U.S. Department of the Interior

U.S. Geological Survey

Open-File Report 2016–1175 

Version 1.1, October 2016

Prepared in cooperation with the Bureau of Reclamation

Benthic Processes Affecting Contaminant Transport 

in Upper Klamath Lake, Oregon

By James S. Kuwabara, Brent R. Topping, James L. Carter, Rick A. Carlson, Francis Parchaso, Steven V. 

Fend, Natalie Stauffer-Olsen, Andrew J. Manning, and Jennie M. Land
Cover. Sunrise over Upper Klamath Lake, fall 2015. Photograph by Francis Parchaso of the U.S. 

Geological Survey. 
Prepared in cooperation with the Bureau of Reclamation

Benthic Processes Affecting Contaminant Transport 

in Upper Klamath Lake, Oregon

By James S. Kuwabara, Brent R. Topping, James L. Carter, Rick A. Carlson, Francis Parchaso, Steven V. 

Fend, Natalie Stauffer-Olsen, Andrew J. Manning, and Jennie M. Land

Open-File Report 2016–1175 

Version 1.1, October 2016

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Director

U.S. Geological Survey, Reston, Virginia

First release: 2016

Revised: October 2016 (ver. 1.1)

For more information on the USGS—the Federal source for science about the Earth,

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Suggested citation:

Kuwabara, J.S., Topping, B.R., Carter, J.L., Carlson, R.A., Parchaso, F., Fend, S.V., Stauffer-Olsen, N., 

Manning, A.J., Land, J.M., 2016, Benthic processes affecting contaminant transport in Upper Klamath 

Lake, Oregon (ver. 1.1, October 2016): U.S. Geological Survey Open-File Report 2016–1175, 103 p.,

http://dx.doi.org/10.3133/ofr20161175. 

ISSN 2331-1258 (online)
iii

Acknowledgments

The authors are grateful for critical logistical support from E. Janney and other personnel from the U.S. 

Geological Survey (USGS) Field Office in Klamath Falls, Oregon, and from the USGS Oregon Water 

Science Center. Insightful reviews by M. Gannett and T. Wood are much appreciated, as are hydraulic 

conductivity determinations by K. Halford. Participation in the design, implementation, and analyses of field 

studies by R. Anduaga, M. Berry, J. Brotherton, S. Burdick, R. Carlson, J. Crauder, K. Doughty, D. 

Eldridge, S. Eldridge, A. Englestadt, M. Green, M. Hornberger, J. Kissock, M. Kritzer, J. Land, C. McPhail, and 

G. Schanzenbacher, and T. Short was critical to its successful completion. J. Constantz, H. Essaid, P. Hsieh, 

R. Niswonger, and D. Rosenberry provided valuable consultation on methods to examine hydrologic 

interactions between the lakebed and overlying water column in Upper Klamath Lake. J. La Marche 

(Oregon Water Resources Department), M. Skinner (Klamath Tribes), and J. Gronberg (USGS California 

Water Science Center) are thanked for help accessing tributary discharge data. The Klamath Falls Office of 

the U.S. Bureau of Reclamation (Interagency Funding Agreement Number R13PG20052), the USGS Toxic 

Substances Hydrology Program, the USGS Water Availability and Use Program, USGS National Water 

Quality Program, and the National Research Program within the USGS Water Mission Area are also 

acknowledged for sustained support of this work. 
iv

Contents

Executive Summary....................................................................................................................................... 1

Introduction.................................................................................................................................................... 3

Potential Management Implications ........................................................................................................... 3

Background................................................................................................................................................ 4

Objectives .................................................................................................................................................. 6

Results and Discussion ................................................................................................................................. 7

2013—Spatial Variability Study.................................................................................................................. 7

Ancillary Parameters in 2013.................................................................................................................. 7

Benthic Flux of Macronutrients in 2013 .................................................................................................. 8

Dissolved (0.2-μm filtered) Nutrients in the Water Column in 2013...................................................... 10

Benthic Flux of Trace Metals in 2013 ................................................................................................... 11

Dissolved (0.2-μm Filtered) Trace Elements in the Water Column in 2013.......................................... 13

Benthic Chlorophyll in 2013.................................................................................................................. 14

Benthic Invertebrate Assemblages in 2013 .......................................................................................... 15

2014—Temporal Variability (Weekly Interval) Study................................................................................ 16

Ancillary Parameters in 2014................................................................................................................ 16

Benthic Flux of Macronutrients in 2014 ................................................................................................ 17

Dissolved (0.2-μm Filtered) Nutrients in the Water Column in 2014 .................................................... 18

Benthic Flux of Trace Metals in 2014 ................................................................................................... 20

Dissolved (0.2-μm Filtered) Trace Elements in the Water Column in 2014.......................................... 22

Water-Column Chlorophyll in 2014....................................................................................................... 23

Benthic Invertebrate Assemblages in 2014 .......................................................................................... 24

Invertebrate Composition Comparison Between 2013 and 2014 ............................................................. 24

Invertebrate Composition Comparison to Previous Studies ..................................................................... 25

Biological Solute Efflux—Soluble Reactive Phosphorus (2011–2015)..................................................... 25

2015—Physical (Advective) Solute Transport and Particle Dynamics ..................................................... 27

Physical (Advective) Solute Transport.................................................................................................. 27

Organic Aggregation and Settling in Upper Klamath Lake ................................................................... 28

Overview of Particle Aggregation and Settling ..................................................................................... 29

Particle Aggregation and Settling Results ............................................................................................ 31

Methods....................................................................................................................................................... 38

Biological Parameters .............................................................................................................................. 40

Benthic Chlorophyll a ........................................................................................................................... 40

Benthic Invertebrate Sampling ............................................................................................................. 40

Benthic Invertebrate Sample Processing.............................................................................................. 40

Benthic Invertebrate Nutrient Release Experiments............................................................................. 41

Chemical Parameters............................................................................................................................... 41

Dissolved Nutrients .............................................................................................................................. 41

Dissolved Organic Carbon (DOC) ........................................................................................................ 41

Dissolved Trace Elements.................................................................................................................... 42

Physical Parameters ................................................................................................................................ 42

Heat Transfer Modeling........................................................................................................................ 42

Vertical Hydraulic Gradient................................................................................................................... 43

Flocculation and Settling Methodology................................................................................................. 44

References Cited......................................................................................................................................... 45
v

Tables.......................................................................................................................................................... 54

Figures

1. Map of Upper Klamath Lake and sampling sites for 2013, 2014, and 2015...................................... 5

2. Upper Klamath Lake organic aggregate (floc) size versus settling velocity population scatterplots..33

3. Photographs showing (a) a complete LabSFLOC-2 floc cam setup in a laboratory, with the settling 

column and camera on the left and computer with operating software and recording hard drive on 

the right. (b) Top view of U.S. Geological Survey (USGS) version of the LabSFLOC-2 floc cam 

setup in stainless steel housing in a Pelican waterproof case, with the camera and lens to the left 

and the settling column to the right. (c) July 2015, site LS01—collecting the near-surface floc 

sample directly from the water column using the modified pipette. (d) USGS Van Dorn sampler 

from same sampling. (e) Professor Manning transferring flocs.. ..................................................... 34

4. Floc images from the LabSFLOC-2 floc cam collected during (a) the pre-bloom conditions, when

Upper Klamath Lake is dominated by brown algae including diatoms. Leaf-like structures appear in 

the July sample (b), assumed to be individual Aphanizomenon flos–aquae (AFA) filaments. 

Organic aggregates during bloom conditions (c) include cyanobacterial colonies.......................... 35

5. Photograph showing a piezometer and thermistor string deployed at one of the northern littoralzone sites. ...................................................................................................................................... 43

Tables

1. Sampling site locations and descriptions within and up gradient of Upper Klamath Lake, Oregon..54

2. Summary of results from 2013 sonde deployments in Upper Klamath Lake, Oregon..................... 55

3. 2013 dissolved (0.2-micrometer filtered) macronutrient benthic fluxes from profiler deployments. . 59

4. Riverine-flux estimates for dissolved (0.2-micrometer filtered) solutes into Upper Klamath Lake, 

Oregon............................................................................................................................................ 62

5. 2013 dissolved (0.2-micrometer filtered) macronutrient concentrations in the water column of Upper 

Klamath Lake, Oregon.................................................................................................................... 63

6. 2013 dissolved (0.2-micrometer filtered) trace-element benthic fluxes from profiler deployments. . 66

7. 2013 dissolved (0.2-micrometer filtered) trace-element concentrations in the water column Upper 

Klamath Lake, Oregon.................................................................................................................... 72

8. 2013 benthic chlorophyll and pheophytin concentrations in Upper Klamath Lake, Oregon, with 

associated temperature and specific-conductivity measurements.................................................. 78

9. Mean density of macroinvertebrates per square meter (m−2), constancy (presence per sample), and 

percentage composition of 63 samples (21 samples per date) during 2013................................... 81

10. Summary of results from 2014 sonde deployments near site ON01 in Upper Klamath Lake, 

Oregon............................................................................................................................................ 84

11. 2014 water-column and pheophytin concentrations in Upper Klamath Lake, Oregon, near site 

ON01. ............................................................................................................................................. 86

12. 2014 Dissolved-macronutrient benthic fluxes from profiler deployments in Upper Klamath Lake, 

Oregon, near site ON01.................................................................................................................. 87

13. Dissolved (0.2-micrometer filtered) macronutrient concentrations in the water column of Upper 

Klamath Lake, Oregon, near site ON01.......................................................................................... 88

14. 2014 dissolved trace-element benthic fluxes from profiler deployments near site ON01................ 89

15. 2014 dissolved (0.2-micrometer filtered) trace-element concentrations in the water column of Upper 

Klamath Lake, Oregon, near site ON01.......................................................................................... 91
vi

16. Mean density of macroinvertebrates per square meter (m−2), mean density per sampling interval, 

constancy (presence per sample), and percentage composition of 27 weekly samples collected 

during 2014 at site ON01................................................................................................................ 93

17. Taxonomic list of benthic invertebrates collected in 2013 and 2014 collections. ............................ 96

18. Phosphorus release (efflux) by some of the most numerically dominant and widespread taxa 

residing in Upper Klamath Lake...................................................................................................... 99

19. Potential importance of solute advective flux across the sediment-water interface. ..................... 100

20. Environmental variables and floc population properties, Upper Klamath Lake, Oregon, 

May 6, 2015.................................................................................................................................. 101

21. Environmental variables and floc population properties, Upper Klamath Lake, Oregon, 

July 30, 2015. ............................................................................................................................... 102

22. Environmental variables and floc population properties, Upper Klamath Lake, Oregon, 

October 6, 2015............................................................................................................................ 103
vii

Conversion Factors

International System of Units to U.S. customary units

Multiply By To obtain

Length

centimeter (cm) 0.3937 inch (in.)

millimeter (mm) 0.03937 inch (in.)

meter (m) 3.281 foot (ft) 

kilometer (km) 0.6214 mile (mi)

Area

square meter (m2) 0.0002471 acre 

square kilometer (km2) 247.1 acre

square centimeter (cm2) 0.001076 square foot (ft2)

square meter (m2) 10.76 square foot (ft2) 

square centimeter (cm2) 0.1550 square inch (ft2) 

square kilometer (km2) 0.3861 square mile (mi2)

Volume

liter (L) 33.81402 ounce, fluid (fl. oz)

liter (L) 0.2642 gallon (gal)

cubic meter (m3) 264.2 gallon (gal) 

cubic decimeter (dm3) 0.2642 gallon (gal) 

cubic meter (m3) 0.0002642 million gallons (Mgal) 

cubic centimeter (cm3) 0.06102 cubic inch (in3) 

liter (L) 61.02 cubic inch (in3) 

cubic meter (m3) 35.31 cubic foot (ft3)

Flow rate

meter per second (m/s) 3.281 foot per second (ft/s) 

meter per day (m/d) 3.281 foot per day (ft/d)

cubic meter per second (m3/s) 35.31 cubic foot per second (ft3/s)

cubic meter per day (m3/d) 35.31 cubic foot per day (ft3/d) 

liter per second (L/s) 15.85 gallon per minute (gal/min) 

cubic meter per day (m3/d) 264.2 gallon per day (gal/d) 

cubic meter per second (m3/s) 22.83 million gallons per day (Mgal/d) 

Mass

gram (g) 0.03527 ounce, avoirdupois (oz)

kilogram (kg) 2.205 pound avoirdupois (lb)

Density

kilogram per cubic meter (kg/m3) 0.06242 pound per cubic foot (lb/ft3) 

gram per cubic centimeter (g/cm3) 62.4220 pound per cubic foot (lb/ft3) 

Specific capacity

liter per second per meter ([L/s]/m) 4.831 gallon per minute per foot ([gal/min]/ft)

Hydraulic conductivity

meter per day (m/d) 3.281 foot per day (ft/d) 

Transmissivity

meter squared per day (m2/d) 10.76 foot squared per day (ft2/d) 

Temperature in degrees Celsius (°C) may be converted to degrees Fahrenheit (°F) as °F = (1.8 × °C) + 32.
viii

U.S. customary units to International System of Units

Multiply By To obtain

Length

inch (in.) 2.54 centimeter (cm)

foot (ft) 0.3048 meter (m)

Volume

cubic foot (ft3) 0.02832 cubic meter (m3) 

Flow rate

cubic foot per second (ft3/s) 0.02832 cubic meter per second (m3/s)

Pressure

pound per square inch (lb/in2) 6.895 kilopascal (kPa) 

Abbreviations

AFA cyanobacterium Aphanizomenon flos–aquae

BNL benthic nepheloid layer

cf. confer (a species that closely matches another species)

D particle size

DO dissolved oxygen

DOC dissolved organic carbon

e.i. early instar

EPA U.S. Environmental Protection Agency 

EPS extra-cellular polymeric substances

J diffusive benthic flux in milligrams per square meter per day

kg d−1 kilogram per day

kg m−3 kilogram per cubic meter

μg cm−2 microgram per square centimeter

m2 d−1 square meter per day

m3 d−1 cubic meter per day

μS cm−1 microsiemen per centimeter

mg L−1 milligram per liter 

mg m−2 d−1 milligram per square meter per day

mg m−2 s−1 milligram per square meter per second

mm s−1 millimeter per second

MSF mass settling flux

na not available

nd nondetectable value

nr. near (taxonomically)

ORP oxidation-reduction potential

ρ density

Reclamation Bureau of Reclamation

SD standard deviation

sp. species

SPMC suspended particulate matter concentration

spp. species (plural)

SRP dissolved (soluble) reactive phosphate

UKL Upper Klamath Lake, Oregon

USGS U.S. Geological Survey

Ws settling velocity
Benthic Processes Affecting Contaminant Transport in 

Upper Klamath Lake, Oregon

fBy James S. Kuwabara, Brent R. Topping, James L. Carter, Francis Parchaso, Rick A. Carlson,1 Stephen V. Fend, 

Natalie Stauffer-Olsen,2 Andrew J. Manning,3 and Jennie M. Land1 

Executive Summary

Multiple sampling trips during calendar years 2013 through 2015 were coordinated to provide 

measurements of interdependent benthic processes that potentially affect contaminant transport in Upper 

Klamath Lake (UKL), Oregon. The measurements were motivated by recognition that such internal 

processes (for example, solute benthic flux, bioturbation and solute efflux by benthic invertebrates, and 

physical groundwater-surface water interactions) were not integrated into existing management models 

for UKL. Up until 2013, all of the benthic-flux studies generally had been limited spatially to a number 

of sites in the northern part of UKL and limited temporally to 2–3 samplings per year. All of the benthic 

invertebrate studies also had been limited to the northern part of the lake; however, intensive temporal 

(weekly) studies had previously been completed independent of benthic-flux studies. Therefore, 

knowledge of both the spatial and temporal variability in benthic flux and benthic invertebrate 

distributions for the entire lake was lacking. To address these limitations, we completed a lakewide 

spatial study during 2013 and a coordinated temporal study with weekly sampling of benthic flux and 

benthic invertebrates during 2014. Field design of the spatially focused study in 2013 involved 21 sites 

sampled three times as the summer cyanobacterial bloom developed (that is, May 23, June 13, and July 

3, 2013). Results of the 27-week, temporally focused study of one site in 2014 were summarized and 

partitioned into three periods (referred to herein as pre-bloom, bloom and post-bloom periods), each 

period involving 9 weeks of profiler deployments, water column and benthic sampling. Partitioning of 

the pre-bloom, bloom, and post-bloom periods were based on water-column chlorophyll concentrations 

and involved the following date intervals, respectively: April 15 through June 10, June 17 through 

August 13, and August 20 through October 16, 2014. 

