DOI: 10.5061/dryad.ffbg79czr
title: Watershed, lake and food web factors influence diazotrophic cyanobacteria in mountain lakes

description: Cyanobacterial blooms can occur in freshwater ecosystems largely isolated
 from development and not experiencing extensive cultural eutrophication.
 For example, remote mountain lakes can experience intense blooms of
 diazotrophic (nitrogen-fixing) cyanobacteria caused by factors acting at
 different spatial and temporal scales. In this study, we examined how
 cross-scale interactions among watershed, lake, and food web
 characteristics influence diazotrophic cyanobacteria biovolume in mountain
 lakes. We quantified diazotrophic cyanobacteria biovolume, zooplankton
 abundance, and physico-chemical variables for 29 lakes in the Cascade
 Mountains of Oregon, USA, in summer 2019. Watershed characteristics were
 compiled from historical data sets available for the region. Diazotrophic
 cyanobacteria biovolume ranged across the lakes from 0 to 1,930,000 µm3
 mL-1; Dolichospermum was the most common genus. Random forest models
 showed that 11 watershed, lake, and food web characteristics explained 76%
 of the variance in diazotrophic cyanobacteria biovolume among the sampled
 lakes. Structural equation models suggested that the drainage ratio (i.e.,
 relative area of the lake to the watershed) correlated positively with
 phosphorus concentrations and in turn, with diazotroph biovolume. Among
 lakes, hypolimnetic dissolved oxygen was negatively correlated with
 diazotroph biovolume, possibly due to the release of nutrients, like
 phosphate and iron, bound to sediments. In addition, zooplankton grazers
 were negatively related to diazotrophic cyanobacteria biovolume,
 potentially reflecting the influence of stocked fish.  Thus, lake
 management must account for bottom-up factors, such as nutrient loading,
 which is influenced by lake morphometry and watershed size, as well as
 top-down factors, such as fish stocking, to effectively mitigate
 diazotrophic cyanobacterial blooms.
description: We compiled a data set of 132 Oregon Cascade lakes on fish
 stocking, water chemistry, and watershed characteristics from various
 sources to identify a potential set of study lakes (D. M. Johnson 1985;
 USDA Forest Service 1996; US Environmental Protection Agency 2009; 2016). 
 We only considered lakes &gt;10 hectares and with a maximum depth of
 ≥3 meters to exclude extremely small lakes or ponds that can be seasonally
 variable in depth and emergent vegetation cover (US Environmental
 Protection Agency 2009). We created a representative sample of lakes from
 this dataset using binary regression trees from the R package rpart v.4
 (T. Therneau, Atkinson, and Ripley 2019). A binary regression tree
 repeatedly divides the response data into nodes to reduce variation within
 nodes based on predictor variables. Total P concentration was the response
 variable as P is often a crucial lake characteristic for diazotrophic
 cyanobacteria and varies significantly in the Cascades (D. M. Johnson
 1985; D. P. Morris and Lewis 1988; J. J. Williams et al. 2016). In
 addition, total P was the most commonly available chemical analyte for all
 lakes from the compiled datasets.  We chose elevation, 10-year annual fish
 stocking average rate, and surface area as the predictor variables. From
 the resulting binary regression tree, we selected a similar number of
 lakes from each node to make up the 29 lakes of the study, which were
 sampled in summer 2019 (Appendix S1: Fig. S1).  We
 compiled lake morphometry data from the Atlas of Oregon Lakes database,
 including bathymetry to calculate Schmidt Stability Index (a measure of
 stratification strength), using the R package rLakeAnalyzer v. 11 (D. M.
