DOI: 10.6084/m9.figshare.20362386.v1
title: Data: Random habitat survey

description: DATA & FILE OVERVIEW File List: Data: randhab.withNREI.csv HSCinput.csv Code: McLaren_microscale.R McLaren_powerAnalysisSims.R McLaren_macroscale.R Metadata: McLaren_RandomHabitatSurvey_Readme.txt METHODOLOGICAL INFORMATION Description of methods used for collection/generation of data: To assess reach-scale effects of submerged macrophytes on habitat availability for fish, we used a random-point physical habitat survey. <br> First, we extracted flow (m3·s-1) from the USGS national water information system (http://waterdata.usgs.gov/nwis/) and temperature (C) and dissolved oxygen (mg·l-1) from the nearest multiparameter water quality monitoring sonde operated by the Henry’s Fork Foundation (https://henrysforkdata.shinyapps.io/scientific_website/), respectively. <br> We next used a geographic information systems (GIS) program to select 30 random geolocated points in each reach, for a total of 60 points per location. The number of sample points to take was determined through a statistical power analysis in "McLaren_powerAnalysisSims.R". We collected submerged macrophyte cover, height, water depth, velocity, and substrate at each location. We estimated submerged macrophyte coverage (%) with a 0.25 m2 plexiglass viewbox with a 1/16 grid. We used a decimeter top-set wading rod to measure water depth (m) and maximum submerged macrophyte growth height above the substrate (m) visible in the viewbox window. We later calculated relative submerged macrophyte growth height as a percentage of depth (%). We measured flow velocity (m·s-1) at 20%, 60%, and 80% depth with a Hach Corporation Marsh-McBirney electronic flowmeter or an anemometer-style analog flowmeter. We estimated substrate size (mm) by a 50-particle random subsample of a scoop of sediment collected by a spade. <br> After completing the snorkeling and physical habitat surveys, we estimated invertebrate drift density (mg⋅m-3) for each reach within each season using standard equipment and methods (Baxter et al. 2017). We collected three replicates from three equally spaced 250 μm mesh, 0.25 m2 opening drift nets at each reach for a total of 9 samples per reach and season. The National Aquatic Monitoring Center at Utah State University identified invertebrates to genus and sized to the nearest mm. invertebrate genus and sizes were converted to biomass using genus- and family-specific length-biomass curves in Benke et al. (1999). We estimated the volume of water sampled by each net at each sampling event by recording and linearly interpolating water velocity in each net every minute using a Marsh-McBirney flowmeter. Time to 70% filtration efficiency determined maximum sampling soak times for each replicate net, which were 15 minutes in the Tailwater locations and 30 minutes at the Middle and Headwater locations (Baxter et al. 2017). We calculated invertebrate drift density (mg·m-3) for each season and reach from biomass (mg) and water volume (m3). Methods for processing the data: Pre-processing involved calculating an average velocity for each datapoint by averaging the velocities at the 20% of depth and 80% of depth positions, or as u60 only, depending on data availability. We also calculated Chezy's Coefficient (a function of the flow Reynolds Number -Re- and the relative roughness -ε/R- of the channel) and the relative height of macrophyte growth relative to the total depth of the channel (macrophyte growth height/depth) to express the effect of macrophytes on the hydraulics of the channel. These variables were not used in the final analysis, but may be of interest to others. The distance of each datapoint from the shoreline was calculated using GIS tools available in QGIS. NREI values (j·s-1) were calculated using the BioenergeticsHSC program (Naman et al. 2020). The BioenergeticsHSC program has batch processing methods that allowed us to feed the program the necessary data from the randhab.withNREI.csv dataset, the Henry's Fork Foundation data website: https://henrysforkdata.shinyapps.io/scientific_website/, and from the HSCinput.csv dataset, which contains necessary invertebrate drift data. For each randomly-sampled datapoint, we calculated three NREI values, one for large (450 mm), medium (250 mm), and small (100 mm) fish. From these values, we could calculate a maximum, minimum, and average