dynamic_proj: Project species distribution and abundance models onto...

In dynamicSDM: Species Distribution and Abundance Modelling at High Spatio-Temporal Resolution

Project species distribution and abundance models onto dynamic environmental covariates.

Description

Projects fitted species distribution and abundance models onto projection covariates for each date given.

Usage

dynamic_proj(
  dates,
  projection.method,
  local.directory,
  drive.folder,
  user.email,
  sdm.mod,
  sdm.thresh,
  sdm.weight,
  sam.mod,
  sam.weight,
  save.directory,
  save.drive.folder,
  cov.file.type,
  prj = "+proj=longlat +datum=WGS84",
  proj.prj,
  spatial.mask
)

Arguments

dates	a character string, vector of dates in format "YYYY-MM-DD".
projection.method	a character string or vector, the method or methods to project distribution and abundance onto projection covariates. Options include proportional, binary, abundance and stacked. See details for more information.
local.directory	optional; a character string, the path to a local directory to read projection covariate data frames from.
drive.folder	optional; a character string, the Google Drive folder to read projection covariate data frames from. Folder must be uniquely named within Google Drive. Do not provide path.
user.email	optional; a character string, user email for initialising Google Drive. Required if drive.folder or save.drive.folder used.
sdm.mod	optional; a model object or list of model objects fitted to species distribution data.
sdm.thresh	optional; a numeric value, the threshold to convert projected distribution suitability into binary presence-absence. Default 0.5. Required if projection.method is "binary" or "stacked".
sdm.weight	optional; a numeric string, weights given to each sdm.mod model projection, given in the same order as the sdm.mod list. Default is equal weighting to all models.
sam.mod	optional; a model object or list of model objects fitted to species abundance data.
sam.weight	optional; a numeric string, weights given to each sdm.mod model projection, given in the same order as the sam.mod list. Default is equal weighting to all models.
save.directory	optional; a character string, path to local directory to save projection rasters to.
save.drive.folder	optional; a character string, Google Drive folder to save projection rasters to. Folder must be uniquely named within Google Drive. Do not provide path.
cov.file.type	a character string, the type of file that contains projection covariates. One of: "tif" (raster stack) or csv(data frame).
prj	a character string, the coordinate reference system of input projection covariates. Default is "+proj=longlat +datum=WGS84".
proj.prj	a character string, the coordinate reference system desired for output projection rasters. Default is assumed to be the same as prj.
spatial.mask	an object of class SpatRaster orsf polygon, representing a mask in which NA cells in the mask layer are removed from the projection covariates.

Details

Function projects a model object or list of model objects onto projection covariate data frames for each projection date given.

Value

Exports projection rasters for each projection date to user-specified Google Drive folder or local directory.

Projection covariate input

• Data frames: if cov.file.type = csv, then projection covariates must be saved "csv" files in the drive.folder or local.directory given. Here, they must be unique in containing the relevant projection date in YYYY-MM-DD format. For instance, two or more csv files saved within the Google Drive folder or local directory that contain the projection date will result in function error. Additionally, column names of projection covariate data frames must match the explanatory variable names that fitted models are trained on.

• Raster stacks: if cov.file.type = tif, then projection covariates must be saved "tif" files, similarly named and formatted as above. Raster layer names must match the explanatory variable names that fitted models are trained on.

Note: It is important to state the coordinate reference system projection of covariates using argument prj.

Model input

When multiple models are provided in sdm.mod or sam.mod, the function projects each model onto the projection covariates and takes the average value across all model projections. If sdm.weight or sam.weight is specified, then the weighted average of model projections is returned. For example, this could be used to down weigh projections by poorly performing models in an ensemble (Ara?jo and New, 2007).

Projection output

• proportional: Projects sdm.mod model objects onto projection covariates for each date, exporting rasters for projected distribution suitability, a continuous measure between 0 (least suitable) and 1 (most suitable).

• binary: Projects sdm.mod onto projection covariates for each date, exporting rasters for projected binary presence (1) or absence (0), derived from distribution suitability using user-specified threshold (sdm.thresh) or default threshold of 0.5 (Jim?nez-Valverde And Lobo, 2007).

• abundance: Projects sam.mod onto projections covariates for each date, exporting rasters for projected abundance in the units that sam.mod were fitted onto.

• stacked: Follows the binary projection method and then projects abundance onto only binary presence (1) cells using the abundance projection method.

Projections are output as rasters. These can be reprojected to a different coordinate reference system using argument proj.prj.

One or both of save.drive.folder and save.directory are required to specify where projection rasters are to be saved.

Google Drive compatibility

If drive.folder or save.drive.folder given, please ensure the folder name is unique within your Google Drive. Do not provide the path if the folder is nested within others.

If one of drive.folder or save.drive.folder are used then user.email is required to access the appropriate Google Drive user account. This requires users to have installed R package googledrive and initialised Google Drive with valid log-in credentials. Please follow instructions on https://googledrive.tidyverse.org/.

References

Araujo, M. B. & New, M. 2007. Ensemble Forecasting Of Species Distributions. Trends In Ecology & Evolution, 22, 42-47.

Jimenez-Valverde, A. & Lobo, J. M. 2007. Threshold Criteria For Conversion Of Probability Of Species Presence To Either-Or Presence-Absence. Acta Oecologica, 31, 361-369.

Examples

# Read in data
data("sample_explan_data")

# Set variable names
variablenames<-c("eight_sum_prec","year_sum_prec","grass_crop_percentage")

model <- brt_fit(sample_explan_data,
                 response.col = "presence.absence",
                 varnames = variablenames,
                 interaction.depth = 1,
                 distribution = "bernoulli",
                 n.trees = 1500)

data(sample_cov_data)
utils::write.csv(sample_cov_data,file=paste0(tempdir(),"/2018-04-01_covariates.csv"))

dynamic_proj(dates = "2018-04-01",
            projection.method = c("proportional"),
            local.directory = tempdir(),
            cov.file.type = "csv",
            sdm.mod = model,
            save.directory = tempdir())

dynamicSDM documentation built on June 28, 2024, 5:08 p.m.