 Johnson 1985; L. Winslow et al. 2019). We obtained fish stocking records
 from 1978-2019 for the study lakes, including biomass, count, and species
 (Oregon Department of Fish and Wildlife, unpublished). The fish were
 mostly stocked as fingerlings that were &lt; 250 mm long (fork length)
 and thus were primarily planktivores (D. A. Beauchamp 1990; J. J. Elser et
 al. 1995). For many watershed variables, we extracted
 data from the LakeCat dataset, which provides watershed characteristics of
 lakes within the conterminous US derived from existing spatial data such
 as land cover, geology, and long-term climate (Appendix S1, Hill et al.,
 2018). Average slope and aspect of watersheds was derived from Digital
 Elevation Models with 10-m resolution (US Geological Survey 2019). Maximum
 snow-water equivalent (SWE) for each watershed in the previous winter
 (2018-2019) was derived from the Snow Data Assimilation System daily
 estimated SWE, selecting the days based on the maximum SWE at the nearest
 SNOTEL site for each watershed (National Operational Hydrologic Remote
 Sensing Center 2004; USDA Natural Resources Conservation Service 2020).
 Percent change in forest cover in each watershed for the past 20 years was
 derived from the spatial Global Forest Change dataset for 2000-2019
 (Hansen et al. 2013). We calculated drainage ratio for each lake by
 dividing the watershed area by the lake surface area.
 <strong>Lake & food web: field
 sampling</strong>  Each lake (n=29) was sampled
 twice in summer 2019 to capture within season variation, with the first
 sampling occurring between June 24-July 13 and the second between August
 8-31. We measured physical and chemical variables at the deepest spot in
 the lake, including a complete depth profile of temperature and dissolved
 oxygen with a YSI ProODO meter (Yellow Springs, Ohio, USA).  For the
 stratified lakes, the profile data were divided into the thermal layers
 (epilimnion, metalimnion, and hypolimnion) to calculate average epilimnion
 temperature, mixed layer depth (i.e. the bottom of the epilimnion), and
 average hypolimnetic dissolved oxygen. For unstratified lakes, average
 whole water column temperature was used in lieu of epilimnetic temperature
 and the bottom dissolved oxygen value was used in lieu of average
 hypolimnetic dissolved oxygen.  We sampled for
 phytoplankton with a Van Dorn sampler (Wildco, Yulee, Florida, USA) at one
 meter below the surface at the deepest point in the lake, identified using
 bathymetric maps and a depth sounder. Samples were collected in 250-mL
 brown Nalgene bottles and preserved for identification using Lugol’s
 solution.  An additional sample was taken at the deep spot and a known
 volume was filtered onto glass fiber filters (Whatman GF/C, 1.2-m pore
 size) for chlorophyll-a analysis. We collected samples for nutrient
 analyses (total N and P) from the top 5 m of the water column, using a 5-m
 long, 2.54-cm diameter tygon tube and transferred them into 125-mL HDPE
 bottles, which were kept cool and preserved with H2SO4 until frozen in the
 laboratory. We conducted an integrated tow of the water column for
 crustacean zooplankton using a plankton net with 80-µm mesh and 25-cm
 diameter, starting two meters from the bottom and preserving the sample in
 70% ethanol for identification. <strong>Lake
 & food web: Lab analyses</strong>  We
 concentrated preserved phytoplankton samples before counting by gently
 mixing the sample for 5 minutes and then taking a 100-mL subsample for
 settling in a graduated cylinder. After 100 hours of settling, the top 98
 mL were removed via a vacuum pump and reserved to dilute while the
 remaining 2 mL were used for counting. We counted and identified 300
 natural units per concentrated sample in Palmer counting cells to the
 genus level or to the lowest taxonomic level possible, using taxonomic
 guides (G. M. Smith 1950; J. D. Wehr 2002; R. A. Matthews 2016) with a
 Leica DM1000 microscope at 400X and ICC50 HD camera (Leica Microsystems
 Inc., Buffalo Grove, IL). Diazotrophic genera were determined based on
