Spatiotemporal biodiversity data of various forms are accumulating rapidly in near real-time. They include: incidental museum or citizen science point observations, survey data of varying protocols, sensor-based inventories, animal movement/tracking data, among others. Notably, these data greatly differ in their spatial grains (e.g. from meters to many kilometers), temporal grains (from minutes to months), and their associated spatiotemporal uncertainties. A vast and growing set of global remotely-sensed and other environmental data offer tremendous opportunities for linking these biodiversity data to environmental data flows (‘environmental annotation’). However, ad hoc fusion of such heterogeneous biodiversity data with environmental data can lead to unreliable results, and even mischaracterizations of the environmental signatures of the biodiversity data.
Results/Conclusions
We present a new platform for the precise and flexible fusion of spatiotemporal biodiversity data and environmental data for ecological analysis. Our platform is the first to provide users with customizable spatial and temporal buffers around input biodiversity records. These buffers can be used to adjust the effective grain size and/or uncertainty of heterogeneous biodiversity data, giving scientists the power to integrate data from heterogeneous biodiversity data sources in a scientifically rigorous way. Our platform additionally offers key datasets for environmental annotation at higher spatial and temporal resolutions than have been previously available in comparable tools. These include global MODIS imagery at a daily time interval, and global Landsat imagery at 30m resolution. We also provide EarthEnv and Chelsa products for annotation for the first time. Our platform will be available to the public free of charge through a webtool and an R package, and will be hosted via Map of Life (mol.org), joining a suite of other tools for the aggregation and analysis of global biodiversity data.