Evaluating the sensitivity of process domains for logjams to spatial and temporal sample size in river networks of the Southern Rockies, USA

Modification of river corridors, particularly deforestation and the removal of large wood, has greatly altered the abundance and influences of large wood on most rivers in the temperate latitudes. The conceptual framework of large wood process domains can assist in both directing research and facilitating large wood-related management and restoration in rivers. Large wood process domains are spatially or temporally distinct portions of a river network or region with distinct processes of wood recruitment, transport, and storage. Previous research has shown wood to be unevenly distributed across space and time. We use a dataset of logjam distribution density in 304 spatially distinct reaches of mountain streams in the Colorado Front Range, and up to 11 years of repeat measurements at some reaches, to (i) statistically evaluate whether a priori designated process domains for logjam distribution density are distinctly different and (ii) evaluate the sensitivity of process domain delineations to spatial and temporal sample size. Our results indicate that the major spatial controls on logjam process domains for logjam distribution density in the Southern Rockies are drainage area, reach morphology, and wildfire disturbance history. Greater logjam distribution densities were present in wide reaches and undisturbed catchments. Using subsets of the dataset composed of under 100 reaches created similar results. The relationship between geomorphic and hydrologic characteristics and their ability to describe logjam distribution density was minimally affected when using only a single year of data.

Methods
We analyzed data using R software and packages (R Core Team, 2022). Statistical differences between population medians were assessed with t-tests and the Wilcoxon Rank Sum test when data did not meet the assumptions of normality or equal variance. We used multivariate linear models to describe relationships between spatial characteristics and logjam storage. To select the best variables to describe logjam density, we used the MuMin package in R (Barton, 2018). We explored using a mixed model to reflect the nested nature of the data, but opted for simple regression models to more easily facilitate model iteration. Statistical tests were assessed at the α = 0.05 level for significance.

To address the influence of the number of reaches on the conclusions, we created subsets of the spatially extensive dataset by randomly resampling the complete spatial dataset (n = 304) to create subsets of data of different sample sizes. One hundred random subsamples of the complete spatial dataset were taken at every even sample size from n = 2 to n = 304. Using these subsets, we then repeated pairwise tests and linear model construction. Additionally, we performed pairwise tests on a subset of catchments to illustrate the results of decreasing sample size with regard to the number of catchments.

To address the influence of temporal extent on the results, we reconstructed linear models using the temporally extensive dataset as described in Wohl and Scamardo (2021). To remain consistent with the analyses in Wohl and Scamardo (2021), we used a square root transformation to address the non-normal distribution of logjam distribution density. We built three linear models for confined (floodplain/channel width < 2), partially confined (2 < floodplain/channel width < 6), and unconfined (floodplain/channel width > 6) settings using peak flow in 2013, floodplain to channel width ratio, and reach slope, respectively, to describe the square root of the average jam density over the period of record in three simple linear models. To assess the influence of the length of the period of record, we iteratively removed years from the average jam density to understand how the relationships between predictors and the square root of jam density described by linear models changed with shorter periods of record.

Barton, K. (2018). MuMIn: Multi-Model Inference. R Package Version, 1 (42), 1. https://CRAN.R-project.org/package=MuMIn.

R Core Team (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.

Wohl E, Scamardo JE. 2021. The resilience of logjams to floods. Hydrological Processes 35, e13970. https://doi.org/10.1002/hyp.13970

This research was conducted on the traditional and ancestral homelands of the Arapaho, Cheyenne, and Ute Nations and peoples. This was also a site of trade, gathering, and healing for numerous other Native tribes. Indigenous peoples are original stewards of this land and all the relatives within it.