To examine dissolved-solute (0.2-micrometer [μm] filtered) benthic flux, sets of nonmetallic 

pore-water profilers (U.S. Patent 8,051,727 B1) were deployed. In 2013, the deployment of profilers at 

21 UKL sites occurred at the beginning of the annual cyanobacterial bloom of Aphanizomenon flos–

aquae (AFA), in the middle of the bloom period, and at the peak of the bloom. Coordinated benthic 

invertebrate collections also were made. Based on results from 2013, weekly deployments of profilers 

and collection of benthic invertebrate samples from late spring to early autumn were used to estimate 

temporal trends in solute flux and benthic invertebrate densities. Estimates of nutrient efflux by benthic 

invertebrates were determined in the spring and autumn from 2011 through 2013 and three times 

(spring, summer, and autumn) in 2015. This work extends UKL studies that began in 2006 to quantify 

 

1

Bureau of Reclamation

2

University of California, Berkeley, College of Natural Resources 

3

Plymouth University, School of Marine Science and Engineering 
2

the importance of benthic solute sources in the lake. In 2015, piezometers and thermistor sets were 

deployed to quantify potential groundwater exchange with the lake water column. 

Analysis of the 2013 soluble reactive phosphorus (SRP) benthic flux indicated no effect of 

location (lake region), habitat, or sampling period, and the average lakewide flux values were consistent 

with earlier studies that had been confined to the northern region of UKL and adjacent wetlands. The 

2014 study therefore focused on estimating temporal trends at a site within Ball Bay. During both 2013 

and 2014 field studies, fluxes of macronutrients (soluble reactive phosphorus (SRP) and ammonia) and 

micronutrients (iron [Fe] and manganese [Mn]) were consistently positive and increased prior to the 

initial AFA bloom, varied or lagged with water-column chlorophyll during the summer bloom period, 

then decreased after the cyanobacterial blooms, only to rebound toward pre-bloom conditions in the 

final weeks of sampling. These four solutes exhibited benthic loads greater than maximum riverine 

loads estimated during the spring and early summers of 2013 and 2014. However, consistently 

detectable concentrations for all four solutes provide no evidence that they consistently serve as the 

limiting nutrient for primary production in the lake. In contrast to the four solutes (SRP, ammonia, Fe, 

and Mn), benthic fluxes of dissolved arsenic (As) were both negative and positive (that is, the lakebed 

currently serves as both a source and a sink for dissolved As, depending on season). In a further contrast 

with SRP, ammonia, dissolved Fe, and Mn, dissolved-As riverine loads to UKL were of similar 

magnitude to benthic loads. A negative relationship between dissolved-As flux and water-column As 

over the 2014 temporal study provides a potential advantage for the management of water-quality in 

contrast to solutes, like SRP or ammonia, with consistently positive flux. 

The mean total benthic invertebrate density during 2013 was 12,610 individuals per square meter

(n=63). Although benthic invertebrate density did not change over the study period, it was higher in 

littoral habitats than open-lake or trench habitats and higher in the northern region compared to the 

central or southern regions of UKL. Mean total benthic invertebrate density during 2014 was 19,726 

individuals m−2

 (n=27). Density during the pre-bloom and bloom periods of April 15 to August 13, 2014 

(the first two thirds of the 2014 sampling period), were similar to 2013. However, benthic invertebrate 

density more than doubled during the latter one-third of the study, that is, the post-bloom period 

between August 20 to October 16, 2014. Oligochaeta, Chironomidae and Hirudinea represented well 

over 90 percent of the benthic fauna; Oligochaeta were twice as abundant as Chironomidae or 

Hirudinea, the latter two of which were similar in density.

Benthic invertebrates may enhance dissolved-nutrient (or toxicant) transport across the 

sediment-water interface by (1) modifying diffusion-layer thicknesses and permeability through 

bioturbation, (2) enhancing advective flow across the interface through bioirrigation, and (3) excreting 

or expelling dissolved or particulate solutes directly into the overlying water column (Boudreau and 

Jorgensen, 2001). We evaluated SRP efflux via excretion for approximately 15 different major taxa in 

UKL. Once these measures were scaled, it was evident that benthic invertebrates potentially contribute 

approximately 1.5 times the amount of SRP to the water column of Upper Klamath Lake as diffusive 

SRP flux alone, measured in profiler deployments. 

Sets of piezometers and temperature loggers were deployed in UKL to obtain estimates of 

vertical advective solute flux. The pressure transducer installations, within the piezometers, did not 

perform as designed, rendering the head gradient data unreliable. However, in terms of future research, 

this field work did demonstrate the feasibility of collecting vertical gradient data with piezometer 

deployments. Advective flux estimates herein are based solely on heat-flow modeling based on 

temperature data from four lake sites, without use of transducer data. Given the magnitudes (both 

positive or negative) of the heat-transfer fluxes for SRP, relative to diffusive-flux and macroinvertebrate 

efflux measurements (all positive but spanning the same orders of magnitude), further examination of 
3

solute advective flux is recommended as a potential transport process to integrate into existing waterquality (for example, Total Maximum Daily Load [TMDL]) models. 

As a complement to the biogeochemical focus of this study, initial analyses of suspendedparticle (floc) characteristics and settling velocities from the water column were derived near the surface 

and lakebed at two UKL sites. To better understand changing particle characteristics during the AFAbloom period, suspended particles were examined in 2015 using a LabSFLOC (LF), which is a 

Laboratory Spectral Flocculation Characteristics version of an In-Situ Settling Velocity instrument 

(INSSEV-LF). Particle characteristics and settling velocities were analyzed from the water column near 

the surface (sample dp_10) and lakebed (sample dp_90) at two lake sites (open-lake site ML and littoral 

site LS01). The term “floc” refers herein to suspended particles that may aggregate or disaggregate to 

change in size, composition, and settling velocity. 

During pre-bloom (May) conditions, where maximum suspended particulate matter 

concentration (SPMC) was 140 milligrams per liter (mg L−1

) at site ML, the average floc sizes were 

small, not exceeding 122 μm at ML or LS01. In the July bloom, a high SPMC of 316 mg L−1was now 

observed at site LS01 in close proximity to the bed, where Dmean peaked at 305 μm, and the 

corresponding Wsmean was 3.9 millimeters per second (mm s−1

). The high near-bed SPMC (828 mg L−1) 

experienced during post-bloom October 2015 at LS01 formed a benthic nepheloid layer (BNL) above 

the lake’s bed. Numerous low density, fast settling macrofloc-sized organic aggregates (D >160 μm) 

were observed (some up to 1 mm in size) near bed at LS01 both during the bloom and post-bloom 

conditions; many of these flocs displayed fibrous organic structures. In terms of mass settling fluxes, the 

post-bloom BNL produced a total MSF of 4,139 milligrams per square meter per second (mg m−2 s

−1

) 

(92.1 percent of MSF credited to the macrofloc-sized organic aggregates/cyanobacterial colonies); that 

was nearly three times the corresponding near-bed settling flux observed during the July 2015 bloom 

and 360 times greater than the pre-bloom conditions from May 2015 (98.8 percent and 14 percent of 

MSF credited to the macrofloc-sized fractions for those respective months). Such changes in the nearbed settling flux demonstrate the highly significant seasonal effects that the AFA bloom has on the floc 

depositional fluxes in UKL and highlights the importance of seasonal monitoring of these conditions in 

order to correctly parameterize the wide range in depositional characteristics and floc properties 

measured throughout UKL. 

Collectively, floc populations observed within UKL demonstrated a wide range in settling 

velocity (Ws) for a given particle size, D. Similarly, a given settling velocity was not associated with a 

specific particle size. This variability in particle characteristics and properties indicates the influence of 

varying floc effective density and its effect on mass and mass settling fluxes (MSF). The use of 

instruments, such as the INSSEV-LF, enables measuring the variability of settling velocity and its

relation to particle density and size. 

Introduction

Potential Management Implications

Harmful algal blooms, like those annually encountered in Upper Klamath Lake (UKL, also 

referred to as “the lake”), Oregon, have become one of the most challenging water-quality issues facing 

the world’s freshwater ecosystems (Brooks and others, 2016). Management goals of maintaining or 

restoring water quality and hence endangered fisheries resources in Upper Klamath Lake and the 

downstream Klamath River Basin are dependent on the transport of essential nutrients and toxicants 

through benthic and pelagic food webs. The autotrophic and heterotrophic base of those food webs, 

from which trophic transfer begins, is dependent on sediment and water quality. In order to meet water-
4

quality objectives for the lake (for example, to reduce the impact of nutrient cycling on eutrophication 

and endangered fish populations in the lake), an understanding of the processes governing nutrient 

transport and distribution is required to help explain temporal and spatial trends in benthic and pelagic 

community composition. The structure and abundance of these communities are in turn linked to 

nutrient accumulation and remobilization within the lake as well as solute loads from upstream. In 

hypereutrophic Upper Klamath Lake, predominance of the nitrogen-fixing cyanophyte Aphanizomenon 

flos–aquae (AFA) suggests that a factor other than nitrogen regulates phytoplankton biomass. With the 

recognition that benthic processes (internal cycling) of contaminants may play an important role in 

solute transport within the lake (Wood and others, 2013; Wherry and others, 2015), this study focuses 

on the benthic nutrient and trace metal sources from the lakebed that may be significant relative to 

potentially regulated allochthonous sources. In addition to phosphorus, we examine the benthic flux of 

dissolved inorganic nitrogen species (nitrate, nitrite, and ammonia) as well as dissolved micronutrient 

fluxes from the lake sediments. Spatial and temporal trends in these benthic solute sources or sinks 

should also be useful to resource managers who are interested in refining management (Total Maximum 

Daily Load [TMDL]) models that integrate benthic processes. Although the study focused on the 

biogeochemical flux of dissolved nutrients and trace elements, piezometer and temperature-logger 

deployments, as well as suspended-sediment characterizations in 2015, began to address the influence of 

physical processes on nutrient cycling within and through the lake. This complementary information is 

envisioned as potentially necessary for the development of numerical models of nutrient transport. 

Information provided herein is intended to provide internal solute-loading information for such models 

and to indicate reasonable expectations when evaluating strategies for solute-load allocations or habitat 

restoration. 

Background

In hypereutrophic Upper Klamath Lake (UKL), Oregon (table 1; fig. 1), organic carbon 

generated by massive cyanophyte blooms that is not transported out of the lake ultimately settles to the 

bed of the lake or, to some extent, wetland habitats and imposes a benthic demand for oxygen. Such a 

demand can pose an environmental stress for endangered fish populations (Wood and others, 2006). 

Although phosphorus (P) may typically serve as the limiting nutrient for those nitrogen-fixing 

cyanophytes, our previous lake studies indicated that internal loading of dissolved micronutrients (for 

example, iron and manganese) should also be further examined. We therefore present water-column and 

benthic-flux information for both macro- and micronutrients that may be pertinent to source 

management for the basin (U.S. Fish and Wildlife Service, Hatfield Restoration Program; 

http://www.fws.gov/klamathfallsfwo/ero/er.html). In recognition of increasing interest in arsenic 

transport through the lake and basin, fluxes for dissolved total arsenic are also presented. On a global 

scale, the imminent need for a better quantitative understanding of the interdependent effects of natural 

and anthropogenic factors on the likelihood and severity of harmful algal blooms, like those occurring 

annually in UKL, have been discussed and advocated (Brooks and others, 2016). 
5

Figure 1. Map of Upper Klamath Lake and sampling sites for 2013, 2014, and 2015.
6

During the early to mid-1900s, massive phytoplankton blooms dominated by Aphanizomenon 

flos–aquae (AFA) have occurred each summer in UKL. After buoyant AFA layers (scums) are 

concentrated by wind over the lake surface and cells senesce, biomass settles and water-column 

dissolved oxygen is depleted at the lakebed. Such hypoxia may affect the recruitment and survival of 

lake fauna, including zooplankton, benthic invertebrates, and endangered populations of the shortnose 

sucker (Chasmistes brevirostris) and Lost River sucker (Deltistes luxatus). Indeed, the sustainability of 

sucker populations is linked to the water quality in the lake, and to the nutrient sources that regulate the 

intensity of AFA blooms, and the succession of phytoplankton assemblages before and after those 

blooms. Of particular significance is the appearance of the non-nitrogen-fixing, hepatotoxin-producing 

cyanobacterium Microcystis aeruginosa (Kann, 2006). Though not abundant relative to AFA, M. 

aeruginosa has been consistently and more-ubiquitously detected in Upper Klamath Lake (Eldridge and 

others, 2014). Investigations on the sources and distributions of microcystin in Upper Klamath Lake 

continue by other collaborating U.S. Geological Survey (USGS) projects within the Ecosystems 

Mission Area and the Oregon Water Science Center. 

Previous studies (Kuwabara and others, 2009; Kuwabara and others, 2012a) consistently 

indicated that problematic phytoplankton blooms are fueled by external and internal loads of particlereactive solutes (for example, ligands/anions like phosphorus as orthophosphate or metals like iron and 

manganese). As these solutes repartition and change in chemical speciation, benthic sources of these 

nutrients may play a major role in phytoplankton dynamics and succession (Kuwabara and others, 

2009). Nonmetallic pore-water profilers (U.S. Patent 8,051,727 B1), designed and fabricated for 

previous studies of Upper Klamath Lake (Kuwabara and others, 2009), were successfully deployed at a 

total of 21 lake sites that include three habitat types (littoral, open-lake, and a western trench feature), as 

well as three area-equivalent geographic types (northern, central, and southern) that represent lake 

regions near tributary inputs in the north and outflows through the dam in the south. 

Furthermore, abiotic internal loads can be significantly augmented by solute efflux and 

bioturbation by benthic invertebrate assemblages in the lake that phenologically change in composition 

throughout the annual cyanobacterial bloom cycle in the lake. Benthic invertebrates (for example, 

worms, insects, mollusks) are an integral component of lake ecosystems. They are composed of all 

higher trophic levels (that is, above primary producers) and include several functional feeding groups, 

such as filterers, grazers, deposit feeders, piercers, and engulfers. They also represent an important food 

resource for fishes, birds, and some mammals. Benthic invertebrates directly cycle nutrients between the 

sediments of lakes and the water column through normal metabolic processes and indirectly influence 

diffusive and advective flux of solutes through their behavioral bioturbation of lakebed sediments. 

Therefore, in agreement with Miller and Tash (1967), “It is imperative to know the extent to 

which nutrients in the sediments can interchange with the overlying water.” This study quantifies 

benthic processes that contribute to that interchange. 

Objectives

In support of science-based restoration/management strategies for Upper Klamath Lake, adjacent 

wetlands, and downstream ecosystems, this study provides measurements of biotic and abiotic benthic 

fluxes of dissolved macronutrients and trace elements between the lakebed and overlying water column. 