 current literature (Bergman et al. 1997; I. Berman-Frank, Lundgren, and
 Falkowski 2003; Reynolds 2006a). We measured the dimensions of 20
 individuals of each taxon in each sample to calculate biovolume using
 standardized equations based on the shapes of taxa (Appendix S1: Table S4;
 Hillebrand et al., 1999). For zooplankton, we counted and identified 250
 individuals from each preserved sample to the order level for Copepoda and
 to the family level for Cladocera using taxonomic guides (M. D. Balcer,
 Korda, and Dodson 1984; J. H. Thorp and Covich 2014) with a Leica M165C
 microscope at 100X and IC80HD camera. For further analyses, cladocerans
 were aggregated into two groups based on different feeding impacts, as
 Daphnia, large and efficient, and small cladoceran grazers, smaller and
 less efficient (e.g. Bosmina, Holopedium, Ceriodaphnia) (William R. DeMott
 1982; Reynolds 2006a). We extracted chlorophyll-a from
 filters using acetone for 20 hours in a dark refrigerator, and measured
 concentrations using a fluorometer following Arar & Collins
 (1997). We used a persulfate solution to digest total P samples heated to
 100°C and then analyzed with a Shimadzu UV-1800 spectrophotometer (Kyoto,
 Japan) using the molybdenum blue colorimetric method (detection limit:
 0.002 mg/L; precision limit: +/- 0.004 mg/L) (C. J. Patton and Kryskalla
 2003; APHA 2018a). We also used a persulfate solution to digest total N
 samples heated to 100°C and then analyzed with a SmartChem 200 discrete
 analyzer (Guidonia, Italy) for colorimetric determination of nitrate and
 nitrite (detection limit: 0.01 mg/L; precision limit: +/- 0.01 mg/L) (APHA
 2018b).
description: Microsoft Excel, Google Sheets, Apple Numbers, or any CSV reading
 software
description: # The dataset associated with, "Watershed, lake and food web factors
 influence diazotrophic cyanobacteria in mountain lakes" The master
 dataset contains the values by lake of the response variable-diazotrophic
 cyanobacteria biovolume and potential lake (physical/chemical), food web
 and watershed factors. Parameters that were measured in situ in 2019 were
 averaged across between the two sampling bouts. The average biovolume
 dataset contains the mean biovolume for each cell or counting unit
 (filament/colony) by phytoplankton genus or lowest identified taxonomic
 group. The correlation matrix dataset contains the Spearman correlation
 coefficients between each of 38 final predictors for the binary regression
 tree and random forest model. The final set of predictors were reached by
 the following steps: Predictors were removed if they had zero or near zero
 variance based on percent of unique values (&lt;10%) and frequency ratio
 (i.e. frequency of the most common value divided by the frequency of the
 second most common value) (Kuhn, 2008; Kuhn & Johnson, 2013).
 Correlations between predictors were examined using the Spearman’s rank
 coefficient due to non-normal distributions and one predictor of a pair
 was removed if ρ &gt; 0.80. The naming of predictors was different from
 the master file as this matrix was not used extensively in analyses unlike
 the master dataset. --- ## Description of the data and file structure
 Missing data in the master file is due to equipment failure or analysis
 error in the field or lab is coded as 'NA'. Missing data in the
 correlation file is because the matrix is set to only show coefficients on
 one half of the matrix to avoid duplicated coefficients. --- ### Master
 data file: #### ormtn19_master.csv Headers: Lake Lake.Name: categorical,
 lake name Dominant_nxcyan: categorical, dominant genus of diazotrophic
 cyanobacteria if present nxcyan_relbv: continuous, average diazotrophic
 cyanobacteria relative biovolume nxcyan_tbvl: continuous, average
 diazotrophic cyanobacteria biovolume by cubic micrometers per mL
 Elevation: continuous, elevation of the lake relative to sea level in m
 avg_depth: continuous, average depth of the lake in m max_depth:
 continuous, maximum depth of the lake in m volume_chm: continuous, volume
 of the lake in cubic hectometers SA_ha: continuous, surface area of lake
