Generate pseudo-absences for presence-only data — generate_pseudo

Presence-only records cannot be modelled directly: every method in AlphaSDM needs absence or background data, and how that background is placed is a modelling decision with real consequences (Barbet-Massin et al. 2012, *Methods in Ecology and Evolution* 3:327-338). This function makes that decision explicit. It draws pseudo-absences inside `aoi` under the strategy you choose, reports every threshold it used, and returns your presences and the new absences as one data frame ready for [evaluate_models()] or [generate_map()].

Usage

generate_pseudo_absences(
  data,
  aoi,
  strategy,
  n = 10000L,
  radius_m = NULL,
  env_threshold = NULL,
  aoi_year = NULL,
  scale = 10,
  seed = 0L,
  gee_project = NULL
)

Arguments

data

Formatted presence records from [format_data()] (standard `longitude`, `latitude`, `year`, `present` columns, all presences). The workflow is format first, then add absences:


pres <- format_data(obs, coords = c("lon", "lat"), year = "yr")
data <- generate_pseudo_absences(pres, aoi = ..., strategy = ...)
evaluate_models(data)

aoi

Where absences may be placed: an `ee.Geometry`, a `list(lon, lat, radius)`, a path to a vector file, or the string `"bbox"` to use the presence bounding box (an explicit choice, not a silent default; a bounding box is rarely the right availability frame for clustered records).

strategy

One of `"random"`, `"disk"`, `"envelope"`, `"combined"`. No default: this is the modelling decision.

n

Number of pseudo-absences (default 10000, Barbet-Massin et al. 2012; use about the presence count for `"combined"` feeding tree methods).

radius_m

Disk radius in metres; NULL estimates it from the embedding-autocorrelation range and reports it.

env_threshold

Mahalanobis envelope threshold; NULL uses the bias-corrected presence maximum and reports it.

aoi_year

Embedding year for placement checks (default: latest Alpha Earth year).

scale

Sampling scale in metres (default 10).

seed

Integer seed for the draws.

gee_project

Optional Earth Engine cloud project.

Value

A data frame with `longitude`, `latitude`, `year`, `present` (your presences as 1, pseudo-absences as 0), ready for [evaluate_models()] or [generate_map()] directly, carrying the settings used in `attr(, "pa_settings")`.

Details

Strategies, following Barbet-Massin et al. (2012):

`"random"`: Uniform over the AOI. Their recommendation for regression-style methods and MaxEnt (with `n = 10000`). MaxEnt is not in the default ensemble for exactly this reason: give it its own random set and run `methods = "maxent"` separately.
`"disk"`: Their "2-degree-far": only beyond a distance from every presence. `radius_m = NULL` estimates the distance at which embedding similarity to the presences decays to the regional baseline, and reports it; pass a number to choose it yourself.
`"envelope"`: Their SRE, in embedding space: only outside the presence environmental envelope, measured as Mahalanobis distance to the presence cloud. `env_threshold = NULL` uses the bias-corrected maximum distance among the presences themselves.
`"combined"`: Both exclusions at once. Their recommendation for classification and machine-learning methods (rf, gbt) with `n` near the number of presences; validated here on Bicknell's Thrush (real-absence AUC) and *Prunus africana* (Boyce index).

Supply is guaranteed: every strategy redraws until `n` points with satellite coverage survive the active exclusions; `"disk"` and `"combined"` halve the radius stepwise rather than come up short (the envelope never relaxes, since points inside it are the likely false absences the strategy exists to avoid).

Examples

if (FALSE) { # \dontrun{
pres <- format_data(records, coords = c("lon", "lat"), year = "year")
occ  <- generate_pseudo_absences(pres, aoi = "bbox", strategy = "combined",
                                 n = nrow(pres))
table(occ$present)
} # }