The spatial and temporal distribution of benthic invertebrate assemblages, solute efflux from major 

species within those assemblages, benthic-chlorophyll and water-column solute concentrations were 

also analyzed to augment solute-flux results. The work addresses two hypotheses of imminent 

management relevance. First, some of the macronutrients (for example, soluble reactive phosphorus 

[SRP] and ammonia) and many of the trace elements (for example, cadmium, copper, iron, manganese, 
7

nickel, and zinc) are chemically reactive and adsorb to biotic and abiotic surfaces. We hypothesize that 

benthic sources, deemed significant for certain solutes in previous studies relative to other sources, 

could be quantified in a way consistent with incorporation into existing water-quality management (for 

example, Total Maximum Daily Load) models. Given the paucity of trace-metal and benthic 

invertebrate information for this or any other part of the Klamath River Basin, we provide trace-element 

benthic flux for Upper Klamath Lake (including dissolved-arsenic water-column concentrations and 

benthic fluxes), as well as benthic invertebrate solute efflux measurement. This study clearly focused on 

biogeochemical processes near the sediment-water interface that may affect solute transport and 

phytoplankton dynamics in the lake. However, in recognition of potential physical processes that may 

also be of management relevance, (1) transducers and temperature loggers were deployed to examine 

advective fluxes across that interface, and (2) suspended particles were examined in 2015 using an InSitu Settling Velocity Instrument (INSSEV-LF) to better understand changing particle characteristics 

during the AFA-bloom period at an open-lake and littoral zone site near the surface and lakebed. 

Results and Discussion

2013—Spatial Variability Study 

Field design of the spatially focused study in 2013 involved 21 sites sampled three times as the 

summer cyanobacterial bloom developed (that is, May 23, June 13, and July 3, 2013). In addition, 

preliminary samples were collected at only two sites during a reconnaissance trip on May 1, 2013, to 

test and refine experimental protocols for the subsequent sampling trips. The 21 lake sites were selected 

among three geographic partitions and three habitats (see “Methods” section). Tables presenting results 

from the 2013 sampling effort have shaded blocks of rows for results from June 13, 2013, to distinguish 

between sampling dates (for example, tables 2–8).

Ancillary Parameters in 2013

Measurements of ancillary water-column parameters (for example, temperature, specific 

conductivity, pH, and dissolved oxygen), collected by vertical sonde deployments at the profiler 

deployment sites, are provided to establish the basic physical and chemical context to assist in 

interpreting results for solute benthic fluxes. Except for the three trench sites (6.0–14.2 meters [m]

depth) and the mid-lake north (MDN) site (~4 m depth), the sites where profilers were deployed were 

shallow (<3 m depth), with minimal haloclines or thermoclines. In fact, the littoral-zone sites (that is, 

site names beginning with “L”) were <2 m depth. Specific conductivity values varied over a narrow 

range (94 to 460 microsiemens per centimeter [μS cm−1

]), with an expected trend of lowest values 

during the pre-bloom and early-bloom deployments as snow melt diluted lake waters (May through June 

2013) and elevated values during the later bloom samplings as lake waters evaporated and water 

elevations decreased (July 2013). Consistent water-temperature ranges reflected seasonal, increasing airtemperature patterns affecting the water column at all sites during our 2013 sampling period (table 2; 

11.3 to 11.9 °C on May 1, 2013, compared to 18.4 to 26.5 °C on July 3, 2013, near the peak of the 

annual AFA bloom). Observed seasonal ranges are consistent with previous and ongoing monitoring of 

Upper Klamath Lake by Wood and others (2006, 2013). 

Dissolved oxygen (DO) decreased with depth in 57 of 60 water column profiles (table 2).

Furthermore, the mean difference between surface- and bottom-water percent DO saturation increased 

from May through July (−9.8 ± 19.7 percent [n=21], −19.9 ± 25.1 percent [n=21], and −62.9 ± 34.2 

percent [n=17] for May 23, June 13, and July 3, 2013, respectively). This temporal increase in the 
8

difference between surface- and bottom-water DO might be expected as the initial AFA bloom 

develops. There are a number of mechanisms that could contribute to this DO depth difference. First, 

enhanced net photosynthesis near the surface as the bloom progresses increases DO in the photic zone. 

For example, the percent DO saturation increase between the May 23 and July 3 deployments in surface 

waters was 23.3 ± 34.7 percent (n=16 sites). Second, near the bottom, organic matter settles from the 

water column to the lakebed, increasing benthic-oxygen demand and decreasing bottom-water DO.

Water-column respiration near the bottom also decreases DO. Percent DO saturation in bottom waters 

decreased (−32.6 ± 32.5 percent, n=16) between the May 23 and July 3 deployments. Enhancing the 

aforementioned processes is the effect of thermal stratification as the summer progresses. Kann and 

Welch (2005) observed increasing differences between surface- and bottom-water DO concentrations in 

UKL during midsummer (July and August 1990 through 2001) with increasing water-column stability 

(that is, stratification dependent on wind speed). However, examining data between 1990 and 2004, 

Wood and others (2006) did not observe a significant relationship between wind speed and differences 

between minimum and maximum DO in the UKL water column. 

For pH, a gradual rise was observed through the sampling dates; pH varied between 6.8 and 7.1 

on May 1, 2013 (prior to the bloom), and varied between 8.2 and 10.3 on July 3, 2013. With 2 

exceptions out of 40, pH was >9 on July 3, 2013 (table 2). The maximum pH, 10.3, was at site OC03 

and measured at 0935 PST, so even higher pH values may be expected as solar energy increases later in 

the day and phytoplankton accelerate their removal of dissolved inorganic carbon from the water 

column (Wood and others, 2006). There are many biogeochemical and toxicological implications owing 

to elevated pH. For example, Falter and Cech (1991) reported a maximum pH tolerance of 9.55 ± 0.43 

for shortnose suckers, while Meyer and Hansen (2002) reported no toxicological effect of pH <10 on 

multiple life stages of Lost River suckers. In 2013, pH >10 was only observed at seven sites, all on July 

3, the final 2013 sampling date near the initial AFA bloom peak (table 2). Morace (2007) also reported 

multiple pH measurements >10 in UKL during the summer AFA-bloom period. Second, as pH 

increases, the chemical speciation and partitioning of biologically reactive solutes are significantly 

affected. For example, as pH rises above the negative logarithm of the acid dissociation constant for the 

ammonium ion (9.3), uncharged and toxic ammonia (NH3) becomes more thermodynamically stable 

relative to the ammonium ion (Arillo and others, 1981; Randall and Tsui, 2002). Third, as pH elevates, 

adsorbed anions (for example, orthophosphate) repartition from particulate to the more biologically 

available, dissolved phase (Sigg and Stumm, 1981; Goldberg, 1985; Fisher and Wood, 2001). Fourth, in 

an oxic environment, micronutrients (for example, iron, manganese, and copper) become less soluble 

(and hence less bioavailable to primary producers) because precipitates and adsorbed forms become 

more stable (Hogfeldt, 1982; Kuwabara and others, 1986), thereby decreasing uncomplexed (that is, 

more bioavailable) metal forms. 

Benthic Flux of Macronutrients in 2013

Benthic fluxes (table 3), calculated using Fick’s law (see “Methods” section), specifically 

represent a diffusive solute flux across the sediment-water interface and may therefore underestimate 

total benthic sources by all additional processes (for example, benthic invertebrate efflux, bioturbation, 

and solute advection) when pore-water concentrations are consistently greater than overlying watercolumn concentrations. Among macronutrients examined in this study, soluble (0.2-micrometer [μm] 

filtered) reactive phosphorus (that is, a biologically available form of dissolved phosphorus, SRP) is an 

extremely particle-reactive solute as pH becomes elevated. That is, SRP can form stable surface 

complexes on a variety of mineral and biotic surfaces (Sigg and Stumm, 1981; Goldberg, 1985; 

Kuwabara and others, 1986; Ruttenberg, 1992). Particulate P can be transported through the lake to the 
9

down-gradient river basin or may settle in the lake and accumulate in the bottom sediments. Various 

biogeochemical processes related to changes in acid-base and redox (oxidation-reduction) chemistry 

near the sediment-water interface can recycle this P and generate a benthic flux of bioavailable P that 

may far exceed external sources (Kuwabara and others, 2003). Because the dominant phytoplankton 

species, AFA, is a nitrogen-fixing cyanophyte, it is reasonable to assume that a nutrient other than 

nitrogen (for example, phosphorus, iron, or manganese) may serve as the stimulatory nutrient to initiate 

the annual cyanobacterial bloom (Istvánovics, 2008). 

In this report, concentration units for dissolved nutrients are in terms of the element rather than 

the associated molecule, with the exception of silica as SiO2. For example, the concentration unit 

specified herein for dissolved ammonia is milligrams of nitrogen per liter (mg-N L−1), as opposed to an 

ammonia species. 

As previously reported for Upper Klamath Lake (Kuwabara and others, 2009; Kuwabara and 

others, 2012a), SRP benthic flux as estimated from 2013 profiler deployments was consistently positive 

(table 3), that is, from the lakebed to the overlying water column, but was highly variable in magnitude 

with a mean of 4.28 ± 4.84 milligrams of phosphorus per square meter per day (mg-P m−2 d−1) (n=65, 

range = 0.13 to 33.27 mg-P m−2 d−1). This represents an areally averaged SRP (as P) load to the lake

water column of 856 ± 968 kilograms per day (kg d−1

) (n=65, range = 26 to 6,650 kg d−1), assuming a 

lake area of 200 square kilometers (km2) (Kuwabara and others, 2009). By comparison, estimates of 

SRP (as P) loads from the inlet tributaries, sampled in the spring and early summer of 2013 and 2014 

(table 4) varied from 115 to 154 kg d−1, bracketed by the SRP benthic-flux range. Statistical analysis of 

the SRP benthic-flux measurements indicated no effect of location (lake region), habitat, or sampling 

period. Furthermore, the average lakewide flux values were consistent with earlier studies that had been 

confined to the northern region of the lake and adjacent wetlands (Kuwabara and others, 2009; 

Kuwabara and others, 2012a). Therefore, the subsequent 2014 experimental design for a temporal study, 

weekly resolution, focused around site ON01 within Ball Bay because of (1) representative SRP fluxes 

in 2013, (2) representative benthic invertebrate communities, (3) accessibility during periods of high 

winds within Ball Bay, and (4) focused allocation of available resources to optimize temporal resolution 

of benthic solute source measurements. 

Benthic flux of ammonia, particularly at elevated pH in the overlying water column, has 

toxicological implications for endangered sucker populations (Meyer and Hansen, 2002). Similar to 

SRP, ammonia benthic flux was consistently positive, indicating (1) a consistent benthic source of 

ammonia to the lake water column and (2) potentially elevated (relative to the water column) solute 

concentrations close to the sediment-water interface, where the endangered suckers presumably feed, 

and also potentially elevated when the water column is thermally stratified. In most areas of UKL, such 

thermal stratification, when it occurs, does not persist overnight, but in deeper parts of the lake, the 

water column can remain stratified for a few days at a time (Wood and others, 2006). Martin and Saiki 

(1999) determined in their fish-cage studies of the Lost River sucker (Deltistes luxatus) that elevated 

concentrations of un-ionized ammonia may be contributing to the precipitous decline of sucker 

populations in Upper Klamath Lake, although depletion of dissolved oxygen in the water column (a 

factor that covaries with ammonia) exerted an even stronger influence on sucker mortality. The 

correlations between DO and tabulated ammonia in 2013 and 2014 were −0.35 (p<0.001, n=77) and 

−0.46 (p<0.05, n=27), respectively. Ammonia benthic flux was 10.73 ± 8.07 milligrams nitrogen per 

square meter per day (mg-N m−2 d−1

 (range = 1.93 to 42.67, n=65). This represents an areally averaged 

ammonia (as N) load to the lake of 2,150 ± 1,610 kg d−1 (table 3). By comparison, estimates of 

ammonia loads from the inlet tributaries ranged from 9 to 326 kg d−1, one to three orders of magnitude 

lower than benthic-flux measurements. In the wetlands adjacent to the northern boundary of the lake, 
10

Kuwabara and others (2012a) observed a positive correlation between dissolved-ammonia benthic flux 

and water-column dissolved ammonia, primarily affected by elevated values of both parameters during 

post-bloom conditions. That ammonia correlation was not present in the 2013 lake data, as elevated 

water-column ammonia concentrations were only observed in the final sampling (July 3, 2013), when

the bloom began its transition to senescence. In contrast to SRP and ammonia, no positive benthic 

fluxes were observed for nitrate. This is consistent with previously published nitrate fluxes for Upper 

Klamath Lake (Kuwabara and others, 2009; Kuwabara, 2012a). Nitrate flux was −0.55 ± 2.34 mg-N m−2

d−1 (range = −12.42 to 0.00 mg-N m

−2 d−1, n=65; table 3). This represents an areally averaged nitrate (as 

N) load to the lake of between −2,480 to 0 kg d−1. 

A microscopic examination of surficial lakebed sediments indicates an abundance of both 

pennate and centric diatom frustules. Prior to the annual summer cyanobacterial bloom in the lake, the 

water column is typically dominated by diatoms that require a source of dissolved silica. Similar to SRP 

and ammonia, benthic fluxes for dissolved silica were consistently positive (that is, a benthic source to 

the lake water column). The mean silica flux was 108 ± 84 mg-SiO2 m

−2 d−1 (range = 16 to 579 mg-SiO2

m

−2 d−1, n=61). This represents an areally averaged silica (as SiO2) load to the lake of 21,600 ± 16,800 

kg d−1. By comparison, estimates for inlet tributary loads for dissolved silica measured in the spring and 

early summer of 2013 and 2014 were between 72,500 to 114,600 kg d−1 (mean of 90,600 ± 15,700 kg 

d−1, n=6); hence, there is a significant tributary source of dissolved silica to the lake water column 

relative to the lakebed (table 4). 

Similar to nitrate, benthic flux of dissolved organic carbon (DOC) was often negative (that is, 

the solute was consumed by the lakebed), particularly during the final sampling in July 2013 (table 3).

However, DOC flux varied between −5.17 and 2.15 mg-C m−2 d−1, with a mean of −0.47 ± 1.24 mg-C 

m

−2 d−1 (n=65). That is, the lakebed served as both a source and a sink for DOC, representing an areally 

averaged DOC (as C) load to the lake of between –1,030 to 430 kg d−1 (−94 ± 248 kg d−1, n=65). In an 

additional contrast to SRP and ammonia fluxes, inlet tributary loads for DOC measured in the spring 

and early summer of 2013 and 2014 were between 1,874 to 8,922 kg d−1 (4,322 ± 2,525 kg d−1, n=7). As 

observed for dissolved silica, surface water sources of DOC to the lake were significant relative to the 

benthic sources. 

Dissolved (0.2-μm filtered) Nutrients in the Water Column in 2013

Through the 2013 spatial study, soluble reactive phosphorus (SRP) varied between 0.02 and 0.36 

mg-P per liter (L−1) (overall mean of 0.07 ± 0.06 mg-P L−1, n=77; table 5). The water-column SRP 

concentrations were generally low given that the detection limit was 0.007 mg-P L−1, but all sites on all 

dates had detectable SRP. That is, SRP depletion was not observed in the lake water column. As 

previously noted (Kuwabara and others, 2009; Kuwabara and others, 2012a), this observation suggests 

the possibility of a limiting factor or factors other than, or in addition to, SRP. In contrast to seasonal 

nutrient trends typical of mono- or dimictic lakes (Wetzel, 2001), water-column solute concentrations in 

shallow lakes like Upper Klamath Lake are also subject to subseasonal variability in benthic interactions 

and rapid cycling through biomass. Spring and early summer SRP concentrations reported here are 

consistent with previous lake monitoring results (for example, Lindenberg and others, 2009; Hoilman 

and others, 2008; Kann, 2010). Near the AFA bloom peak on July 3, 2013, SRP did not exceed 0.13 

mg-P L−1 at any of the 21 sampling sites, but a closer examination of temporal trends will be provided 

with the discussion of 2014 water-column nutrient concentrations.