NREI, as well as determine which size category of fish would perform best at each location. Data required for NREI calculations included: temperature (C), dissolved oxygen (mg·l-1), water depth (m),nvelocity (mg·l-1), substrate size or macrophyte growth height (whichever was larger, mm), turbidity (NTU), and invertebrate drift information. Invertebrate drift information is contained within the "HSCinput.csv" dataset, and is organized into rows by invertebrate genus and size (1 mm bins). Each row contains the density in mg·m-3, and the regression equations from Benke et al. 1999 to calculate dry mass. Energy density remains at the preset default of 5200. This data is fed in various forms to the BioenergeticsHSC GUI, manual available by Naman et al. (2020). <br> To assess the extent to which submerged macrophytes affected fish habitat availability on the reach scale, we started by testing across-season and location variability in physical conditions with a two-way analysis of variance (ANOVA) with a Bonferroni’s correction to obtain a nine-test family error rate of α = 0.05. Using data from the randomized physical habitat surveys, we tested for differences in mean habitat characteristics across seasons (spring, summer, fall) locations (Headwaters, Middle, Tailwaters), and season-location combinations. To meet ANOVA assumptions, we transformed the following variables: depth (loge), velocity (square root), median substrate (loge), submerged macrophyte cover (logit) and NREI (loge). Based on the results of this analysis, we treated the nine season-location group means of the habitat variables as a sample of independent observations and tested for significant pairwise Pearson correlations between submerged macrophyte cover, flow, water temperature, depth, velocity substrate particle size, and NREI. <br> To assess dependence of physical habitat conditions on submerged macrophyte cover, we used mixed-effects multimodel inference based on Akaike’s information criterion (Burnham and Anderson 2002; Gelman and Hill 2007). We used mixed-effects models to accurately account for our study design, which included multiple measures across grouping variables—location and season—that were inherently different in discharge and temperature. Discharge and temperature influence NREI, water depth, and velocity, but we were only interested in the effect of submerged macrophytes on these variables. Thus, we set the random effects as location and season. After setting random effects, we used a hypothesis-driven approach whereby we assumed all physical and biotic habitat variables synergistically interact to create unique habitats within each reach. To understand the role of submerged macrophyte coverage within this synergistic structure, we fit “full” models with all predictors and interaction terms. Since physical and biotic habitat variables interact and synergistically create the physical and biotic environment, we found it best to compare and contrast the results of each of the two hypothesis-driven modelling exercises to understand how submerged macrophyte coverage interacts with other physical and biotic habitat variables. Citations: Baxter, C. V., Kennedy, T.A., Miller, S.W., Muehlbauer, J.D., and Smock, L.A. 2017. Macroinvertebrate drift, adult insect emergence and oviposition. Methods Stream Ecol. Third Ed. 1: 435–456. doi:10.1016/B978-0-12-416558-8.00021-4. Benke, A.C., Huryn, A.D., Smock, L.A., and Wallace, J.B. 1999. Length-mass relationships for freshwater macroinvertebrates in North America with particular reference to the Southeastern United States. J. North Am. Benthol. Soc. 18(3): 308-343. doi: 10.2307/1468447 Burnham, K.P., and Anderson, D.R. 2002. Model selection and multimodel inference, a practical information-theoretic approach. In 2nd edition. Springer-Verlag, New York. Gelman, A., and Hill, J. 2007. Data analysis using regression and multilevel/hierarchical models. Cambridge University Press, Cambridge, United Kingdom. Naman, S.M., Rosenfeld, J.S., Neuswanger, J.R., Enders, E.C., Hayes, J.W., Goodwin, E.O., Jowett, I.G., and Eaton, B.C. 2020. Bioenergetic habitat suitability curves for instream flow modeling: introducing user-friendly software and its potential applications. Fisheries 45(11): 605–613. doi:10.1002/fsh.10489. See <strong>McLaren_RandomHabitatSurvey_Readme.txt for dataset-specific information and expanded metadata</strong>