sampbias

sampbias quantifies geographic sampling biases in species occurrence data by assessing how human accessibility factors such as proximity to cities, rivers, roads, and airports influence sampling intensity for biodiversity research and conservation.

Key Features:

• Visualization: Visualizes spatial distribution of occurrence records and species to reveal patterns potentially driven by sampling bias.
• Quantification of Biasing Effects: Uses a Bayesian statistical framework to quantify how sampling rates vary with distance from bias factors such as cities, rivers, roads, and airports.
• Comparative Analysis: Enables comparison of biasing effects across different geographic features and across datasets to assess relative impacts of accessibility factors.
• Publication-Level Graphs: Produces graphs that illustrate spatial distributions of sampling bias for use in scientific publications.

Scientific Applications:

• Ecology: Assess how accessibility-driven sampling bias affects ecological inferences from species occurrence data.
• Evolution: Evaluate the impact of sampling bias on studies of species distributions and evolutionary patterns.
• Conservation Biology: Quantify biases that can affect species distribution models and conservation decision-making.
• Biodiversity Assessment and Planning: Inform biodiversity assessments and conservation planning by characterizing spatial sampling effort related to human accessibility.

Methodology:

A Bayesian statistical framework models sampling rates as functions of distance to specified bias factors (cities, rivers, roads, airports) and allows integrated assessment of multiple accessibility features simultaneously.