 in ha tp_mean: continuous, average total phosphorus concentration in mg/L
 tn_mean: continuous, average total nitrogen concentration in mg/L
 tmp_mean: continuous, average epilimnetic temperature in degrees Celsius
 cd_mean: continuous, average epilimnetic specific conductance in
 microSiemens/cm pH_mean: continuous, average epilimnetic pH do_epi_mean:
 continuous, average epilimnetic dissolved oxygen in mg/L hyp_do_mean:
 continuous, average hypolimnetic dissolved oxygen in mg/L chla_mean,
 continuous, average chlorophyll-a concentrations in mg/L zm_mean:
 continuous, average mixed layer depth in m ze_mean: continuous, average
 photic depth in m Lat: continuous, latitude in decimal degrees Long:
 continuous, longtitude in decimal degrees ssi_jm2: continuous, Schimdt
 Stability Index in joules/m squared est_fetch_m: estimated lake fetch in m
 10yr.avg.kg.fish.stocked.per.ha: continuous, 10 year average stocked fish
 biomass per ha (2010-2019) Calanoida: Mean abundance of adult calanoid per
 L Clad_pred: Mean abundance of adult predatory cladoceran per L
 Copepod_nauplii: Mean abundance of copepod nauplii per L Cyclopoida: Mean
 abundance of adult cyclopoid per L Daphnia: Mean abundance of adult
 Daphnia per L Sm_clad_grazers: Mean abundance of adult grazing cladoceran
 that are not Daphnia per L Watershed level data areawstd_ha: continuous,
 area of the watershed in ha wtshd_lkarea: continuous, drainage ratio as
 watershed area relative to lake area PctAlkIntruVolWs: continuous, percent
 of the watershed lithology classified as alkaline volcanic rock
 PctSilicicWs: continuous, percent of the watershed lithology classified as
 silicic rock PctExtruVolWs: continuous, percent of the watershed lithology
 classified as extrusive volcanic rock PctGlacTilCrsWs: continuous, percent
 of the watershed lithology classified as coarse glacial till
 PctGlacLakeFineWs: continuous, percent of the watershed lithology
 classified as fine-textured glacial lake sediment PctWaterWs: continuous,
 percent of the watershed lithology classified as water PctOw2011Ws:
 continuous, percent of the watershed landcover classified as open water
 PctIce2011Ws: continuous, percent of the watershed landcover classified as
 ice/snow PctUrbTotal2011Ws: continuous, percent of the watershed landcover
 classified as urban/developed PctBl2011Ws: continuous, percent of the
 watershed landcover classified as barren or exposed bedrock, desert
 pavement, scarps, talus, slides, volcanic materical, glacial debris, sand
 dunes, strip mines, gravel pits, and other accumulations of earthen
 material PctConif2011Ws: continuous, percent of the watershed landcover
 classified as coniferous forest PctShrb2011Ws: continuous, percent of the
 watershed landcover classified as shrub PctGrs2011Ws: continuous, percent
 of the watershed landcover classified as grass PctWdWet2011Ws: continuous,
 percent of the watershed landcover classified as woody wetland
 PctHbwet2011Ws: continuous, percent of the watershed landcover classified
 as herbaceous wetland BFIWs: continuous, base flow index which is the
 portion of streamflow attributed to groundwater discharge Precip8110Ws:
 continuous, 30 year average annual precipitation for the watershed in mm,
 1981-2011 Tmax8110Ws: continuous, 30 year maximum annual air temperature
 for the watershed in degrees Celsius Tmean8110Ws: continuous, 30 year
 average air temperature for the watershed in degrees Celsius
 NH4_2018Ws:2018 average of precipitation-weighted deposition for ammonium
 for the watershed NO3_2018Ws: 2018 average of precipitation-weighted
 deposition for nitrate for the watershed east_mean: Mean eastness
 [sin(aspect)]of the watershed north_mean: Mean northness [cos(aspect)] of
 the watershed slope_mean: Mean slope of the watershed forloss_s00: Mean
 forest cover loss for 2000-2019 for the watershed forloss_s15: Mean forest
 cover loss for 2015-2019 for the watershed med_maxSWE_19_m: median maximum
 snow-water equivalent (SWE) for the winter of 2018-2019 for the watershed
 ### Average biovolume file: #### chp1_avgBV.csv Headers: Genus:
 categorical, name of phytoplankton genus or the lowest level of possible
