Watershed, lake and food web factors influence diazotrophic cyanobacteria in mountain lakes
Data files
Feb 25, 2024 version files 34.27 KB
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chp1_avgBV.csv
1.62 KB
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corr_pred38_jansen61623.csv
7.43 KB
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ormtn19_master.csv
14.34 KB
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README.md
10.89 KB
Abstract
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.
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 (<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 > 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: ljansen@pdx.edu/larasj67@gmail.com
Derived data for lake variables
Atlas of Oregon Lakes (https://oregonlakesatlas.org/map) 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:https://www.epa.gov/national-aquatic-resource-surveys/lakecat-dataset) provided: watershed area, lithology, land cover, PRISM climate averages, base-flow index, atmospheric deposition estimates of nitrogen
Hansen/UMD/Google/USGS/NASA (https://earthenginepartners.appspot.com/science-2013-global-forest/download_v1.7.html) provided: forest cover loss for 00-19 and 15-19
National Operational Hydrologic Remote Sensing Center (https://nsidc.org/data/g02158/versions/1) & USDA Natural Resources Conservation Service (https://www.nrcs.usda.gov/wps/portal/wcc/home/snowClimateMonitoring/snowpack/) provided the data for estimating maximum SWE by watershed.
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 >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 < 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.
Lake & food web: field sampling
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.
Lake & food web: Lab analyses
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).
Microsoft Excel, Google Sheets, Apple Numbers, or any CSV reading software
- Jansen, Lara S.; Sobota, Daniel; Pan, Yangdong; Strecker, Angela L. (2024). Watershed, lake, and food web factors influence diazotrophic cyanobacteria in mountain lakes. Limnology and Oceanography. https://doi.org/10.1002/lno.12523