invisibly. Called for its side effect: a plot of the habitat zones.

Examples

library(sf)
square <- function(a, b) st_polygon(list(cbind(c(a, b, b, a, a), c(a, a, b, b, a))))
good_polygons <- st_sf(geometry = st_sfc(square(1, 9)))
roads <- st_sf(geometry = st_sfc(square(4, 6)))
hab <- habitat() |>
  add_habitat(good_polygons, weight = 1) |>
  add_nohabitat(roads)
plot(hab)

Plot an empirical variogram and its fitted model(s)

Description

Plot an empirical variogram and its fitted model(s)

Usage

## S3 method for class 'soil_vgm'
plot(x, all_models = TRUE, ...)

Arguments

Value

A 'ggplot' object.

Plot a trophic table

Description

Creates a simple graphical representation of a trophic network using ggplot2.

Usage

## S3 method for class 'trophic_tbl'
plot(
  x,
  shift = TRUE,
  colors = NULL,
  edge_width = c("none", "raw", "norm_target", "norm_source"),
  ...
)

Arguments

Details

Nodes are positioned according to their trophic level:

• The y-axis represents trophic levels

• Nodes of the same level are placed on the same horizontal line

• The x-axis positions are assigned sequentially (0, 1, 2, ...)

Directed links are drawn from lower to higher trophic levels using arrows.

Value

A ggplot object.

Examples

net <- trophic() |>
  add_link("a", "b") |>
  add_link("b", "c")

# Plot with default colors
plot(net)

# Plot with custom colors
my_pal <- c("a" = "brown", "b" = "green", "c" = "blue")
plot(net, colors = my_pal)

Plot Habitat Suitability Maps from OCSGE Data

Description

Generates a composite plot of three spatial maps (suitability, foraging weight and resistance) from an 'sf' object previously enriched with species traits.

Usage

plot_species_habitat(sf_merged, title_species = NULL)

Arguments

Details

Suitability and foraging weight are drawn on their full 0-10 scale, and resistance on its 1-100 logarithmic scale, so that maps of different species can be compared.

Value

A patchwork object containing the combined ggplot2 maps.

Create a RasterStack from a list of rasters and names

Description

This function creates a 'SpatRaster' stack from a list of rasters and assigns unique names to each layer.

Usage

raster_stack(raster_list, names = NULL)

Arguments

Details

The function checks that the length of 'raster_list' matches the length of 'names', and that all names are unique. If not, it stops with an error.

Value

A SpatRaster object with named layers.

Examples

# Example with terra rasters
library(terra)
r1 <- rast(nrows=10, ncols=10, vals=1:100)
r2 <- rast(nrows=10, ncols=10, vals=101:200)
raster_stack(list(r1, r2), c("layer1", "layer2"))

Nomenclature and render colors of EUNIS land cover classes

Description

Code / label / render-color lookup table for the EEA "Ecosystem Type Map v3.1, terrestrial part" ArcGIS service, at both EUNIS Level 1 (9 broad classes) and Level 2 (46 detailed classes). It is used internally by get_eunis_data to decode the service's exported image back into EUNIS codes, since that service publishes no raw-value raster endpoint.

Usage

data(ref_eunis)

Format

A data frame with the following columns:

level

Character. "L1" or "L2".

code

Character. The EUNIS code (e.g. "J1" at L2, "J" at L1).

nomenclature

Character. The class label.

couleur

Character. The hex render color used by the source ArcGIS service.

r, g, b

Integer (0-255). The render color, split into channels for exact matching.

Details

At level == "L2", two pairs of classes share an identical render color and cannot be distinguished from the exported image alone: D5/F6 and D6/F7. level == "L1" has no such collision. See get_eunis_data for how this is handled.