 identified taxonomy(such as chlorophyte) Avg biovolume: continuous,
 average biovolume by cubic micrometers per mL for each genus/taxonomic
 group ### Spearman correlation matrix of predictors file: ####
 corr_pred38_jansen61623.csv Headers: Elevation: continuous, elevation of
 the lake relative to sea level in m Max depth: continuous, maximum depth
 of the lake in m Surface area: continuous, surface area of lake in ha
 Total P: continuous, average total phosphorus concentration in mg/L Total
 N: continuous, average total nitrogen concentration in mg/L Epilimnetic
 temperature: continuous, average epilimnetic temperature in degrees
 Celsius Sp. Conductance: continuous, average epilimnetic specific
 conductance in microSiemens/cm Mixed layer depth: continuous, average
 mixed layer depth in m Latitude: continuous, latitude in decimal degrees
 Longitude: continuous, longitude in decimal degrees Stocked fish biomass:
 continuous, 10 year average stocked fish biomass per ha (2010-2019) %
 Alkaline instrusive volcanic: continuous, percent of the watershed
 lithology classified as alkaline volcanic rock % Silicic: continuous,
 percent of the watershed lithology classified as silicic rock % Extrusive
 volcanic: continuous, percent of the watershed lithology classified as
 extrusive volcanic rock % Coarse glacial till: continuous, percent of the
 watershed lithology classified as coarse glacial till Drainage ratio:
 continuous, drainage ratio as watershed area relative to lake area % Ice
 cover: continuous, percent of the watershed landcover classified as
 ice/snow % Total developed area: continuous, percent of the watershed
 landcover classified as urban/developed % Coniferous forest cover:
 continuous, percent of the watershed landcover classified as coniferous
 forest % Shrub cover: continuous, percent of the watershed landcover
 classified as shrub % Grass cover: continuous, percent of the watershed
 landcover classified as grass % Woody wetland cover: continuous, percent
 of the watershed landcover classified as woody wetland %
 Herbaceous/emergent wetland cover: continuous, percent of the watershed
 landcover classified as herbaceous/emergent wetland Base-flow index:
 continuous, base flow index which is the portion of streamflow attributed
 to groundwater discharge Annual precipitation: continuous, 30 year average
 annual precipitation for the watershed in mm, 1981-2011 Maximum air
 temperature: continuous, 30 year maximum annual air temperature for the
 watershed in degrees Celsius Mean eastness: Mean eastness [sin(aspect)]of
 the watershed Mean northness: Mean northness [cos(aspect)] of the
 watershed Mean slope: Mean slope of the watershed Calanoida: Mean
 abundance of adult calanoid per L Cyclopoida: Mean abundance of adult
 cyclopoid per L Daphnia: Mean abundance of adult Daphnia per L Small
 cladoceran grazers: Mean abundance of adult grazing cladoceran that are
 not Daphnia per L Copepod nauplii: Mean abundance of copepod nauplii per L
 Mean forest loss 2000-19: Mean forest cover loss for 2000-2019 for the
 watershed Mean forest loss 2015-19: Mean forest cover loss for 2015-2019
 for the watershed Maximum snow water equivalent: median maximum snow-water
 equivalent (SWE) for the winter of 2018-2019 for the watershed
 Hypolimnetic dissolved oxygen: continuous, average hypolimnetic dissolved
 oxygen in mg/L ## Sharing/access Information Data is also available upon
 request from the corresponding author:
 /larasj67@gmail.com ## Derived data for lake
 variables Atlas of Oregon Lakes ()
 provided the bathymetry data to calculate mean depth and Schmidt Stability
 index. ## Derived data sources for watershed variables LakeCat by the
 Environmental Protection Agency
 (link:) provided: watershed area, lithology, land cover, PRISM climate averages, base-flow index, atmospheric deposition estimates of nitrogen Hansen/UMD/Google/USGS/NASA () provided: forest cover loss for 00-19 and 15-19 National Operational Hydrologic Remote Sensing Center () & USDA Natural Resources Conservation Service () provided the data for estimating maximum SWE by watershed.