fauxcurrence

fauxcurrence generates simulated multi-species occurrence data to produce null models for species distribution modeling (SDM) and biogeographic analyses while accounting for spatial autocorrelation and sampling biases.

Key Features:

• R package: Implemented as an R package providing functions for simulation and analysis.
• Multi-species simulation: Supports generation of randomized occurrence points for multiple species simultaneously, incorporating both within-species and between-species spatial structure.
• Spatial-structure algorithm: Implements an algorithm that mimics the spatial structure of real datasets across geographic domains.
• Randomisation of occurrences: Generates randomized occurrence points that replicate observed within-species distances and between-species spatial relationships.
• Accounts for spatial autocorrelation and sampling bias: Produces null expectations that explicitly consider spatial autocorrelation and sampling biases affecting geographic patterns.
• Computational efficiency: Operates efficiently across varied datasets to enable large-scale null model generation.

Scientific Applications:

• Testing species distribution models (SDMs): Generates null expectations to assess whether observed SDM fits are better than expected by chance.
• Evaluating niche overlap and divergence: Assesses niche overlap and niche divergence among species to distinguish meaningful ecological or evolutionary patterns from spatial artifacts.
• Comparative biogeographic analyses: Enables comparative analyses by preserving between-species spatial relationships when simulating occurrence data.

Methodology:

Generates randomized occurrence points that replicate observed spatial structure by maintaining within-species distances and between-species spatial relationships using an algorithm that mimics real datasets' spatial patterns across geographic domains.