PyCLKDE: A big data‐enabled high‐performance computational framework for species habitat suitability modeling and mapping

Abstract

AbstractSpecies habitat suitability modeling and mapping (HSM) at large spatial scales (e.g., continental, global) and at fine spatiotemporal resolutions helps understand spatiotemporal dynamics of species distributions (e.g., migratory birds). Such HSM endeavors often involve “big” environmental and species datasets, which traditional software tools are often incapable of handling. To overcome the computational challenges facing conducting big data‐involved HSM tasks, this study develops a big data‐enabled high‐performance computational framework to conduct HSM efficiently on large numbers of species records and massive volumes of environmental covariates. As a demonstration of its usability, PyCLKDE was implemented based on the computational framework for flexibly integrating multi‐source species data for HSM. The computing performance of PyCLKDE was thoroughly evaluated through experiments modeling and mapping Empidonax virescens habitat suitability in the continental Americas using high‐resolution environmental covariates and species observations obtained from citizen science projects. Results show that PyCLKDE can effectively exploit computing devices with varied computing capabilities (CPUs and GPUs on high‐end workstations or commodity laptops) for parallel computing to accelerate HSM computations. PyCLKDE thus enables conducting big data‐involved HSM using commonly available computing resources. Using PyCLKDE as an example, efforts are called for to develop similar geocomputation tools based on the proposed framework to realize the potential of effectively performing geospatial big data analytics utilizing heterogenous computing resources on “personal‐grade” computing resources.