High-throughput computing provides substantial time savings for landscape and conservation planning

The social and ecological complexity of conservation has increased as a function of human transformation of landscapes, requiring robust decision support tools for planning. Remotely sensed data, available at increasingly fine-grain sizes, combined with powerful computing hardware and a plethora of software packages, provide landscape and conservation planners an opportunity to contribute to the design of future landscapes at local-regional extents. A computing limitation that has largely been accepted by the planning community is a sacrifice of grain size with increasing spatial extent. High-throughput and high-performance computing applications (i.e., “grid computing”, “supercomputing”) expand the potential for large-extent analyses with high-resolution data. We provide three landscape and conservation planning experiments that investigate to what degree high-throughput computing expedites spatial analyses at varying grains and extents. The first two distribute tasks to networked GIS computers: (1) detecting small landforms using fine-grained data over a limited spatial extent and (2) coarse-grained protected areas analysis at a continental-extent. The final experiment uses a supercomputer to run stand-alone software for a multi-step habitat connectivity analysis at moderate resolution and extent. All three experiments demonstrated massive time savings, shifting processing time for intensive landscape analyses from months to hours. We suggest high-throughput computing will improve landscape planning by (1) allowing finer grained analyses at greater extents, (2) reducing time consumed by complex algorithms, and (3) facilitating analyses of model sensitivities. Employing these methods may allow planners to ask and solve more complex ecological and planning questions and more accurately represent pattern and process at multiple scales.