An R-Based Adaptive Quadtree Spatial Tiling Workflow for Boundary-Exact GBIF Species Occurrence Mining within User-Defined KML Polygons

The paper introduces an R-based adaptive quadtree spatial tiling workflow for mining Global Biodiversity Information Facility (GBIF) species occurrence data within user-defined KML polygons. Traditional GBIF API spatial queries are limited to rectangular envelopes or WKT polygons, which do not accommodate irregular regions of interest such as protected areas, wetlands, or municipal boundaries.

This paper presents an open, self-contained protocol that resolves this limitation by ingesting any KML polygon, breaking it into rectangular tiles compatible with the GBIF API, querying, cleaning, and clipping records, and summarizing the inventory with a diversity-completeness-rarefaction module. The protocol employs an iterative quadtree refinement algorithm that adapts tile numbers, sizes, and locations based on the target polygon's geometry.

It also includes a fault-tolerant pagination/retry query system, boundary-exact two-step clipping, and a Chao1-based completeness assessment to ensure statistical comparability between sites. The entire protocol is implemented in open R source, with parameters for initial cell size, area floor, tile overlap threshold, and recursion limit.

Validation on two distinct polygons—Bishnupur and Sonamukhi Sal forests—demonstrated comparable tiling and diversity metrics despite differences in size and complexity. The protocol has the potential to address general bioinformatic challenges in polygon-based GBIF queries, providing a reusable and documented method for future use.