Through the 2013 spatial study, dissolved-ammonia concentrations varied from <0.007 to 1.031 

mg-N L−1 (overall mean of 0.13 ± 0.22 mg-N L−1, n=77; table 5). In contrast to water-column SRP, 

seven samples were below the detection limit (that is, <0.007 mg-N L−1), all during the June 13, 2013, 
11

sampling. Those seven samples also had undetectable dissolved nitrate (that is, <0.01 mg-N L−1). The 

depletion of both ammonia and nitrate during the progression to a cyanobacterial peak has been 

observed at Lake Mendota, Wisconsin, and selects for nitrogen fixers like AFA (Beversdorf and others, 

2013). In addition, dissolved-ammonia concentrations become a particular concern when elevated 

temperature and pH shift the speciation of ammonia toward the un-ionized form, NH3

0

. The effects of 

water temperature and pH on ammonia speciation can be calculated to determine un-ionized ammonia 

concentrations from tabulated ammonia concentrations (the sum of NH3

0

 and ionized NH4

+

) (Canadian 

Council of Ministers of the Environment, 2010). Potentially toxic water-column un-ionized ammonia 

concentrations above the median lethal concentration (LC50) for Lost River suckers (0.45 mg-N L−1; 

Meyer and Hansen, 2002) were only observed at four sites (LS01 with 0.65 mg-N L−1, LS01 with 0.66 

mg-N L−1, TR02 with 0.58 mg-N L−1, and TR03 with 0.76 mg-N L−1). All four exceedances were 

observed on July 3, 2013, the final 2013 sampling date near the initial AFA bloom peak (table 5). 

Morace (2007) also noted infrequent elevated un-ionized ammonia concentrations among 2,023 UKL 

samples collected between 1990 and 2006 (only 3 percent of samples greater than 0.53 mg-N L−1). In 

comparison to suckers, Arillo and others (1981) observed sublethal dissolved-ammonia effects in both 

brain and liver of rainbow trout at much lower concentrations (0.02 mg-N L−1 in un-ionized form). In 

2013, 22 of 77 water-column samples in UKL had dissolved un-ionized ammonia concentrations >0.02 

mg-N L−1 (table 5). By contrast, dissolved ammonia was consistently <0.02 mg-N L−1 in tributary 

samples collected in the spring and early summer of 2013 and 2014, so even at the highest discharge to 

the lake measured during the present study in April 2013, riverine input for dissolved ammonia would 

be 326 kg d−1, considerably lower than the benthic ammonia loads determined in the 2013 spatial study

(2,150 ± 1,610 kg d−1, table 3). 

Dissolved-nitrate concentrations were consistently low, with approximately one-third (that is, 25 

of 77) water-column samples having undetectable concentrations (<0.01 mg-N L−1). Concentrations 

varied between <0.01 and 0.20 mg-N L−1 (overall mean 0.02 ± 0.05 mg-N L−1, n=77; table 3). The 

combination of low water-column and riverine nitrate concentrations and the absence of any positive 

nitrate fluxes suggest that ammonia rather than nitrate is the primary form of dissolved inorganic 

nitrogen (DIN) used by autotrophs in Upper Klamath Lake. 

Similar to dissolved ammonia, dissolved silica increased as the initial AFA bloom neared its 

peak (38.29 ± 1.03 mg-SiO2 L−1 [n=24] on July 3 compared to 31.94 ± 1.21 mg-SiO2 L−1 on the 

previous sampling date of June 13, 2013). Overall, silica concentrations in 2013 varied between 29.60 

and 39.80 mg-SiO2 L−1 (mean of 35.82 ± 3.00 mg-SiO2 L−1, n=71). Lake concentrations were lower 

than inlet-tributary concentrations along the Spring Creek, Sprague and Wood Rivers (46.71 ± 5.69 mgSiO2 L−1, n=9) but higher than the Williamson River headwaters (downgradient of the marsh; 19.73 ± 

0.26 mg-SiO2 L−1, n=1 site with two sample replicates; table 4). 

Water-column concentrations in the lake for DOC in 2013 ranged from 3.58 to 6.88 mg-C L−1

(overall mean of 4.44 ± 0.86 mg-C L−1, n=74). As seen with dissolved ammonia, the highest DOC 

concentrations occurred during the final sampling trip near the initial AFA bloom peak. For example, 

the mean DOC for July 3, 2013, was 5.55 ± 0.55 mg-C L−1 , n=24, whereas water-column samples 

collected on the previous profiler deployment date of June 13, 2013, yielded a mean DOC of 4.07 ± 0.21 

mg-C L−1 , n=24. 

Benthic Flux of Trace Metals in 2013

Information on the flux of trace elements across the sediment-water interface for any part of the 

Klamath River Basin is limited (Kuwabara and others, 2007; Kuwabara and others, 2012a). This study 

provides among the first dissolved trace metal benthic-flux estimates for a spatial network as broad as 
12

designed in 2013. In particular, to our knowledge, it is the first study to provide benthic-flux estimates 

for total dissolved arsenic (primarily as arsenate) for Upper Klamath Lake in response to recent, more 

stringent water-quality criteria for the State of Oregon (Atkins, 2016). In contrast to macronutrient 

fluxes (presented in units of mg m

−2 d−1), trace-element fluxes are provided in units of micrograms per 

square meter per day (μg m

−2 d−1), owing to lower solute detection limits using inductively coupled 

plasma mass spectrometry (ICP-MS) relative to batch nutrient analyses. Like macronutrient fluxes, 

dissolved trace-element fluxes are presented in terms of the element rather than a dominant molecule 

(for example, vanadium [V] rather than vanadate).

Dissolved iron (Fe) and manganese (Mn), both essential micronutrients, exhibited consistently 

positive diffusive flux (that is, a consistent benthic solute source throughout the 2013 sampling period; 

table 6). In addition, the flux of these dissolved trace elements were at least two orders of magnitude 

higher than any of the other eight trace elements. Dissolved-iron benthic flux varied between 100 and 

16,755 μg-Fe m−2 d−1 (overall mean of 3,456 ± 3,270 μg-Fe m

−2 d−1, n=66). This represents an areally 

averaged dissolved-Fe load to the lake water column of 691 ± 654 kg d−1, assuming a lake area of 200 

km2. These values are consistent with those reported previously for three sites in the northern region of 

the lake over a 4-year study (3,881 ± 3,640 μg-Fe m

−2 d−1, n=19; Kuwabara and others, 2012a). By 

comparison, estimates of dissolved-Fe loads from the inlet tributaries, sampled in the spring and early 

summer of 2013 and 2014 (table 4), were 169 ± 108 kg d−1 (n=7). Despite the fact that the riverine 

source of dissolved Fe was lower than the internal benthic source, unlike SRP and ammonia fluxes, the 

lower riverine fluxes were at least in the same order of magnitude as the benthic fluxes. Results 

therefore suggest that riverine dissolved-Fe sources should be carefully integrated when modeling Fe 

transport to and through the lake. As mentioned in the “Introduction” section, Upper Klamath Lake is 

hypereutrophic. The maximum iron-flux estimate for oligotrophic Coeur d’Alene Lake, Idaho (down 

gradient of the U.S. Environmental Protection Agency [EPA] Bunker Hill Superfund Site), was 1,100 

μg-Fe m

−2 d−1 (Kuwabara and others, 2000), as determined by core incubations that include the effects 

of bioturbation and bioirrigation. 

As with iron fluxes, dissolved-Mn flux estimates were consistently positive and varied from 

22.62 to 5,454 μg-Mn m

−2 d−1 (overall mean of 1,050 ± 902 μg-Mn m−2 d−1, n=66; table 6). These 

results are consistent with previous Mn-flux estimates for a study of the northern region of Upper 

Klamath Lake and adjacent wetlands (1,100 ± 1,600 μg-Mn m

−2 d−1, n=47; Kuwabara and others, 

2012b) and similar to those reported for Coeur d’Alene Lake, Idaho (1,900 ± 590 mg-Mn m

−2 d−1, 

n=12), an oligotrophic lake with surficial sediment deposits of iron and manganese oxyhydroxides 

(Kuwabara and others, 2000; Kuwabara and others, 2003). The diffusive dissolved-Mn flux observed in 

this study generates an areally averaged benthic flux of 210 ± 180 kg d−1 that exceeds by more than an 

order of magnitude the riverine Mn loads presented herein (8 ± 5 kg d−1, n=7; table 4). 

Arsenic (As), primarily as As(V) arsenate in oxic waters, is structurally similar to the essential 

nutrient phosphate and hence interferes with adenosine triphosphate (ATP) production. The disruption 

of this basic biochemical mechanism may be linked to a number of toxicological responses like 

neurotoxicity, cancer, and liver diseases (Debarshi and others, 2013). With associated concerns for 

human and aquatic-system health, the State of Oregon has recently lowered water-quality standards for 

arsenic down to 2.1 μg L−1 (Atkins, 2016). Unlike dissolved Fe and Mn, benthic flux of dissolved As 

was not consistently positive, varied from −117 to 253 μg-As m

−2 d−1 (overall mean of 18 ± 60 μg-As

m

−2 d−1, n=66; table 6), and was an order of magnitude lower in absolute value than Fe or Mn benthic 

flux. These results represent, to our knowledge, the first publication of benthic-flux measurements for 

dissolved As in any part of the UKL watershed, and so they are not directly comparable to previously 

published results from the lake. However, using peeper deployments, Martin and Pedersen (2002) 
13

reported much higher and consistently positive benthic flux for dissolved (0.45-μm filtered) arsenic that 

varied from 4,904 to 10,411 μg-As m

−2 d−1 in mining-impacted Balmer Lake, Ontario, Canada. The 

diffusive dissolved-As flux observed in this study generates an areally averaged benthic flux of 4 ± 12 

kg d−1 that is of similar magnitude to the estimated maximum spring As loads from the lake’s inlet 

tributaries presented herein (3 ± 4 kg d−1, n=7; table 4). In their study of the Link River, immediately 

downgradient of Upper Klamath Lake, Sullivan and Rounds (2016) determined instantaneous loads of 

dissolved (0.45-μm filtered) arsenic of 15.4 ± 5.2 kg d−1 (n=9; a range from 8.7 to 21.7 kg d−1). That As 

load was primarily in the dissolved phase (92.6 ± 9.0 percent; n=9). In comparison to SRP and 

ammonia, benthic sources (and sometimes sinks) for dissolved As appear to play a lesser role in overall 

arsenic transport.

Very little is known about vanadium (V) distribution and transport in Upper Klamath Lake, and 

it has heretofore garnered minimal interest. The magnitude of dissolved-V benthic flux was clearly 

lower than the magnitude of dissolved Fe or Mn (table 6). However, a brief discussion of V fluxes is 

provided because they are similar in magnitude to dissolved As and two orders of magnitude greater 

than dissolved cobalt, nickel, copper, zinc, cadmium, and lead. Vanadium distributions in rivers and 

estuaries have been studied because of vanadium’s association with certain uranium deposits, 

enrichment in bituminous materials, and redox geochemistry (Shiller and Boyle, 1987). The linkage 

between P and V owing to competitive scavenging when hydrothermal precipitates form (Feely and 

others, 1998) may also be pertinent to subsequent P and V remobilization from similar precipitates in 

the Klamath River Basin (Morford and Emerson, 1999). As one might expect of an adsorbing anion, 

vanadium flux was predominantly negative, varying between −179 and 3 μg-V m−2 d−1 (overall mean of 

−53 ± 35 μg-V m−2 d−1

, n=66; table 6). Dissolved-V benthic flux observed in this study generates an 

areally averaged benthic flux of −11 ± 7 kg d−1. In contrast, Harita and others (2005) modeled a positive 

V benthic flux from Lake Biwa, Japan, of 7 ± 3 μg-V m−2 d−1, based on seasonal changes in pH and 

water-column V. Consistent with Harita’s modeling efforts, there was a positive correlation between pH 

and dissolved water-column V in this study (r = 0.43, p<0.01, n = 72). However, a negative rather than 

positive correlation between 2013 V benthic fluxes and pH (r = −0.34, p<0.01, n = 62) is in contrast to 

the Lake Biwa work and suggests site-specific differences in V and adsorbent speciation. 

In comparison to iron and manganese fluxes, benthic fluxes for dissolved cobalt, nickel, copper, 

zinc, cadmium, and lead were at least three orders of magnitude lower than either dissolved iron or 

manganese fluxes (table 6). This observation is consistent with previously reported results from Upper 

Klamath Lake (Kuwabara and others, 2012a). Some of these dissolved trace metals (for example, 

copper and zinc) may generate reduced rates of primary production when they exist in the free-ion form. 

Although a decline in primary production may help mitigate eutrophic and suboxic conditions, the mean 

flux of both copper and zinc is negative and hence is not expected to exacerbate any toxic effects on 

primary production (Kuwabara and Leland, 1986; Stauber and Florence, 1987). In addition, both copper 

and zinc would thermodynamically favor chelation with dissolved humic substances, represented herein 

as DOC (Donat and others, 1994), over the free-ion form.

Dissolved (0.2-μm Filtered) Trace Elements in the Water Column in 2013

Volcanic pumice, a dominant component of UKL inorganic sediment, generates consistently low 

particulate trace-metal concentrations relative to other lacustrine environments (Martin and Rice, 1981). 

Although information about dissolved, and hence more biologically available, trace metals in Upper 

Klamath Lake is sparse (Kuwabara and others, 2007; Kuwabara and others, 2009), existing data 

generally reflect a trace-metal-depleted environment in the dissolved phase. Data described in this 

section represent dissolved trace-element concentrations from surface-grab samples collected 
14

approximately 0.5 m below the surface using a clean technique (“Methods” section; table 7). As 

previously reported for Upper Klamath Lake, many of those dissolved metals (for example, cadmium, 

cobalt, lead, nickel, and zinc) were present at dissolved concentrations below or near detection limits 

(Kuwabara and others, 2009; Kuwabara, 2012a). The discussion below focuses on water-column traceelement data for dissolved iron (Fe), manganese (Mn), and arsenic (As). These trace elements are of 

particular interest because they are potentially limiting micronutrients or toxicants, and their 

concentrations are elevated relative to the other dissolved trace elements.

Concentrations of dissolved Fe, an essential micronutrient for primary production, varied 

between 4.51 and 29.29 μg-Fe L−1 (overall mean of 9.10 ± 5.17 μg-Fe L−1, n=75; table 7). That is, 

dissolved-Fe depletion in the lake water column was not observed at any site, as one would expect if Fe 

served consistently as a limiting nutrient (Kuwabara and others, 2009). Furthermore, there was no 

difference in dissolved-Fe concentrations between sampling dates as the intial AFA bloom developed. 

Concentrations of dissolved Mn, an essential micronutrient for primary production, in the 2013 

spatial study exhibited a range from 0.13 to 7.27 μg-Mn L−1 (overall mean of 0.79 ± 0.94 μg-Mn L−1, 

n=75; table 7). During the first sampling, on May 23, 2013, as the AFA bloom began, dissolved Mn 

varied between 0.86 and 7.27 μg-Mn L−1 (mean of 1.05 ± 2.72 μg-Mn L−1

, n=24). By comparison, 

dissolved Mn in the lake water column on June 13, 2013, as the bloom progressed, varied between 0.13 

and 0.74 μg-Mn L−1 (sampling-date mean of 0.42 ± 0.10 μg-Mn L−1, n=24). As the initial AFA peak 

was approached on July 3, 2013, dissolved Mn rebounded to higher values relative to June 13 (range 

from 0.28 to 1.66 μg-Mn L−1, sampling-date mean of 0.86 ± 0.28 μg-Mn L−1, n=24). Despite the 

observed temporal trend with the development of the initial AFA bloom in 2013, all samples had 

detectable dissolved Mn; that is, Mn depletion in the water column was not evident. 