Source

European Environment Agency, "Ecosystem Type Map v3.1, terrestrial part": https://bio.discomap.eea.europa.eu/arcgis/rest/services/Ecosystem/EcosystemTypeMap_v3_1_Terrestrial/MapServer

Examples

data(ref_eunis)

Nomenclature of OCS-GE soil cover

Description

Nomenclature of OCS-GE soil cover

Usage

data(ref_ocsge)

Format

An object of class data.frame with 14 rows and 4 columns.

Examples

data(ref_ocsge)

Crosswalk between OCS-GE (cover and use) and EUNIS Level 2

Description

Correspondence table between the French OCS-GE nomenclature and the 45 EUNIS Level 2 classes of ref_eunis. The two nomenclatures do not nest: OCS-GE cover (CS) describes what physically covers the ground in 14 classes, while EUNIS describes habitats. The relation is therefore many-to-many, and each OCS-GE class is given a ranked list of EUNIS classes. Rows with a land use code (US) refine the match for a given cover: for instance herbaceous cover (CS2.2.1) is arable land (I1) under agricultural use, but gardens and parks (I2) under residential use.

Usage

data(ref_ocsge_eunis)

Format

A data frame with 152 rows and 7 columns:

code_cs

Character. OCS-GE cover code, as in ref_ocsge$code_cs (e.g. "CS2.2.1").

code_us

Character. OCS-GE use code (e.g. "US1.1"), or NA for the cover-only match, to be used when the use is unknown or has no dedicated row.

code_eunis

Character. EUNIS Level 2 code, as in ref_eunis$code and eunis_species_dict$code_eunis.

rank

Integer. Order of the EUNIS classes within a (code_cs, code_us) group; 1 is the default match.

main_cs

Logical. TRUE on the single row giving the main OCS-GE cover of each EUNIS class (EUNIS to OCS-GE direction, 45 rows).

pct_metaleurop

Numeric. Measured share (%) of the OCS-GE group area falling in this EUNIS class on the Metaleurop site; NA when the group covers less than 10 ha there. See Details.

note

Character. What the match stands for, and what extra data would be needed to resolve it.

Details

rank is an expert reading of the two nomenclatures (CNIG OCS-GE specification and EUNIS habitat definitions). pct_metaleurop is a measurement: OCS-GE polygons of roi_metaleurop were overlaid on the EUNIS raster returned by get_eunis_data. The two columns are not expected to agree closely, because the EUNIS raster has a 100 m pixel carrying one dominant class, while OCS-GE maps objects a few meters wide: a road or a garden usually falls in a pixel classified as buildings (J1, J2). The measurement also only covers the classes present on this site.

Some EUNIS distinctions cannot be recovered from OCS-GE, with or without the use: wetlands (D), grassland types (E1 to E4), standing versus running water (C1, C2), deciduous versus evergreen broadleaved woodland (G1, G2), and crops versus permanent grassland, which share the same cover and the same agricultural use.

The source table and the build script are raw_data/ocsge_eunis_crosswalk.csv and raw_data/build_ref_ocsge_eunis.R of the source repository.

See Also

ref_ocsge, ref_eunis, eunis_species_dict, get_ocsge_data_fgb

Examples

data(ref_ocsge_eunis)

# default EUNIS class of each OCS-GE cover
subset(ref_ocsge_eunis, is.na(code_us) & rank == 1)

# main OCS-GE cover of each EUNIS class
subset(ref_ocsge_eunis, main_cs, c(code_eunis, code_cs))

# EUNIS class of OCS-GE polygons from cover and use, falling back on cover alone
ocs <- data.frame(code_cs = c("CS2.2.1", "CS2.2.1", "CS2.1.1.1"),
                  code_us = c("US1.1", "US5", "US1.2"))
best <- subset(ref_ocsge_eunis, rank == 1)
with_us <- best$code_eunis[match(paste(ocs$code_cs, ocs$code_us),
                                 paste(best$code_cs, best$code_us))]
cs_only <- best$code_eunis[match(paste(ocs$code_cs, NA),
                                 paste(best$code_cs, best$code_us))]
ocs$code_eunis <- ifelse(is.na(with_us), cs_only, with_us)
ocs

SF object defining ROI metaleurop

Description

Simple feature collection with 1 feature and 1 field. Geodetic CRS: WGS 84.

