Intact riverscape floodplains promote water security, increase water supply late in the growing season, mitigate flooding and wildfire, and serve as biological hotspots. These systems often have intact macroinvertebrate communities, an important pillar of riverscape function. However, many of the riverscapes in the Western U.S. are in poor ecological condition and have degraded macroinvertebrate communities. Remotely sensed time series information captures dense seasonal variability that would be nearly impossible to recreate with in-situ monitoring. We begin linking remote sensing to in-situ measurements in order to build a more complete picture of landscape dynamics, resulting in better management decisions and improved watershed health.
This study leverages remote sensing to address two critical knowledge gaps by: (1) improving the use of remote sensing indicators as proxies for field-based ecological monitoring, and (2) deepening our understanding of how macroinvertebrate assemblages are shaped by riverscape conditions. We ask: Can remotely sensed indicators of riverscape structure, when integrated with large-scale field sampling, improve our understanding of biota–environment relationships?
We analyze two major datasets: (1) MRRMaid, a Landsat-based tool that classifies mesic vegetation across the growing season and (2) BLM AIM benthic macroinvertebrate (BMI) data from 2164 sites across the Sagebrush Biome. In order to effectively use remote sensing data in our model, we develop distinct “signatures” of how mesic vegetation is expressed across the landscape at different spatiotemporal scales. Specifically, our signatures are: seasonal minimums, maximums, and the coefficient of variation of mesic vegetation across riparian buffers at three spatial scales. We use generalized linear mixed effects models to examine associations between these signatures and taxonomic shifts in over 1.6 million BMI’s.
I hypothesize that in systems where all three signatures co-indicate a pronounced water regime [Wet stable, Variable, Dry stable] these signatures will be highly correlated with BMI assemblage. By integrating high-resolution remote sensing data with broad-scale field monitoring efforts, this study aims to refine our understanding of the drivers of BMI assemblage. This study offers a scalable, replicable workflow for combining remote sensing and field data, expanding the toolkit for ecological monitoring and informing effective riverscape management.