Dissolved As varied between 3.62 and 12.58 μg-As L−1 (overall mean of 7.34 ± 1.94 μg-As L−1, 

n=76; table 7). As the initial AFA bloom developed, dissolved-As concentrations increased (5.74 ± 2.31 

μg-As L−1, n=24, on May 23, 2013; 7.24 ± 0.68 μg-As L−1, n=24, on June 13, 2013, and 9.51 ± 1.94 μgAs L−1, n=24, on July 3, 2013). This temporal behavior will be discussed in greater detail in the 2014 

water-column trace-element section below. 

Aside from Fe, Mn, and As, dissolved-copper (Cu) concentrations in the water column were also 

consistently above detection limits (>0.01 μg-Cu L−1), with a range from 0.02 to 1.22 μg-Cu L−1

(overall mean of 0.24 ± 0.21 μg-Cu L−1, n=75; table 7). Dissolved-vanadium (V) concentrations were 

even higher, with a range from 7.55 to 13.60 μg-V L−1 (overall mean of 9.86 ± 1.34 μg-V L−1, n=75; 

table 7). Although not a primary focus of this study, summary statistics are presented for dissolved Cu 

and V because of their magnitude relative to the remaining five trace elements and their consistent 

detectability. Although both have biogeochemical significance (Cu as a micronutrient and potential 

toxicant, and V, as vanadate, as a competitive adsorbate with orthophosphate), the environmental 

significance of these solutes within UKL has yet to be demonstrated. The variability in dissolved traceelement concentrations observed in our 2013 spatial study may reflect processes (for example, air 

temperatures, wind directions, and current patterns) that operate over multiple time scales, some much 

shorter than the frequency of profiler deployments in 2013. Therefore, a greater temporal resolution of 

this variability was a primary objective the UKL studies in 2014. 

Benthic Chlorophyll in 2013

As pelagic phytoplankton grow, senesce, and settle to the lakebed, benthic-chlorophyll 

measurements can help track that deposited carbon load. Based on the elevated algal biomass generated 

in the water column of hypereutrophic aquatic systems like Upper Klamath Lake, one might expect 

changes in benthic chlorophyll as blooms wax and wane. In 2013, benthic chlorophyll varied between 
15

0.5 and 52.8 micrograms per square centimeter (μg cm−2) (overall mean of 5.44 ± 8.36 μg cm−2, n=46; 

table 8). The ratio of benthic chlorophyll to the sum of benthic chlorophyll plus pheophytin increased 

between the May (0.26 ± 0.12, n=21) and June (0.37 ± 0.17, n=21) samplings. Corresponding mean 

values for benthic chlorophyll were 3.82 μg cm−2

 in May and 7.89 μg cm−2 in June, and for pheophytin 

were 9.89 μg cm−2 in May and 8.60 μg cm−2 in June. Among habitats, the ratio of benthic chlorophyll to 

the sum of benthic chlorophyll plus pheophytin, during May and June samplings, was lowest at the 

trench sites (0.18 ± 0.06, n=6) and highest at the littoral and open-lake sites (0.41 ± 0.15 and 0.27 ± 

0.13, respectively, n=18). The narrow, deep morphology of the trench may be prone to scouring events 

that minimize the accumulation of benthic chlorophyll. Prior to the bloom on May 23, 2013, benthic 

chlorophyll at littoral sites varied between 2.6 and 9.5 μg cm−2 (habitat mean for that date was 6.7 ± 2.7 

μg cm−2, n=9). At open-lake sites, benthic chlorophyll was lower, with a range from 0.5 to 3.1 μg cm−2

(habitat mean of 1.7 ± 0.8 μg cm−2, n=9). As the bloom progressed, planktonic carbon was transported 

horizontally and vertically, and habitat differences were mitigated. On June 13, 2013, only 3 weeks after 

the previous sampling trip, benthic chlorophyll measurements for the littoral and open-lake sites were 

no longer different and more variable within habitat sites than observed on May 23 (9.5 ± 6.1 and 8.6 ± 

11.6 μg cm−2, respectively, n=9). Such changes in benthic carbon distributions may affect the 

distribution of benthic invertebrates and hence solute efflux by their respective communities. Growth 

and subsequent settling of phytoplankton augment the benthic carbon source that supports microbial and 

macroinvertebrate assemblages near the sediment-water interface with feeding and foraging 

mechanisms, and in so doing, may seasonally enhance the benthic flux of solutes (Kuwabara and others, 

1999; Boudreau and Jorgensen, 2001). 

Benthic pheophytin varied between 1.9 and 29.2 μg cm−2

 (overall mean of 8.81 ± 5.43 μg cm−2, 

n=46). In comparison to benthic-chlorophyll concentrations, pheophytin concentrations at trench sites 

were similar to the rest of lake (5.9 ± 2.5 μg cm−2, n=6). Furthermore, benthic pheophytin prior to and 

during the bloom were similar (9.89 ± 5.67 and 8.60 ± 5.34 μg cm−2 on May 23 and June 13, 

respectively, n=21). 

As a coarse indicator of the reproductive status of the benthic algal community, the ratio of 

benthic chlorophyll to the sum of pheophytin plus benthic chlorophyll varied between 0.10 and 0.74 

(overall mean of 0.34 ± 0.29, n=46). Pheopigments represent degradation products of chlorophyll, so the 

fact that only 4 of 46 tabulated ratios are ≥0.5 (table 8) suggests that phytoplankton cells degrade or 

senesce then settle to the lakebed, or that active benthic invertebrate communities consume benthic 

algae (or settled phytoplankton) to generate feces rich in pheopigments (Thompson and others, 1981; 

Light and John, 1998; U.S. Environmental Protection Agency, 2000). 

Benthic Invertebrate Assemblages in 2013

Average invertebrate density over the study period in 2013 was 12,610 ± 7,505 individuals 

(inds) m−2

 (n=63) (table 9). Each of the 63 samples represented the mean of 3 Ekman grabs. 

Oligochaeta, Chironomidae, and Hirudinea represented 57, 25, and 15 percent, respectively, of all 

organisms identified. Average taxon richness (number of unique taxa identified) per sample was 16.1 ± 

3.7 (n=63) and ranged from 8.7 to 26.0. Total taxon richness (total number of unique taxa identified) 

was 74; 24 were oligochaetes, 14 were chironomids, and 10 were hirudineans. 

Average invertebrate densities during each of the three collecting dates were very similar. Total 

densities on May 23, June 13, and July 3, 2013, were 11,888 ± 6,858 inds m−2

 (n=21), 12,945 ± 7,376 

inds m−2

 (n=21), and 12,998 ± 8,506 inds m−2 (n=21), respectively. Oligochaetes decreased in 

composition from a high of 66 percent to 50 percent of the total density in samples over the study 

period. Chironomids remained approximately the same at 25 percent, and hirudineans increased from a 
16

low of 6 percent in May 2013 to a high of 21 percent in July 2013. These changes in composition are 

generally a function of the life histories of the taxa over this part of the late spring and early summer 

months. 

2014—Temporal Variability (Weekly Interval) Study 

Results of the 27-week Upper Klamath Lake sampling in 2014 were summarized in three 

temporal periods, each period involving 9 weeks of profiler deployments, water column and benthic 

sampling. Based on water-chlorophyll measurements taken weekly at the profiler-deployment sites 

(table 11), the first period, representing pre-bloom conditions, spanned the period between April 15 

through June 10, 2014. The annual cyanobacterial blooms marked the second period between June 17 

and August 13, 2014. Post-bloom conditions were represented by a third period, between August 20 and 

October 16, 2014. Tables presenting results from the 2014 sampling effort (tables 10–15) have shaded

blocks of rows to distinguish the bloom period from the unshaded pre-bloom and post-bloom sampling 

periods. 

Ancillary Parameters in 2014

As in 2013, vertical water-column profiles of ancillary parameters (depth, temperature, specific 

conductivity, pH, and dissolved oxygen) were collected using sonde deployments. The sampling 

proximity around open-lake site ON01, the area of focus for 2014 temporal study, was <3 m in depth. In 

comparison with 2013 sonde data (tables 2 and 10), specific conductivity values in 2014 varied over an 

even narrower range (112 to 142 μS cm−1). Similar to 2013 results, lowest values for specific 

conductivity (115 ± 3 μS cm−1

, n=18) were observed during the pre-bloom period between April 16 

through June 11, 2014, as snow melt diluted lake waters. By comparison, as the cyanobacterial blooms 

progressed (between June 18 through August 13, 2014), the mean specific conductivity was 121 ± 6 μS 

cm−1 (n=18). In July 2013, much higher specific conductivity was observed (210 ± 76 μS cm−1, n=40) 

relative to 2014, an indication of interannual variability of water quality flowing into the lake or 

evaporation rates. Following the bloom period (between August 20 and October 16, 2014), mean 

specific conductivity increased (129 ± 4 μS cm−1, n=18) as temperature and evaporation increased 

relative to pre-bloom conditions. Consistent with 2013 results, water-column temperatures were lowest 

during pre-bloom period (14.7 ± 2.8 °C) and expectedly peaked during the cyanobacterial blooms (21.4 

± 2.3 °C, table 10). Water temperature then declined during the post-bloom period (16.1 ± 3.0 °C). 

As observed in 2013, dissolved oxygen (DO) consistently decreased with depth (table 10).

During the pre-bloom period, the decrease with depth in percent DO saturation was 21 ± 26.5 percent

(range from 1.1 to 79.7 percent decreased DO saturation with depth, n=9). During the cyanobacterial 

blooms between June 18 and August 13, 2014, the decrease in percent DO saturation with depth was 

36.0 ± 22.9 percent (range from 9.1 to 72.4 percent, n=6). After the bloom period, that is, during the 

August 20 to October 16, 2014, deployments, the mean decrease in percent DO saturation with depth 

was 10.8 ± 10.0 percent (range from 0.2 to 23.9 percent, n=9), suggesting that the benthic oxygen 

demand, generated by settled cyanobacterial biomass, was quickly dissipated following the bloom. 

Consistent with 2014 phytoplankton-bloom dynamics in the lake, mean pH during pre-bloom 

deployments (8.0 ± 0.5, n=18) increased during the bloom period (9.4 ± 0.5, n=18), then decreased 

following the bloom (7.8 ± 0.6, n=18; table 10). As mentioned above in the discussion of 2013 

ancillary-parameter results, pH above the negative logarithm of the acid dissociation constant for the 

ammonium ion (9.3) may generate environmental concerns by imposing a variety of toxicity and 

nutrient-deficiency problems. However, the coincidence of both high pH and elevated ammonia 

concentrations in the UKL water column is episodic and infrequent (tables 2, 5, 10, and 13). 
17

Benthic Flux of Macronutrients in 2014

During all three sampling periods, SRP benthic flux was consistently positive (table 12). SRP 

flux during the pre-bloom period varied between 1.55 and 4.26 mg-P m−2 d−1 (2.89 ± 0.96 mg-P m−2 d−1, 

n=9). This represents an areally averaged SRP (as P) load to the lake water column of 578 ± 192 kg d−1, 

assuming a lake area of 200 km2. By comparison, the mean SRP load from the inlet tributaries, sampled 

in the spring and early summer of 2013 and 2014 (table 4), was 133 ± 15 kg d−1 (n=7). As the summer 

cyanobacterial blooms (AFA then additional species like Microcystis aeruginosa) progressed during the 

bloom period, SRP flux displayed greater variability relative to pre-bloom conditions, varying between 

1.17 and 5.77 mg-P m−2 d−1 (2.80 ± 1.71 mg-P m−2 d−1, n=9). Following the blooms, SRP fluxes 

decreased to as low as 0.60 mg-P m−2 d−1, then rebounded in the final 3 weeks of sampling to 1.74 mg-P 

m−2 d−1 toward pre-bloom conditions (mean of 1.41 ± 0.69 mg-P m−2 d−1, n=9; lowest of the three 

periods). Over the 27-week sampling period, the mean SRP flux was 2.37 ± 1.35 mg-P m−2 d−1 (n=27), 

representing an areally averaged SRP load to the lake water column of 474 ± 270 kg d−1, again higher 

than the spring estimates for SRP tributary loads. Note that tributary solute loads decrease during the 

summer as discharge decreases (for example, to zero in the Williamson River wetland headwaters; table

4). 

As observed in 2013 and previous studies (Kuwabara and others, 2012a), benthic flux of 

ammonia was consistently positive over the 27-week sampling period in 2014 (table 12). During the 

pre-bloom period, ammonia flux varied between 3.23 and 12.86 mg-N m−2 d−1 (pre-bloom mean of 6.75 

± 3.31 mg-N m−2 d−1

, n=9). This represents an areally averaged ammonia (as N) load to the lake water 

column of 1,350 ± 662 kg d−1. By comparison, the mean ammonia load from the inlet tributaries, 

sampled in the spring and early summer of 2013 and 2014 (table 4), was an order of magnitude lower 

(82 ± 112 kg d−1, n=7). During the bloom period, when a succession of cyanobacteria appeared, 

ammonia flux (like SRP) became more temporally variable than pre-bloom conditions, varying between 

3.95 and 20.62 mg-N m−2 d−1 (11.11 ± 5.18 mg-N m−2 d−1

, n=9). Following the blooms, ammonia flux 

decreased to as low as 1.74 mg-N m−2 d−1, then rebounded (as observed with SRP flux) in the final 3 

weeks of sampling to 6.48 mg-N m−2 d−1, approaching the pre-bloom mean flux provided above (postbloom mean of 5.97 ± 4.58 mg-N m−2 d−1, n=9). The 2014 overall mean ammonia flux over the 27-week 

sampling period was 7.94 ± 4.84 mg-N m

−2 d−1 (n=27). 

Consistent with 2013 results, and in contrast to SRP and ammonia fluxes, no positive nitrate 

fluxes were observed in 2014, varying between −3.50 and 0.00 mg-N m−2 d−1, with an overall mean of 

−0.68 ± 1.11 mg-N m−2 d−1 (n=27, table 12). This represents an areally averaged nitrate (as N) load to 

the lake of between −700 to 0 kg d−1 (that is, a net nitrate sink to the lakebed). During the pre-bloom 

period, nitrate flux varied between −2.16 and 0.00 mg-N m−2 d−1 (−0.41 ± 0.73 mg-N m−2 d−1, n=9).

Note that a 0.00 calculated flux indicates nondetectable flux because all profiler samples across the 

sediment-water interface were below detection limits (for nitrate, <0.01 mg-N L−1). During the 

cyanobacterial bloom period that followed, nitrate flux became less variable (−0.32 ± 0.48 mg-N m−2

d−1, n=9), again in contrast to SRP and ammonia fluxes. As a further contrast with SRP and ammonia 

fluxes, the post-bloom period provided the most negative and most variable nitrate fluxes, varying 

between −3.50 and 0.00 mg-N m−2 d−1 (post-bloom mean of −1.29 ± 1.61 mg-N m−2 d−1, n=9).

As observed in 2013 and previous studies, benthic fluxes for dissolved silica were consistently 

positive, that is, a benthic silica source to the lake water column, as observed for SRP and ammonia. 

