An automated analytical system to synthesize environmental data from long-term remote sensors for use in animal migration ecology
November 2020 - December 2022
- US Geological Survey
The seasonality of Earth results in a tapestry of resource waves across space and time. Most notable are the waves of snowmelt and plant green-up moving altitudinally and latitudinally in temperate ecosystems each spring. Migratory animals capitalize on resource waves by fine-tuning their movements with the ebb and flow of resources. With increasing variation in weather and temperature patterns, the consistency of phenological events, and hence resource waves, will inevitably change in complex ways. Our aim is to develop an automated analytical system that utilizes high-end computing to calculate derived metrics of environmental predictability – such as timing of green-up, brown-down, snow melt, winter severity, etc. – from relatively long-term remote sensors and algorithms (e.g., MODIS, SNODAS, PRISM). We plan to use derived metrics to 1) provide automated data products (e.g., maps of winter severity over time) to stakeholders such as state agencies, and 2) develop an empirical understanding of the link between environmental predictability and big game migration.