DOI: 10.5061/dryad.5hqbzkhh5
title: Data from: Evaluating the sensitivity of process domains for logjams to spatial and temporal sample size in river networks of the Southern Rockies, USA

description: 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.
description: 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.<br>
 <br> 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.<br> <br> 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 &lt; 2),
 partially confined (2 &lt; floodplain/channel width &lt; 6), and
 unconfined (floodplain/channel width &gt; 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.
description: # Data from: Evaluating the sensitivity of process domains for logjams to
 spatial and temporal sample size in river networks of the Southern
 Rockies, USA Data describing logjam distribution density (number of jams
 per 100 m of channel length) for the Southern Rocky Mountains. Each reach
 was previously defined. Data were compiled from Beckman (2013), Jackson
 and Wohl (2015), Livers (2016), Triantafillou & Wohl (2024), Wohl and
 Beckman (2014), Wohl and Jaeger (2009), Wohl and Scamardo (2020), Wohl and
 Iskin (2022), and Wohl (unpublished data). ## Description of the data and
 file structure spatial.csv: A csv with the spatially extensive data. Data
 cover 304 reaches from 38 catchments. spatial_key.csv: A table with
 variable definitions associated with 'spatial.csv,' including
 category, variable name, units if applicable, description of methods, and
 format. temporal.csv: A csv with the temporally extensive data. Data are
 from the North Saint Vrain catchment and its tributaries and cover 10
 years of field surveys. temporal_key.csv: A table with variable
 definitions associated with 'spatial.csv' including category,
 variable name, units if applicable, description of methods, and format.
 pairwiseresults_key.csv: A table with variable definitions associated with
 'burn_pairwiseresults.csv', 'elevation_pairwise
 results.csv', and 'morphology_pairwiseresults.csv'.
 burn_pairwiseresults.csv: The results associated with randomly resampling
 the entire 'spatial' dataset to perform pairwise testing to
 determine if there is a difference in logjam density between unburned and
 burned reaches. The burn status and logjam distribution density are
 described in 'spatial.csv' and 'spatial_key.csv.' Data
 were randomly sampled from the full spatial dataset 100 times at every
 even sample size from 10 to 304. elevation_pairwiseresults.csv: The
 results associated with randomly resampling the entire
 'spatial.csv' dataset to perform pairwise testing to determine
 if there is a difference in logjam distribution density between montane
 and subalpine reaches. The elevation zone and logjam distribution density
 are described in 'spatial.csv' and 'spatial_key.csv.'
 Data were randomly sampled from the full spatial dataset 100 times at
 every even sample size from 10 to 304. morphology_pairwiseresults.csv: The
 results associated with randomly resampling the entire
 'spatial.csv' dataset to perform pairwise testing to determine
 if there is a difference in logjam distribution density between bead and
 string reaches. The morphology type and logjam distribution density are
 described in 'spatial.csv' and 'spatial_key.csv.' Data
 were randomly sampled from the full spatial dataset 100 times at every
 even sample size from 10 to 304. lmer_results.csv: The results associated
 with randomly resampling the entire 'spatial.csv' dataset to
 construct linear mixed effects models describing the log-transformed
 logjam distribution density with terms for slope, drainage area,
 elevation, morphology, burn history, and a fixed effect for site. The
 terms are described in 'spatial.csv'
 and 'spatial_key.csv.' Data were randomly sampled from the full
 spatial dataset 100 times at every even sample size from 20 to 304.
 lmer_key.csv: A table with variable definitions associated with
 'lmer_results.csv' including variable name, description, and
 format. 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. # Data sources Beckman N 2013. Crossing a Threshold:
 The Legacy of 19th Century Logging on Log Jams and Carbon Storage in Front
 Range Headwater Streams. PhD dissertation, Colorado State University, Fort
 Collins, CO. Jackson KJ, Wohl E. 2015. Instream wood loads in montane
 forest streams of the Colorado Front Range, USA. Geomorphology 234,
 161-170. Livers Gonzalez B. 2016. Instream wood loads and channel
 complexity in headwater Southern Rocky Mountain streams under alternative
 states. PhD dissertation, Colorado State University, Fort Collins, CO.
 Triantafillou, S, & Wohl, E. (2024). Geomorphic characteristics
 influencing post-fire river response in mountain streams. Geomorphology,
 466, 109446.
 [https://doi.org/10.1016/j.geomorph.2024.109446](https://doi.org/10.1016/j.geomorph.2024.109446) Wohl E, Beckman N. 2014. Controls on the longitudinal distribution of channel-spanning logjams in the Colorado Front Range, USA. River Research and Applications 30, 112-131. Wohl E, Jaeger K. 2009. A conceptual model for the longitudinal distribution of wood in mountain streams. Earth Surface Processes and Landforms 34, 329-344. [https://doi.org/10.1002/esp.1722](https://doi.org/10.1002/esp.1722) Wohl E, Scamardo JE. 2021. The resilience of logjams to floods. Hydrological Processes 35, e13970. [https://doi.org/10.1002/hyp.13970](https://doi.org/10.1002/hyp.13970) Wohl E, Iskin EP. 2022. The transience of channel-spanning logjams in mountain streams. Water Resources Research 58, e2021WR031556. [https://doi.org/10.1029/2021WR031556](https://doi.org/10.1029/2021WR031556)