The Efficacy of Beaver Mimicry Restoration Techniques on Riparian Vegetation Vigor in Central Oregon

Abstract

Anthropogenic disturbances have adversely affected riparian habitat and accelerated stream incision across rangeland systems of the Western United States. There has been mounting interest in mitigation efforts to accelerate ecosystem recovery, including the development of beaver mimicry structures known as beaver dam analogs (BDAs). I examined a time-series analysis of five sites of stream-adjacent riparian vegetation using remote sensing techniques, imagery from the National Agricultural Imagery Program (NAIP), and the Normalized Difference Vegetation Index (NDVI) over an eight-year period before and after beaver dam analog construction along a segment of the Crooked River in Central Oregon. I hypothesized that post-restoration NDVI values would be higher than pre-restoration NDVI values in areas upstream of BDA complexes due to increased water impoundment generated by BDA structures and, thus, increased water accessible to adjacent riparian vegetation. Results from this study, however, revealed variable trends in NDVI values pre- and post- construction of BDA complexes compared with NDVI values from control locations absent of BDA development. For each complex, there was a consistent increase in NDVI values observed after the initial construction. However, in subsequent years, NDVI values declined for complexes 1 and 2 by 51% and 26% respectively between 2016-2020, were unchanged for complex 3, decreased by 50% in 2020 and increased by 120% in 2022 for complex 4, and consistently increased for complex 5 by 45% from 2016-2022. Possible explanations for variability in results include individual BDA structural failure resulting from high flows throughout the course of my study period. Recommendations for future studies include the use of higher resolution imagery from unoccupied aerial vehicles (UAVs) to monitor fine-scale nuances in riparian vegetation productivity, incorporating supplementary vegetation indices, and applying spatial covariates to strengthen statistical analyses and improve understanding of outcomes.