Usage

data(roi_metaleurop)

Format

An object of class sf (inherits from data.frame) with 1 rows and 2 columns.

Examples

data(roi_metaleurop)

DataBase of collected MicroMammals species

Description

DataBase of collected MicroMammals species

Usage

data(sf_micromammals)

Format

An object of class sf (inherits from tbl_df, tbl, data.frame) with 1426 rows and 27 columns.

Examples

data(sf_micromammals)

Build a regular raster grid around sampling points

Description

Creates an empty 'SpatRaster' covering the bounding box of 'points', enlarged by 'margin' on each side, with square cells of size 'res'. The extent is aligned on multiples of 'res'.

Usage

soil_grid(points, res = 25, margin = 1000)

Arguments

Value

An empty 'SpatRaster'.

Examples

data(soil_metaleurop)
soil_grid(soil_metaleurop, res = 25, margin = 1000)

Soil metal concentrations sampled around the former Metaleurop smelter

Description

Topsoil concentrations of cadmium (Cd), lead (Pb) and zinc (Zn) measured at 595 sampling points around the former Metaleurop Nord lead and zinc smelter (Noyelles-Godault, northern France). This is the data set used to build the soil rasters of the package ('ground_concentration_*.tif'; the cadmium raster is shipped in 'inst/extdata'), see the kriging article.

Simple feature collection (POINT), projected CRS: RGF93 v1 / Lambert-93 (EPSG:2154).

Usage

data(soil_metaleurop)

Format

An 'sf' object with 595 rows and 9 columns:

id

Sample identifier.

usage

Land-use class of the sampled plot, as coded in the original survey ('"A"', '"L"', '"U"').

dist

Distance (m) to the smelter chimney.

angle

Direction of the sample seen from the smelter (degrees clockwise from the North).

wind

Wind-rose frequency (in %) of the 20-degree sector containing 'angle' (18 sectors centred on 0, 20, ..., 340 degrees, summing to 100). The highest values lie north-east of the smelter, i.e. downwind of the dominant south-westerly winds.

cd, pb, zn

Total concentrations in mg/kg of dry soil.

geometry

Sampling location.

Examples

data(soil_metaleurop)
summary(soil_metaleurop[, c("cd", "pb", "zn")])

Empirical semi-variogram of soil concentrations

Description

Computes the experimental (empirical) semi-variogram of a soil concentration measured at sampling points: for every pair of points separated by a distance falling in a given distance class (a *lag*), half of the squared difference of their values is averaged.

When 'covariates' are given, the variogram is computed on the residuals of the linear regression of the values on the covariates (this is the variogram needed for kriging with external drift, see [krige_soil()]).

Usage

soil_variogram(
  points,
  value,
  log10 = TRUE,
  covariates = NULL,
  cutoff = NULL,
  width = NULL,
  estimator = c("classical", "robust")
)

Arguments

Value

A 'data.frame' of class 'soil_variogram' with columns 'np' (number of pairs), 'dist' (mean distance of the pairs) and 'gamma' (semi-variance).

See Also

[fit_soil_variogram()], [krige_soil()]

Examples

data(soil_metaleurop)
v <- soil_variogram(soil_metaleurop, "cd")
v

Build a variogram model by hand

Description

Creates a 'soil_vgm' object from known parameters, e.g. to re-use a variogram fitted elsewhere (gstat, gstools, ...).

Usage

soil_vgm(model, psill, range, nugget = 0, kappa = 0.5)

Arguments

Value

A 'soil_vgm' object.