During the pre-bloom period, silica flux varied between 10.58 and 72.37 mg-SiO2 m

−2 d−1 (41.68 ± 

22.41 mg-SiO2 m

−2 d−1

, n=9; table 12). This benthic flux represents an areally averaged dissolved silica 

(as SiO2) load to the lake water column of 8,340 ± 4,480 kg d−1. By comparison, the mean silica load 

from the inlet tributaries, sampled in the spring and early summer of 2013 and 2014 (table 4), was an 
18

order of magnitude higher (90,600 ± 15,700 kg d−1, n=6). That is, the benthic silica source is less 

dominant over the riverine source, relative to SRP and ammonia. During the cyanobacterial bloom

period, dissolved-silica flux varied between 27.16 and 106.52 mg-SiO2 m

−2 d−1 (53.37 ± 26.02 mg-SiO2

m

−2 d−1

, n=9). Following the blooms, silica flux decreased to as low as 6.38 mg-SiO2 m

−2 d−1, but as 

observed for SRP and ammonia fluxes, silica flux rebounded in the final weeks of sampling to 18.11 

mg-SiO2 m

−2 d−1, toward pre-bloom conditions (post-bloom mean of 15.48 ± 10.49 mg-SiO2 m−2 d−1, 

n=9). The 2014 overall mean silica flux was 36.85 ± 25.64 mg-SiO2 m

−2 d−1 (range from 6.38 to 106.52 

mg-SiO2 m

−2 d−1, n=27), representing an areally averaged silica load to the lake water column of 7,370 

± 5,130 kg d−1, much lower than the spring estimates for silica tributary loads (90,600 ± 15,700 kg d−1, 

n=7). 

As observed in 2013, and similar to nitrate flux, benthic flux of dissolved organic carbon (DOC) 

was often negative (removed from the water column to the lakebed), particularly during the 

cyanobacterial bloom period (table 12). During the pre-bloom period, DOC flux varied between −3.63 

and 0.78 mg-C m−2 d−1 (−0.64 ± 1.41 mg-C m−2 d−1, n=9). In the bloom period that followed, DOC flux 

became more negative and variable (−8.57 ± 3.46 mg-C m−2 d−1, n=9). As a mirror image of the 2014 

SRP and ammonia flux time series, the DOC flux peaked after the bloom on September 17, 2014 (11.53 

mg-C m−2 d−1 with a post-bloom mean of −0.22 ± 5.46 mg-C m−2 d−1, n=9), then reverted back to 

negative values during the final sampling weeks. Over the 27-week sampling period, DOC flux varied 

between −14.37 and 11.53 mg-C m−2 d−1, with a mean of −3.15 ± 5.36 mg-C m−2 d−1 (n=27). This 

represents an areally average DOC (as C) load to the lake of between –2,870 and 2,310 kg d−1. In 

contrast to the greater magnitudes of SRP and ammonia benthic fluxes over riverine loads, DOC loads, 

measured in the spring and early summer of 2013 and 2014, were between 1,874 and 8,922 kg d−1

(4,322 ± 2,525 kg d−1, n=7). As observed for dissolved silica, benthic DOC sources were not dominant 

over riverine sources to the lake.

Dissolved (0.2-μm Filtered) Nutrients in the Water Column in 2014

During the 2014 temporal study, SRP varied between 0.02 and 0.25 mg-P L−1 (overall mean of 

0.08 ± 0.07 mg-P L−1, n=27; table 13). These results are consistent with previous monitoring studies of 

the Upper Klamath Lake and adjacent Agency Lake to the north (Wong and others, 2011). During the 

pre-bloom period, SRP varied between 0.02 and 0.09 mg-P L−1 (mean of 0.05 ± 0.02 mg-P L−1, n=9). 

SRP increased in the lake water column during the subsequent bloom period, with a range from 0.04 to 

0.25 mg-P L−1 (mean of 0.15 ± 0.07 mg-P L−1, n=9), a somewhat counterintuitive behavior if SRP 

served consistently as the limiting nutrient. Finally, during the post-bloom period, SRP decreased to prebloom levels, varying between 0.02 and 0.11 mg-P L−1 (mean of 0.03 ± 0.03 mg-P L−1, n=9). Again, this 

temporal trend over the three bloom-related periods, over 27 consecutive weeks, is not what would be 

expected if phosphorus served consistently as the limiting nutrient for primary production. Furthermore, 

as observed in 2013, all samples had detectable SRP (SRP depletion did not occur in the lake water 

column). 

Dissolved-ammonia concentrations varied from <0.007 to 0.600 mg-N L−1 (overall mean of 0.11 

± 0.16 mg-N L−1, n=27; table 13). Consistent with 2013 results, there were 7 of 27 weeks where 

undetectable dissolved ammonia concentrations were observed (that is, <0.007 mg-N L−1), all prior to 

the highest observed chlorophyll concentrations on July 2 and July 8, 2014 (that is, 257 and 250 μg L−1, 

respectively). The pre-bloom period displayed the lowest dissolved ammonia, between <0.007 to 0.070 

mg-N L−1 (mean of 0.02 ± 0.02 mg-N L−1

, n=9). Coupled with elevating temperatures during the prebloom period (table 10), as well as low water-column nitrate and nitrite, these low dissolved inorganic 

nitrogen (DIN) concentrations (ammonia plus undetectable nitrate and nitrite) would favor nitrogen-
19

fixing species like AFA (Beversdorf and others, 2013). Unlike 2013 results, un-ionized water-column 

ammonia concentrations in 2014 (table 10) were never observed above the LC50 threshold for Lost 

River suckers (0.45 mg-N L−1; Meyer and Hansen, 2002), which is likely indicative of interannual 

variability in geochemical and biological parameters. In addition, within-lake spatial variability may 

have contributed to the absence of observed LC50 exceedances in 2014. Ammonia concentrations at 

ON01 in 2013 (0.02 ± 0.03 mg-N L−1, range from 0.00 to 0.05 mg-N L−1, n=3) were found to be on the 

lower end of the distribution among all sites (0.13 ± 0.22 mg-N L−1, range from 0.00 and 1.03 mg-N 

L−1, n=77). Consistent with 2013 results, the subsequent bloom period displayed higher and more 

variable ammonia concentrations with a range from <0.007 to 0.600 mg-N L−1 (mean of 0.19 ± 0.24 

mg-N L−1, n=9). Although less variable than the bloom period, the post-bloom period did not generate 

the highest dissolved-ammonia concentrations in 2014, with a range from 0.025 to 0.243 mg-N L−1

(mean of 0.10 ± 0.09 mg-N L−1, n=9). This again is an indication of the interannual variability of 

interdependent geochemical and biological parameters, as phytoplankton succession involving both 

nitrogen-fixing (for example, AFA) and non-nitrogen-fixing cyanobacterium (for example, M. 

aeruginosa) occur over multiple time scales (Kann, 2006; Kann, 2010). It will be useful to compare 

water-column trends with the timing of phytoplankton taxonomic succession in Upper Klamath Lake 

when taxonomic data become available. 

As observed in 2013 and previously reported studies within Upper Klamath Lake (Kuwabara 

and others, 2009; Kuwabara and others, 2012a; Wood and others, 2006; Hoilman and others, 2008; 

Lindenberg and others, 2009, dissolved-nitrate concentrations were consistently low, with 5 of 27 watercolumn samples having undetectable concentrations (<0.01 mg-N L−1). Concentrations varied between 

<0.01 and 0.20 mg-N L−1 (overall mean 0.07 ± 0.06 mg-N L−1, n=27; table 13). During the pre-bloom 

period, nitrate varied from <0.01 to 0.12 mg-N L−1 (mean 0.07 ± 0.06 mg-N L−1, n=9). The bloom 

period that followed had the same nitrate range from <0.01 to 0.12 mg-N L−1 (mean 0.05 ± 0.04 mg-N 

L−1, n=9). The subsequent post-bloom period displayed similar water-column nitrate concentrations, 

varying between <0.01 and 0.20 mg-N L−1 (mean 0.11 ± 0.07 mg-N L−1, n=9). Neither the water column 

nor diffusive solute flux provided adequate nitrate in support of non-nitrogen-fixing primary producers 

(table 13); hence, the primary DIN source would be dissolved ammonia, when available. 

Over the entire 2014 sampling period, dissolved silica varied between 36.03 and 64.03 mg-SiO2

L−1 (overall mean of 49.92 ± 9.70 mg-SiO2 L−1, n=27; table 13). As observed in 2013, dissolved silica in 

the water column increased as the initial AFA bloom neared its peak and continued to increase as the 

bloom faded during the final post-bloom period of the study period. During the pre-bloom period, 

dissolved-silica concentrations were lowest, varying from 36.03 to 48.37 mg-SiO2 L−1 (mean of 41.70 ± 

4.44 mg-SiO2 L−1, n=9). In the bloom period that followed, dissolved silica exhibited a range from

36.78 to 60.59 mg-SiO2 L−1 (mean of 48.10 ± 9.14 mg-SiO2 L−1, n=9). Finally, during the post-bloom 

period, dissolved-silica concentrations were highest and least variable, displaying a range from 55.29 to 

64.03 mg-SiO2 L−1 (mean of 59.95 ± 3.17 mg-SiO2 L−1, n=9). This seasonal accumulation of dissolved 

silica in the water column is to be expected as cyanobacteria (with minimal silica requirements relative 

to diatoms) favorably compete for required nutrients following the spring diatom dominance. 

Water-column concentrations for DOC in 2014 were more variable than observed in 2013, with 

a range from 3.15 to 9.33 mg-C L−1 (overall mean of 5.44 ± 1.77 mg-C L−1, n=27; tables 3 and 13). The 

lowest DOC concentrations in the water column occurred in the pre-bloom period and varied between 

3.15 and 4.40 mg-C L−1 (mean of 3.83 ± 0.40 mg-C L−1, n=9). Concentrations increased during the 

subsequent bloom period, with a range from 4.17 to 9.33 mg-C L−1 (mean of 7.21 ± 1.66 mg-C L−1, 

n=9). During the post-bloom period, DOC concentrations varied between 4.45 and 6.95 mg-C L−1 (mean
20

of 5.29 ± 0.90 mg-C L−1, n=9). As seen with dissolved ammonia in 2014, the highest DOC 

concentrations occurred during the bloom period rather than the post-bloom period. 

Benthic Flux of Trace Metals in 2014

Similar to the macronutrient-flux discussion above, results of the 2014 benthic flux for trace 

elements are summarized in the same three temporal periods based on water-column chlorophyll 

measurements (table 11): a pre-bloom period (April 15 through June 10, 2014), a cyanobacterial bloom 

period (June 17 and August 13, 2014), and a post-bloom period (August 20 and October 16, 2014). 

Consistent with the discussion of 2013 trace-element benthic flux, 2014 fluxes are also discussed and 

tabulated in units of micrograms per square meter per day (μg m−2

 d−1), owing to lower solute detection 

limits for trace elements. 

Consistent with 2013 results, benthic flux for dissolved iron (Fe) was consistently positive (that 

is, the lake sediments were a consistent dissolved-Fe source to the water column throughout the 2013 

and 2014 sampling periods). In the pre-bloom period, dissolved-Fe benthic flux attained its highest 

values and varied between 1,917 and 6,058 μg-Fe m−2 d−1 (overall mean of 3,582 ± 1,448 μg-Fe m

−2

d−1, n=9; table 14). This represents an areally averaged dissolved Fe load to the lake water column (as 

Fe) of 708 ± 290 kg d−1. These values are consistent with those reported previously for three sites in the 

northern region of the lake over a 4-year study (3,881 ± 3,640 μg-Fe m

−2 d−1, n=19; Kuwabara and 

others, 2012a). By comparison, estimates of dissolved-Fe loads from the inlet tributaries, sampled in the 

spring and early summer of 2013 and 2014 (table 4) were 169 ± 108 kg d−1 (n=7). During the bloom 

period, peaks in dissolved-Fe benthic flux typically lagged water-column chlorophyll and varied 

between 971 and 3,731 μg-Fe m

−2 d−1 (overall mean of 1,944 ± 1,018 μg-Fe m−2 d−1, n=9), converting to 

an areally lake-averaged dissolved-Fe load to the lake water column of 389 ± 204 kg d−1. In the final 

post-bloom period, dissolved-Fe benthic flux reached its lowest values but then rebounded (similar to 

SRP and ammonia) during the final 3 sampling weeks and varied between 948 and 4,290 μg-Fe m

−2 d−1

(overall mean of 2,151 ± 1,171 μg-Fe m

−2 d−1

, n=9). The associated dissolved-Fe benthic load to the 

lake water column was 430 ± 234 kg d−1. 

Dissolved-manganese (Mn) flux estimates were consistently positive with one exception, on July 

8, 2014 (that is, −6.12 μg-Mn m

−2 d−1), coincident with the initial AFA peak (table 11). As with 

dissolved Fe in the pre-bloom period, dissolved-Mn benthic flux attained their highest values and varied 

between 331 and 1,290 μg-Mn m−2 d−1 (mean of 710 ± 298 μg-Mn m

−2 d−1

, n=9; table 14). This 

represents an areally averaged dissolved-Mn load to the lake water column (as Mn) of 142 ± 60 kg d−1. 

These results are similar to previous Mn-flux estimates for a study of the northern region of Upper 

Klamath Lake and adjacent wetlands (1,100 ± 1,600 μg-Mn m−2 d−1, n=47; Kuwabara and others, 

2012b) but lower than those reported for Coeur d’Alene Lake, Idaho (1,900 ± 590 μg-Mn m−2 d−1, 

n=12), where surficial Mn oxyhydroxides were visually abundant on the lakebed when Ekman grabs 

were collected. By comparison, estimates of dissolved-Mn loads from the inlet tributaries, sampled in 

the spring and early summer of 2013 and 2014 (table 4), were lower (8 ± 5 kg d−1, n=7). During the 

bloom period, dissolved-Mn benthic flux decreased relative to the pre-bloom period and varied between 

−6.12 and 430 μg-Mn m−2 d−1 (mean of 282 ± 124 μg-Mn m−2 d−1, n=9), converting to an areally lakeaveraged dissolved-Mn load to the lake water column of 56 ± 25 kg d−1. In the final post-bloom period, 

dissolved-Mn benthic flux varied between 135 and 591 μg-Mn m

−2 d−1 (mean of 335 ± 140 μg-Mn m−2

d−1

, n=9). The associated dissolved-Mn benthic load to the lake water column was 67 ± 28 kg d−1, again 

higher than the maximum riverine Mn loads estimated herein (8 ± 5 kg d−1, n=7). Unlike SRP, 

ammonia, and Fe, there was no clear rebound of Mn benthic flux in the final sampling weeks of the 

post-bloom period. 
21

Consistent with 2013, both positive and negative As fluxes were observed in 2014. In the prebloom period, dissolved-As benthic flux gradually decreased from positive to negative values, varying 

between 26.24 and 8.47 μg-As m−2 d−1 (mean of 4.41 ± 13.33 μg-As m

−2 d−1, n=9; table 14), an order of 

magnitude lower than Mn benthic flux. The mean As flux represents an areally averaged dissolved-As 

load to the lake water column (as As) of 0.9 ± 2.7 kg d−1. By comparison, estimates of dissolved-As

loads from the inlet tributaries, sampled in the spring and early summer of 2013 and 2014 (table 4), 

were lower (4 ± 3 kg d−1, n=7). During the bloom period, dissolved-As benthic flux further decreased 

relative to the pre-bloom period and varied between −39.33 and −12.70 μg-As m−2 d−1 (mean of −23.97 

± 9.82 μg-As m

−2 d−1, n=9), converting to an areally lake-averaged dissolved-As load to the lake water 

column of −4.8 ± 2.0 kg d−1. In the final post-bloom period, dissolved-As benthic flux attained both 

negative and positive extremes, varying between −47.94 μg-As m

−2 d−1 immediately following the end 

of the bloom to 70.56 μg m

−2 d−1 only 3 weeks after (mean of 15.73 ± 43.58 μg-As m−2 d−1

, n=9). The 

associated dissolved As benthic load to the lake water column was 3.15 ± 8.72 kg d−1, similar to the 

aforementioned maximum riverine As loads estimated herein (4 ± 3 kg d−1, n=7). During the post-bloom 

period, the rebound of As benthic flux occurred in the initial rather than final weeks of the post-bloom 

period, unlike SRP, ammonia, and Fe. Although benthic sources of As do not dominate riverine inputs 

as they do for SRP, ammonia, Fe, and Mn, it is important to note that As was the only solute in this 

study to exhibit a seasonal shift in the benthos as a source and a sink. This shorter-term response relative 

other solutes may be a useful factor in designing and monitoring TMDL programs for arsenic, if needed. 

