(Advanced) Build a human-readable multi-section report

Most users do not need to call this directly. When you run spesim_run(write_outputs = TRUE), a report is written to disk automatically under your timestamped OUTPUT_PREFIX. This function returns the same report as a single character string for advanced workflows (e.g., embedding the text in another system, tests, or custom pipelines).

The report summarises environmental ranges, gradient correlations, species abundance distribution, per-quadrat alpha diversity, diversity partitioning, simple spatial autocorrelation, Fisher log-series validation, and computation notes (e.g., whether fast Rcpp engines were used for the point processes).

Usage

generate_full_report(res, include_audit = TRUE, audit_top_n = 6)

Arguments

res

A named list produced by the simulator with at least the elements:

P: Parameter list returned by load_config(). Must include keys such as N_INDIVIDUALS, N_SPECIES, DOMINANT_FRACTION, FISHER_ALPHA, FISHER_X, and, if used, a tibble GRADIENT with columns species, gradient (one of "temperature","elevation","rainfall"), optimum, and tol.
env_gradients: A data frame with columns temperature_C, elevation_m, and rainfall_mm containing the gridded environmental fields used for context.
species_dist: An sf POINT layer of individuals with a species column. Used for tallies and alpha snapshots.
quadrats: An sf POLYGON layer with quadrat_id; used to compute per-site alpha summaries.
abund_matrix: A site \(\times\) species abundance table (first column site; remaining columns are species counts), typically returned by create_abundance_matrix().
site_coords: A data frame with columns site, x, y giving quadrat centroids in the same CRS used for analysis.

include_audit

Logical; if TRUE (default), include a short "Conceptual audit" section based on spesim_audit().

audit_top_n

Integer; number of rows to show for the environmental-filtering audit (ranked by absolute correlation). Default 6.

Value

A single character scalar containing the full report text. No files are written by this function; callers typically append the string to a log or include it in the simulation report sink.

Details

Sections produced:

Environmental Gradients: min/max/range for temperature (deg C), elevation (m), rainfall (mm), with a short pattern note and a list of gradient-responsive species including their optima/tolerances rendered in natural units.
Gradient Correlations: pairwise Pearson correlations among the three gradients and a brief interpretation (orthogonal vs. correlated).
Species Abundance Distribution: ranked counts with percentage and labels for dominant and gradient-responsive species.
Spatial Alpha Diversity: per-quadrat species richness and a compact species list (with counts) for each quadrat.
Diversity Partitioning: mean alpha richness (+/-SE), Shannon's H', Simpson's 1-D, gamma richness, Whittaker and additive beta, mean pairwise Sorensen dissimilarity (computed as binary Bray-Curtis on presence-absence), and simple abundance dispersion metrics.
Spatial Autocorrelation: Pearson correlation between inter-quadrat distances and differences in richness (a Mantel-style proxy) with p-value and interpretation.
Fisher Log-series Validation: RMSE, R^2, max residual between observed ranks and theoretical log-series abundances; compares configured FISHER_ALPHA with fisher.alpha estimated from the data.
Computation Notes: which baseline point-process models were requested for the dominant and other species (SPATIAL_PROCESS_A, SPATIAL_PROCESS_OTHERS), and whether the fast Rcpp engines were used when applicable:
- Thomas: rthomas_bbox_cpp (fast) or spatstat-based fallback
- Strauss: rstrauss_bbox_cpp (fast) or spatstat-based fallback

Internally, this routine relies on base summaries, sf for spatial intersections, and vegan for diversity indices. It avoids tidy-evaluation in favor of explicit column access to keep dependencies minimal within a non-interactive reporting context.

Typical usage

Most users should read the saved report from disk rather than call this function. See read_latest_report() for a convenient helper.

Requirements

The res object must be consistent (all components correspond to the same simulation and coordinate reference system). Missing or empty inputs will lead to sections being populated with NA summaries or informative defaults where possible; irrecoverable inconsistencies raise errors.