Problems of geometry, sampling, and scale in gridded biodiversity data, and proposed solutions

Abstract

Grids, and gridded biodiversity data such as regional or country-level atlases, play a prominent role in ecology, particularly in the study of spatial patterns of species occupancy, geographic ranges, biodiversity, and their drivers and temporal dynamics. However, managing, exploring, and analyzing gridded data presents numerous challenges. Here, we review the problems with gridded data, and the existing solutions. We hope to provide guidance, particularly to early-career ecologists who may otherwise struggle to make sense of the various solutions scattered throughout the literature. We focus on grid-specific problems of sampling (e.g. varying sampling method and effort in space and time, imperfect detection), geometry (e.g. varying grid cell area and shape, positional errors), and scale (e.g. spatial grain and temporal extent). Solutions can be implemented before gridding, during data processing, or during data analysis. These include selecting appropriate projections and grid designs, aggregating or filtering grid cells, and accounting for biases within statistical models. By exposing gaps, trade-offs and single solutions to multiple problems across existing approaches, we further aim to inspire the development of novel modelling frameworks that tackle multiple challenges simultaneously.

Florencia Grattarola

Postdoc Researcher

Uruguayan biologist doing research in macroecology and biodiversity informatics.