generate_pa_env_space_flexsdm

Uses flexsdm::sample_pseudoabs(method = c("env_const", env = predictors) within each timestamp stratum so that
pseudo-absences match the temporal distribution of presences. Extracts covariate values
for the sampled coordinates and returns a data.frame with the same predictor columns.

A user-friendly 'shiny' application for Bayesian machine
learning analysis of marine species distributions. GLOSSA (Global
Ocean Species Spatio-temporal Analysis) uses Bayesian Additive
Regression Trees (BART; Chipman, George, and McCulloch (2010)
<doi:10.1214/09-AOAS285>) to model species distributions with
intuitive workflows for data upload, processing, model fitting, and
result visualization. It supports presence-absence and presence-only
data (with pseudo-absence generation), spatial thinning,
cross-validation, and scenario-based projections. GLOSSA is designed
to facilitate ecological research by providing easy-to-use tools for
analyzing and visualizing marine species distributions across
different spatial and temporal scales. Optionally, pseudo-absences can
be generated within the environmental space using the external package
'flexsdm' (not on CRAN), which can be downloaded from
<https://github.com/sjevelazco/flexsdm>; this functionality is used
conditionally when available and all core features work without it.

Jorge Mestre-Tomás

glossa

User-Friendly 'shiny' App for Bayesian Species Distribution
Models

Alba Fuster-Alonso

generate_pa_env_space_flexsdm function

<dl><dt>presences</dt>
<dd>Data frame containing presence points.</dd>
<dt>raster_stack</dt>
<dd>`SpatRaster` object containing covariate data. Uses continuous predictor only and samples per timestamp.</dd>
<dt>predictor_variables</dt>
<dd>Character vector of the predictor variables selected for this species.</dd>
<dt>coords</dt>
<dd>Character vector specifying the column names for latitude and longitude. Defaults to `c("decimalLongitude", "decimalLatitude")`.</dd>
<dt>ratio</dt>
<dd>Ratio of pseudo-absences to presences (default 1 = balanced).</dd>
<dt>attempts</dt>
<dd>Integer specifying the number of attempts to generate exact pseudo-absences. Defaults to 100.</dd>
<dt>seed</dt>
<dd>Optional seed for reproducibility.</dd></dl>

Arguments

Generate Environmental-space Pseudo-Absences via flexsdm (per temporal stratum) — generate_pa_env_space_flexsdm

<dl>

<dt>presences</dt>
<dd>Data frame containing presence points.</dd>


<dt>raster_stack</dt>
<dd>`SpatRaster` object containing covariate data. Uses continuous predictor only and samples per timestamp.</dd>


<dt>predictor_variables</dt>
<dd>Character vector of the predictor variables selected for this species.</dd>


<dt>coords</dt>
<dd>Character vector specifying the column names for latitude and longitude. Defaults to `c("decimalLongitude", "decimalLatitude")`.</dd>


<dt>ratio</dt>
<dd>Ratio of pseudo-absences to presences (default 1 = balanced).</dd>


<dt>attempts</dt>
<dd>Integer specifying the number of attempts to generate exact pseudo-absences. Defaults to 100.</dd>


<dt>seed</dt>
<dd>Optional seed for reproducibility.</dd>

</dl>

generate_pa_env_space_flexsdm: Generate Environmental-space Pseudo-Absences via flexsdm (per temporal stratum)

Description

Usage

Value

Arguments