In the pre-bloom period, dissolved-V benthic flux was consistently negative, varying between 

−97.57 and −14.88 μg-V m−2 d−1 (mean of −52.18 ± 26.53 μg m−2 d−1, n=9; table 14). The negative V

flux represents dissolved-V loss from the lake water column to the lakebed (areally averaged as V loss, 

−10.4 ± 5.3 kg d−1). The subsequent bloom period generated similarly negative dissolved-V benthic flux 

varying between −125.05 and −18.26 μg-V m−2 d−1 (mean of −61.86 ± 40.81 μg-V m−2 d−1, n=9), 

converting to an areally lake-averaged dissolved-V loss from the water column to the lakebed (−12.4 ± 

8.2 kg d−1). In the final post-bloom period, dissolved-V benthic flux was similar to those observed 

during the pre-bloom period and again consistently negative. Flux varied between −95.95 and −27.47 

μg-V m−2 d−1 (mean of −52.23 ± 22.11 μg-V m−2 d−1, n=9). The associated dissolved V benthic loss 

from the lake water column was −10.4 ± 4.4 kg d−1. The predominantly negative 2014 V fluxes through 

all three sampling periods of varying pH were consistent with those observed in 2013. However, they 

again contrast the modeled positive, pH-dependent V benthic flux for Lake Biwa, Japan, by Harita and 

others (2005), based on seasonal changes in water-column V. Aside from bathymetric and 

hydrodynamic differences between Lake Biwa (41 m average depth) and Upper Klamath Lake (2.5 m 

average depth), the site-specific discrepancy between the results is due to a lack of pH dependence on V 

flux in Upper Klamath Lake. That is, as pH increased during the bloom period, V benthic flux 

(presumably incurred by anion desorption) did not increase and in fact remained negative. We 

hypothesize that these flux differences may be due to differences in chemical speciation of V 

(adsorbate) and sediment (adsorbent) between lakes. 

As observed in the 2013 spatial study, benthic fluxes for dissolved cobalt, nickel, copper, zinc, 

cadmium, and lead were at least three orders of magnitude lower than either dissolved iron or 

manganese fluxes (table 14). Although some of these dissolved trace metals (for example, cobalt, 

copper, and zinc) serve as essential micronutrients, they may also generate a toxicological response on 

primary production when they exist in the free-ion form at elevated concentrations. For example, Martin 

and Pedersen (2002) noted that treatment and removal of toxic trace metals from point sources flowing 

into mining-impacted Balmer Lake, Ontario, Canada, caused the lake to become more productive. With 

a greater carbon source from the water column to the lakebed, they hypothesized that benthic oxygen 
22

demand following enhanced pelagic primary production generated reducing conditions that remobilized 

other toxic elements like As. Although Martin and Pedersen (2002) observed this trend of increasing As

benthic flux over multiyear time scales, the basic geochemical mechanisms are consistent with the 

temporal trends in As benthic flux observed here over weekly time scales. As noted with the 2013 

results, there is no indication of a toxic response to elevated dissolved trace elements like copper and 

zinc. 

Dissolved (0.2-μm Filtered) Trace Elements in the Water Column in 2014

Consistent with the discussion of water-column trace elements in 2013, this section will focus on 

a summary of data for dissolved iron (Fe), manganese (Mn), and arsenic (As). As previously mentioned, 

there is particular interest in these solutes as potentially limiting micronutrients or toxicants and 

concentration exceedances relative to recently revised water-quality standards. Although not discussed 

below, initial water-column concentrations for seven other trace elements are provided (table 15), all 

with biogeochemical significance, but without current water-quality implications for UKL. Should 

future concerns regarding these trace elements arise, this study provides initial data for reference, 

analysis, and modeling. For example, arsenic was not initially a focus of this study until recent revisions 

in water-quality standards in the State of Oregon. 

For dissolved Fe, concentrations varied between 5.48 and 42.40 μg-Fe L−1 (overall mean of 

21.02 ± 9.23 μg-Fe L−1, n = 26; table 15). Consistent with 2013 results, dissolved-Fe depletion in the 

lake water column was not observed in 2014. During the pre-bloom period, dissolved Fe exhibited a 

range from 5.48 to 26.81 μg-Fe L−1 (mean of 14.29 ± 7.12 μg-Fe L−1, n = 9). During the subsequent 

bloom period, dissolved Fe varied between 15.82 and 34.47 μg-Fe L−1 (mean of 21.18 ± 6.46 μg-Fe L−1, 

n = 8). In the final post-bloom period, the highest dissolved-Fe concentrations were observed, ranging

from 13.59 to 42.40 μg-Fe L−1 (mean of 27.59 ± 8.91 μg-Fe L−1, n = 9). In the bloom and post-bloom 

periods, Fe may repartition and remobilize as cyanobacterial cells senesce. 

Dissolved Mn in the 2014 temporal study exhibited a range from 0.05 to 10.26 μg-Mn L−1

(overall mean of 2.05 ± 2.10 μg-Mn L−1, n = 26; table 15). As with dissolved Fe, dissolved-Mn 

depletion in the lake water column was not observed in 2014. During the pre-bloom period, dissolved 

Mn exhibited a range from 0.05 to 2.98 μg-Mn L−1 (mean of 1.02 ± 1.04 μg-Mn L−1, n = 9). Dissolved 

Mn in the subsequent bloom period varied between 0.72 and 3.96 μg-Mn L−1 (mean of 1.69 ± 1.07 μgMn L−1, n = 8). Again, similar to dissolved Fe, the highest dissolved-Mn concentrations were observed 

in the post-bloom period, ranging from 0.42 to 10.26 μg-Mn L−1 (mean of 3.35 ± 2.95 μg-Mn L−1, n = 

9). Analogous to Fe, the essential micronutrient Mn may also repartition and remobilize once a 

cyanobacterial peak is achieved. 

Dissolved As varied between 2.79 and 11.94 μg-As L−1 (overall mean of 6.78 ± 2.65 μg-As L−1, 

n = 26; table 15), all well above detection limits (>0.01 μg-As L−1). During the pre-bloom period, 

dissolved As increased, but among the three periods, the lowest dissolved-As concentrations were 

observed during this period, varying between 2.79 and 6.44 μg-As L−1 (mean of 4.12 ± 1.18 μg-As L−1, 

n = 9). Consistent with 2013 water-column results, dissolved As within the subsequent bloom period 

continued to increase in concentration and variability, from 5.43 to 11.94 μg-As L−1 (mean of 8.79 ± 

2.68 μg-As L−1, n = 8). The post-bloom period displayed a contrast relative to the previous two periods 

for dissolved-As concentrations, which decreased during the post-bloom period from 10.09 to 6.16 μgAs L−1 (mean of 7.64 ± 1.19 μg-As L−1, n = 9). As mentioned in the previous section, dissolved-As 

benthic flux decreased between the pre-bloom and bloom periods (table 14) then generated the highest 

positive fluxes in the post-bloom period. This inverse relationship between water-column As and 

benthic flux is consistent with 2013 As results (tables 6 and 7), although greater spatial coverage in 
23

2013 generated greater parameter variability for a given date. In summary, as water-column As 

increased during the spring and early summer (the pre-bloom and bloom periods), As benthic flux 

coincidentally decreased. Conversely, as water-column As decreased during mid to late summer (the 

post-bloom period), As benthic flux increased. This inverse relationship is reasonable because watercolumn concentrations that overlay the lakebed inversely affect concentration gradients that are used to 

calculate diffusive flux (see “Methods” section). The relationship generates three desirable water-quality 

management implications for As. First, the interdependence between water-column As and benthic flux 

is apparent within a seasonal time scale, not observed for macronutrients like SRP and ammonia. 

Multiyear to decadal response times for macronutrients like SRP and ammonia have been reported 

(Kuwabara and others, 2012a). Second, riverine As loading plays a more important role in solute 

transport relative to macronutrients like SRP and ammonia, where lake water-column loading is 

principally from the lakebed (table 4), so the effects of regulations on riverine and other As point 

sources to the lake may be more quickly detected. Finally, the lakebed currently serves as both a solute 

source and sink that mitigates or buffers water-column As concentrations. 

Water-Column Chlorophyll in 2014

As with other ancillary parameters discussed above, water-column chlorophyll places the 

geochemical and benthic invertebrate results in the context of phytoplankton dynamics within the lake. 

In fact, water-column chlorophyll concentrations during the 2014 temporal study identified three 

distinct periods within the 27-week sampling period that aggregate and facilitate comparison of 

geochemical data. For example, overall, the water-column chlorophyll concentrations varied between 

4.5 and 257 μg L−1 (mean of 68.8 ± 80.2 μg L−1, n=27; table 11). However, the pre-bloom period 

between April 16 and June 11, 2014, generated a limited range of water-column chlorophyll, from 4.5 to 

66.9 μg L−1 (mean of 15.9 ± 19.7 μg L−1, n=9). During the subsequently identified bloom period 

between June 18 through August 13, 2014, water-column chlorophyll varied between 67.0 and 257 μg 

L−1 (mean of 164 ± 69.1 μg L−1

, n=9). The third and final post-bloom period between August 20 and 

October 16, 2014, generated expectedly lower water-column chlorophyll that ranged from 6.2 to 45.0 

μg L−1 (mean of 24.9 ± 13.5 μg L−1, n=9). Independent monitoring of water-column chlorophyll at 

another open-lake site in Upper Klamath Lake near the profiler deployment site ON01 yielded similar 

results, with an overall range from 6.4 to 283 μg L−1 (mean of 76.3 ± 71.6 μg L−1, n=21) between June 5 

and October 14, 2014 (U.S. Geological Survey, 2016). The pre-bloom data between June 5 and 11, 

2014, varied between 9.1 and 58.1 μg L−1 (mean of 33.6 ± 34.6 μg L−1, n=2). During the subsequent 

bloom period between June 16 through August 11, 2014, water-column chlorophyll varied between 39.0 

and 283 μg L−1 (mean of 126 ± 74 μg L−1, n= 9). The post-bloom period between August 18 and 

October 14, 2014, generated water-column chlorophyll that ranged from 6.4 to 122 μg L−1 (mean of 

35.1 ± 36.4 μg L−1, n= 9). 

Water-column pheophytin varied between 1.42 and 156.06 μg L−1 (overall mean of 21.12 ± 

37.75 μg L−1, n=27; table 11). By comparison, the pre-bloom period generated a pheophytin range from

1.42 to 5.74 μg L−1 (mean of 2.86 ± 1.22 μg L−1, n=9). In the subsequent bloom period, pheophytin 

varied between 11.59 and 156.06 μg L−1 (mean of 55.22 ± 51.37 μg L−1, n=9). The final post-bloom 

period generated lower pheophytin concentrations that ranged between 1.60 and 18.46 μg L−1 (mean of 

5.28 ± 5.10 μg L−1, n=9).

As mentioned above for benthic chlorophyll, the ratio of water-column chlorophyll to the sum of 

pheophytin plus water-column chlorophyll serves as a coarse indicator of the reproductive status of the 

dynamic phytoplankton communities in 2014 (Thompson and others, 1981; Light and John, 1998; U. S. 

Environmental Protection Agency, 2000). Over the entire temporal study, the ratio was expectedly 
24

higher and less variable than benthic chlorophyll determined in 2013 (table 8) and in previous studies 

(Kuwabara and others, 2009; Kuwabara and others, 2012a), varying between 0.62 and 0.92 (overall 

mean of 0.80 ± 0.09, n=27; table 11). Ratios during the pre-bloom period range from 0.69 to 0.92 (mean

of 0.79 ± 0.07, n=9). In the subsequent bloom period, those ratios varied between 0.62 and 0.90 (mean

of 0.78 ± 0.11, n=9). The final post-bloom period generated ratios with a range from 0.68 to 0.92 (mean

of 0.83 ± 0.09 μg L−1, n=9). In contrast to previous benthic-chlorophyll measurements, all 27 watercolumn ratios were >0.5 (that is, greater chlorophyll than degraded pheopigments). However, somewhat 

unexpectedly, there was no difference in these ratios between the three sampling periods. Perhaps, 

transport and turnover of benthic chlorophyll mitigate anticipated growth-phase effects. Geochemical 

and biological data reported herein provide indication of the interannual variability of interdependent 

geochemical and biological parameters as phytoplankton succession involving both nitrogen-fixing and 

non-nitrogen-fixing cyanobacteria occur over multiple time scales (Kann, 2006; Kann, 2010). It will be 

useful to compare water-column trends with the timing of phytoplankton taxonomic succession in 

Upper Klamath Lake when taxonomic data for 2013 and 2014 become available. 

Benthic Invertebrate Assemblages in 2014

Average invertebrate density over the 2014 study period was 19,796 ± 14,056 inds m−2 (n=27) 

(tables 16 and 17). Oligochaeta, Chironomidae and Hirudinea represented 66.6, 19.0, and 14.2 percent, 

respectively, of all organisms identified. Average taxon richness per sample (mean of 5 Ekman grabs) 

was 19.0 ± 3.7 (n=27) and ranged from 13.4 to 24.6. Total taxon richness was 45; 13 were oligochaetes, 

13 were chironomids, and 7 were hirudineans. 

Invertebrate data were partitioned into three time periods, which were identical to those used to 

summarize the physicochemical data. Average invertebrate densities during each of the three collecting 

periods were similar for the first two periods but were higher in the final sampling period. Total 

densities for the pre-bloom, bloom, and post-bloom periods were 10,643 ± 2,308 inds m−2 (n=21), 

12,668 ± 4,040 inds m−2 (n=21), and 36,078 ± 13,071 inds m−2 (n=21), respectively. Percentage of 

oligochaetes decreased from 70 percent during the first period to 55 percent during the middle period 

and then increased to 74 percent during the final period. Chironomids comprised a higher than typical 

percentage of the assemblage during the middle period, increasing from 18 percent to 26 percent and 

then deceasing to 12 percent during the last period. Percentage abundance of hirudineans was somewhat 

similar to chironomids, beginning at 11 percent, then increasing to 18 percent, and finally decreasing to 

13 percent in the final period. 

Invertebrate Composition Comparison Between 2013 and 2014

The estimate of mean densities for benthic invertebrates during 2013 and 2014 were 12,610 and 

19,796 inds m−2

, respectively. The difference likely was a function of the time period over which 

samples were collected (3 collecting dates in the beginning of summer in 2013 compared to 27 

collecting dates during 2014) and the spatial scale over which samples were collected (21 sites 

distributed throughout the lake and among three different habitats in 2013 compared to a single openlake site sampled on 27 consecutive weeks in 2014). The three sampling dates in 2013 were in late 

spring and early summer, coincident with the development of the summer AFA bloom. In contrast, 

samples in 2014 were collected from late spring through summer and into early autumn. As previously 

mentioned, weekly sampling in 2014 was partitioned into three periods based on water-column 

chlorophyll measurements: the pre-bloom period (April 15 through June 10, 2014), the bloom period 

(June 17 through August 13, 2014), and the post-bloom period (August 20 through October 16, 2014). 