Examples

soil_vgm("Exp", psill = 0.1, range = 500, nugget = 0.02)

Distance and wind covariates around a contamination source

Description

Builds covariate rasters describing the position of every cell relative to a point source of contamination (e.g. a smelter chimney). They can be used as external drift in [krige_soil()]:

'log10_dist'

the log10 of the distance (m) to the nearest source. Atmospheric deposition typically decreases as a power law of the distance, i.e. linearly in log-log scale.

'wind'

(only if 'wind_rose' is given) the frequency of the wind sector in which the cell lies, seen from the source. Cells lying downwind of the dominant winds receive more deposition.

Usage

source_covariates(template, source, wind_rose = NULL, min_dist = NULL)

Arguments

Value

A 'SpatRaster' with layers 'log10_dist' and, optionally, 'wind'.

Examples

data(soil_metaleurop)
grid <- soil_grid(soil_metaleurop, res = 100)
smelter <- sf::st_sfc(sf::st_point(c(701156, 7036799)), crs = 2154)
cov <- source_covariates(grid, smelter)
terra::plot(cov)

Create a spacemodel object

Description

Constructor for the 'spacemodel' class. This function combines spatial data (a raster stack) and ecological data (a trophic table) into a single object used for modelling.

Usage

spacemodel(raster_stack, trophic_tbl)

Arguments

Details

The function performs several checks to ensure data consistency:

• Verifies that raster_stack is a SpatRaster.

• Verifies that trophic_tbl is a trophic_tbl object.

• Ensures the number of raster layers matches the number of levels in the trophic table.

• Ensures that the names of the raster layers match the names in the trophic table.

Value

A SpatRaster object with the following additional attributes:

• trophic_tbl: The trophic_tbl object passed as input.

• spacemodel: A logical flag set to TRUE, indicating this raster is part of a spacemodel.

See Also

rast, trophic_tbl

Summarise a cross-validation

Description

Summarise a cross-validation

Usage

## S3 method for class 'soil_cv'
summary(object, ...)

Arguments

Value

A one-row 'data.frame' with 'RMSE' (root mean squared error), 'MAE' (mean absolute error), 'bias' (mean error), 'R2' (proportion of variance explained, 1 - SSE/SST) and 'n' (number of predicted points).

Transfer (food, contaminant) across trophic levels

Description

Computes the transfer through a trophic network from lower to higher trophic levels using spatial spreading and flux functions.

Usage

transfer(
  spacemodel,
  kernels,
  fluxes = NULL,
  exposure_weighting = "local",
  verbose = FALSE
)

Arguments

Details

The function processes layers in ascending trophic order, as defined by the level attribute of the trophic table.

For each layer:

1. Resources (lower neighbors) are identified.

2. Concentration from each resource is spatially spread using spread().

3. Flux is computed using apply_flux().

4. Contributions from all resources are summed.

The function assumes that flux weights are already normalized so that, for each consumer, the sum of contributions from all resources equals 1.

Value

A named 'spacemodel' object as a list of spatial layers representing values after transfer.

Create a trophic table

Description

Creates a data.frame of class trophic_tbl designed to store trophic links. Can be initialized empty or from an existing data.frame.

Usage

trophic(data = NULL, from = NULL, to = NULL, weight = NULL)

Arguments

Value

An object of class trophic_tbl.

Examples

# 1. Empty initialization (pipe style)
net <- trophic() |>
  add_link("sol", "sp1")

# 2. Initialization from data.frame
df_raw <- data.frame(src = c("A", "A"), target = c("B", "C"), w = c(2, 5))
net_from_df <- trophic(df_raw, from = "src", to = "target", weight = "w")

Validate a trophic table

Description

Internal function used to ensure that a trophic_tbl object respects all structural constraints.

Usage

validate_trophic_tbl(df)

Arguments

Details

The function checks that:

• All links are unique

• No self-loops are present

• The graph is acyclic

It also computes and updates the trophic level attribute.

Value

A validated trophic_tbl object.