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Abstract Intermittent streams are globally ubiquitous and represent a large percentage of stream networks. As climate change in many arid regions increases the frequency and intensity of drying disturbances, it is important to understand how aquatic biota will respond to such disturbances and how it would impact aquatic biodiversity. To address these topics, we sampled 10 stream reaches in the Sycamore Creek basin, an arid‐land stream in central Arizona (USA), with reach‐scale flow regimes ranging from perennial to highly intermittent. We sampled aquatic macroinvertebrates during 4 seasons to explore seasonal variability in community structure through flowing and drying phases. We also collected continuous flow data with remote data loggers to explore the impacts of intermittency and distance to perennial refuges on species richness, taxonomic composition and trait composition. Overall, richness was lower at intermittent reaches than perennial reaches, and richness values increased linearly as flow duration increased. We found no relationship between richness and distance to the nearest perennial refuge. Community assemblages differed significantly by season but were not distinct between perennial and intermittent reaches. Trait composition was also distinct between seasons and flow regimes, with traits such as a lack of diapause, longer life span and predatory feeding behaviours being indicators for perennial reaches. As climate change alters natural flow regimes, understanding the responses of macroinvertebrate community structure to drying disturbances in arid‐land streams can provide insight on aquatic community responses to climate change at larger scales.
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A Bifrost Accelerated Intermittent Small Baseline Subset Analysis Pipeline for InSAR Ground Deformation
The Intermittent Small Baseline Subset approach to Interferometric Synthetic Aperture Radar data was originally devised as a way to recover information from regions with intermittent coherence, making it particularly useful in agricultural regions or those featuring significant vegetation. However, as modern data products grow in size, the increased computational complexity that this methodology demands makes processing more daunting. Here, we present a solution: leveraging the Bifrost data processing framework and GPUs, we analyze Sentinel-1 data covering a large region of northern California and are able to achieve dramatic speed-ups on the order of 300–400 times faster than CPU-bound implementations of ISBAS, processing the entire dataset in only 5 h.
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- Award ID(s):
- 2103707
- PAR ID:
- 10633950
- Editor(s):
- Balz, T
- Publisher / Repository:
- Remote Sensing
- Date Published:
- Journal Name:
- Remote Sensing
- Volume:
- 16
- Issue:
- 14
- ISSN:
- 2072-4292
- Page Range / eLocation ID:
- 2554
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/rs16142554