Average invertebrate density was similar (~12,000 inds m−2

) among the three collecting dates in 2013 
25

and the first two collecting periods in 2014. The collecting dates in 2013 and the first two periods in 

2014 represent a somewhat similar seasonal period. Densities increased dramatically during the last 

sampling period in 2014. The increase in invertebrate density was represented by a fourfold increase in 

oligochaetes and a twofold increase in hirudineans. Helobdella hirudineans brood their young; 

consequently, it is apparent from tables 9 and 16, which show an increase in undetermined Helobdella

hatchlings, that their population increases during this part of the season. Chironomids also increased by 

approximately 25 percent during the third period. 

Invertebrate Composition Comparison to Previous Studies

Relatively few studies on the distributions of benthic invertebrates in Upper Klamath Lake have 

been completed prior to ours. Nevertheless, the few that have been completed allow a coarse assessment 

of whether total benthic invertebrate densities have dramatically changed over time and (or) whether 

patterns in abundances among major taxa have changed. One of the earliest studies was by Davis and 

others (1938) as reported in Hazel (1969). They reported approximately 1,262 chironomids m−2, almost 

identical to the 1,264 dipterans (almost all of which were Chironomidae) reported by Hazel (1969). We 

found total density and relative densities of oligochaetes, hirudineans, and chironomids during our 2013 

to be remarkably similar to those reported by Hazel (1969) approximately 50 years ago.

Season and time period over which benthic invertebrates are collected can have a large influence 

on estimated densities. As noted above, average densities during the studies in 2013 and 2014 differed, 

with densities in general being much higher during the 2014 study than in the 2013 study. Kuwabara 

and others (2009), using the same sampling and processing techniques as we used in 2013 and 2014, 

found similar total density (~10,000 inds m−2

) as we found in 2013 (~12,000 inds m−2). Both studies 

sampled from late spring to the end of July or beginning of August. In comparison, Wood and others 

(2013), using the same collecting and processing methods, found densities (~20,000 inds m−2

) almost 

identical to our 2014 study. Wood and others (2013) and our 2014 study sampled from late spring 

through, at least, the beginning of October. 

Biological Solute Efflux—Soluble Reactive Phosphorus (2011–2015)

A total of 470 measurements were completed from autumn 2011 through autumn 2015 to 

evaluate the release of soluble reactive phosphorus (SRP) from benthic invertebrates. From autumn 

2011 through 2013, invertebrates were collected and assessed for the release of nutrients in late spring 

and early autumn. Invertebrates were assessed three times in 2015: May 7, July 31, and October 8, 2015. 

In general, invertebrates were targeted that were (1) most widely distributed, (2) most abundant, and (3) 

represented the larger of the taxa available, with the assumption that larger and more abundant 

organisms would contribute the most to lake nutrient loading. 

The release of SRP varied among taxa (table 18). In general, the highest rates of per-taxon efflux 

were among the Hirudinea. Release of SRP by the hirudinean Mooreobdella far exceeded all other taxa 

on a per-individual basis, likely because it is approximately 20–50 times larger than most other taxa 

typically collected in the benthic grabs. Fourth instar chironomid Cryptochironomus released 

substantially less SRP than Mooreobdella and only released slightly higher amounts than the remaining 

hirudinean species assessed. 

The contribution of SRP to the water column by any one species is a function of its rate of efflux 

and its density. The species-specific rate of efflux is a function of its life-history stage and potentially 

also a function of water temperature. An organism’s density is a function of its life-history 

characteristics and other biotic interactions (for example, competition and predation). The hirudinean 

Helobdella nr. bowermani and the chironomid Procladius exceed other taxa by approximately a factor 
26

of 2 to 4 in their respective population’s total contributions of SRP to the water column. The most 

frequently encountered large chironomid Chironomus plumosus released SRP amounts that differed as a 

function of its life-history stage. C. plumosus released several times more SRP as a fourth instar 

compared to a third instar larva. 

The average density of the taxa tested, based on the 2013 benthic data, was approximately 6,700 

inds m−2 (table 9). This represents approximately one-half the average total density of invertebrates 

collected from a square meter of lake bottom, which was around 12,000 inds m−2 in 2013. The sum of 

the product of individual SRP efflux estimates per taxon and average density per taxon yields 3.3 mg-P 

m−2 d−1. We assume this estimate of invertebrate efflux can be at least doubled, to around 6.5 mg-P m

−2

d−1, because it represents the efflux of only one-half the benthic invertebrates per square meter found 

during the 2013 spatial sampling. Also, our samples were sieved using the typical 500-μm mesh so that 

they would be as comparable as possible to other benthic studies relative to estimating density. We 

know that many benthic organisms pass through a 500-μm mesh sieve; therefore, our density estimates 

are likely biased low, and consequently, 6.5 mg-SRP m

−2 d−1 represents a conservative estimate of 

benthic invertebrate SRP efflux to the lake water column. 

A number of studies have estimated phosphorus release by aquatic invertebrates. Unfortunately, 

there is little similarity in the methods used or how release rates are reported. Regardless, once estimates 

are converted to common units, the release of SRP by UKL benthic invertebrates was not dissimilar to 

estimates by other studies. Fukuhara and Sakamoto (1987) estimated SRP release by fourth instar 

Chironomus plumosus to be 1.183 μg-P ind−1 d−1, similar to our 0.843 μg-P ind−1 d−1. Gardner and 

others (1981) found very similar rates (0.729 μg-P ind−1 d−1) when evaluating three species of 

Chironomus including C. plumosus. Substantially higher rates (1.84 μg-P ind−1 d−1 and 4.86 μg-P ind−1

d−1) were estimated by Devine and Vanni (2002); however, they assumed their estimates were higher 

than other studies because of the presence of food and possibly because their experiments were 

completed at higher temperatures than comparative studies. 

The effect of temperature on nutrient release by invertebrates has been mixed. Gallepp (1979) 

found that SRP release increased by a factor of 10 as temperature varied from 10 to 20 °C, although this 

magnitude of effect has infrequently been detected by subsequent research. Our experiments were 

performed at ambient lake temperatures, which were 14–15 °C in the spring and autumn and 20 °C at 

the end of July. Our 2015 estimates for Chironomus plumosus indicated that there potentially was a 

temperature effect. SRP release was 1.089 ± 1.793 37 μg-P ind−1 d−1 (n=37) on May 7, 2015, at 14 °C; 

1.305 ± 1.159 μg-P ind−1 d−1 (n=25) on July 31, 2015, at 20 °C; and 0.533 ± 0.676 μg-P ind−1 d−1 (n=22) 

on October 8, 2015, at 14 °C. However, heterogeneous variances among experiments preclude a 

definitive conclusion. Although all individuals tested were fourth instar larvae, other seasonal 

differences in the populations and water-quality conditions of the lake likely influence release rates as 

well. 

Estimates of SRP release by oligochaetes have also been variable. Postolache and others (2006) 

estimated rates for tubificids that ranged from 0.094 to 0.144 μg-P ind−1 d−1, and Ji and others (2015) 

estimated values ranging from 0.002 μg-P ind−1 d−1 for Capitella to 0.415 μg-P ind−1 d−1 for 

Limnodrilus hoffmeisteri. The above values are similar to the values we estimated for resident UKL 

oligochaetes (table 18). To our knowledge, our estimates of SRP release by hirudineans are unique. 

They span a substantial range in magnitude, which is clearly a function of size, from the very large 

Mooreobdella to the much smaller Helobdella spp.

Although all lake-dwelling organisms contribute to nutrient cycling (for example, bacteria, 

phyto- and zooplankton, fish, birds), benthic invertebrates represent an obvious linkage between lake 

sediments and the water column (Sereda and Hudson, 2011). Aside from their importance in the trophic 
27

transfer of energy and solutes from lower trophic levels to higher trophic levels, elevated densities of 

benthic invertebrates have the potential to increase the benthic flux of solutes (Kuwabara and others, 

2009). Benthic invertebrate assemblages may enhance dissolved nutrient (or toxicant) diffusion across 

the sediment-water interface by (1) modifying diffusion-layer thicknesses and permeability through 

bioturbation, (2) enhancing advective flow across the interface through bioirrigation, and (3) excreting 

or expelling dissolved or particulate solutes directly into the overlying water column (Boudreau and 

Jorgensen, 2001). We only evaluated benthic invertebrate SRP efflux via excretion for approximately 15 

different taxa. Once species-specific efflux was scaled by species density, the results suggest that 

benthic invertebrates potentially contribute 1.5 times the amount of SRP (6.5 mg-P m−2 d−1) to the water 

column of Upper Klamath Lake as diffusive flux alone (4.3 mg-P m

−2 d−1).

2015—Physical (Advective) Solute Transport and Particle Dynamics

Physical (Advective) Solute Transport

Previous estimates of heat transfer near the sediment-water interface suggested the potential 

importance of physical (hydrologic) transport of solutes between the lakebed and overlying water 

column (Kuwabara and others, 2009). To examine this process further, piezometers were deployed at all 

18 open-lake and littoral-zone sites studied in 2013, and at an additional seep/spring site (SPR, fig. 1) in 

proximity of visible groundwater inputs at the lakeshore. Piezometers were fitted with pressure 

transducers to measure head in the lake and 1.5 m below the lakebed. In addition, a set of 3 temperature 

loggers was deployed at 6 of the 19 piezometer sites (see “Methods” section). Because of an unknown 

combination of piezometer-pipe failure at threaded connections and (or) vandalism, only 4 temperaturelogger sets and 15 piezometers were retrievable from which to collect and analyze data (table 19). The 

pressure transducer installations, however, did not perform as designed in that the vertical positions of 

the instruments apparently shifted slightly in response to external influences such as wave action. These 

shifts were of a magnitude similar to the head differences between the lake and underlying sediments. 

Because of this, the magnitude and direction of vertical head gradients were deemed unreliable. This 

issue, however, did not significantly affect temperature data. Consequently, advective groundwater flux 

estimates presented herein are based solely on temperature data. 

Heat is a well-established tracer for accurately estimating water fluxes through sediments, based 

on measured spatial and temporal sediment-temperature patterns (Bredehoeft and Papadopulos, 1965; 

Stallman, 1965; Lapham, 1989; Constantz and others, 2003; Stonestrom and Constantz, 2003; 

Anderson, 2005; Constantz, 2008; Essaid and others, 2008; Rahimi and others, 2015). Modeling for this 

study used the program 1DTempPro V2 (Koch and others, 2015) to generate advective flux estimates 

based on temperature-logger data from four UKL sites, without use of tranducer data (that is, without 

imposed hydraulic conductivity or heads in running the model). Because model inputs for lakebed 

sediment characteristics were a potential source of uncertainty for the output, in this case discharge, the 

model was run for all four sites (SPR, LN01, ON01, and LS01) to examine the effect of changing the 

modeled porosity over a range from 0.7 to 0.9, as well as the effects of thermal conductivity and heat 

capacity (Stonestrom and Constantz, 2003). The inclusion of groundwater discharge consistently 

improved the model fit to the temperature-logger data, relative to a “no discharge” assumption (table

19). Estimated groundwater discharge into the lake continuously decreased from spring to autumn and 

was negative during the third sampling period in September. Given decreasing lake elevations over the 

period of temperature-logger deployments, the apparent reduction in groundwater inflow and reversal of 

direction, although noted in previous heat-flux estimates (Kuwabara and others, 2009), would not be 

expected if lake water-column hydraulic head solely regulated groundwater input to the lake. Although 
28

not used in this modeling effort, a low hydraulic conductivity (0.0006 m d−1) was determined for site 

LN01 using a slug-test method (Bouwer and Rice, 1976), suggesting lower, perhaps negligible fluxes 

relative to those determined by heat-flow modeling. In addition, Gannett and others (2007) reported a 

mean groundwater discharge into Upper Klamath Lake of 350 cubic feet per second (ft3

 s

−1) (8.6 × 108

L d−1) using a water mass-balance model. Assuming a lake projected area of 249 km2, that water flux is 

equivalent to a mean discharge from the lakebed of 0.003 m d−1. By comparison, the mean discharge 

from the heat-flow modeling was 0.005 m d−1 (n=33, table 19). Given the spatial and temporal 

variability of the heat-flow derived discharges, caution should be taken if these results (table 19) are 

extrapolated into lakewide fluxes. Over the porosity range from 0.7 to 0.9 (table 19), estimates (both 

positive and negative) of the temperature-derived advective fluxes for SRP are of comparable 

magnitude in absolute value to diffusive-flux and macroinvertebrate efflux (all positive but spanning the 

same orders of magnitude). It is therefore recommended that solute advective flux as a potentially 

important transport process be examined for possible integration into existing and future water-quality 

models (for example, TMDL models). 

Based on challenges faced in these initial groundwater-advection investigations, it is 

recommended in future studies that temperature-logger sets be deployed at every piezometerdeployment site so comparative estimates can be made. Greater numbers of temperature loggers per 

deployment set at each site may also be advisable. Three temperature loggers were used here for each 

set, whereas a previous deployment at one mid-lake site used five (Kuwabara and others, 2009). 

Because both the piezometers and temperature logger sets are highly visible (as required for boating 

safety), it is also suggested that some security measures be adopted, such as visual monitoring from the 

shore or by other research collaborators sampling the lake, or remote transmission of data with 

appropriate alarms. It may also be useful to directly determine lakebed thermal properties (for example, 

thermal conductivity and volumetric heat capacity) that were empirically derived as input for heat-flow 

modeling. To confirm transducer depths within the piezometer and lake (and most importantly their 

relative positions), precise water depths in both the piezometer and the stilling well should be measured 

to a common datum at each site at initial deployment, before and after instrument removal and 

replacement during site visits, and possibly at prescribed periods during deployment. Transducer 

removal and replacement during piezometer deployments should be kept to a minimum. 

Organic Aggregation and Settling in Upper Klamath Lake

As indicated above, the overall study clearly focused on biogeochemical processes near the 

sediment-water interface that may affect solute transport and phytoplankton dynamics in the lake.

However, the authors also recognized that other physical processes might also be of management and 

modeling relevance. For example, as suspended particles with biogenic origin aggregate and settle to a 

lakebed, there is a potential for heterotrophic degradation of this settled organic matter to alter redox 

conditions in the lake water column. An initial examination of suspended-particle characteristics was 

therefore conducted in 2015 using an In-situ Settling Velocity Instrument (INSSEV-LF) to better 

understand changing particle characteristics and settling velocities during the AFA-bloom period, at an 

open-lake and littoral-zone site near the surface and lakebed. This work involved different protocols, 

equipment, background literature, and terminology than the biogeochemistry sections presented herein. 

So to aid the reader in referencing and following the discussion of this complementary study, a 

somewhat independent discussion of this work is provided below. For this work, the term “floc” refers 

to suspended particles that may aggregate or disaggregate to change in size, composition, and settling 

velocity. 
29

Overview of Particle Aggregation and Settling

A high proportion of biological matter, which can vary both spatially in the lake and seasonally, 

dominates suspended particulate matter in Upper Klamath Lake (UKL). Although the amount of organic 

matter in suspended particulates was not directly assessed during this project, Wood and Gartner (2010) 

found the average organic fraction of the suspended matter to be as high as 77 percent during algal 

bloom conditions (such as July 2015) in UKL. It is possible that the organic matter content in UKL is 

lower than this value during pre- and post-cyanobacteria-bloom conditions (that is, early May 2015 and 

October 2015, respectively). Any remaining suspended particles resident within UKL would include 

fine, cohesive sediment that includes silts and clays and possibly even coarser fragments.

Particulate matter which is predominantly organic, will tend to behave as agglomerates, as 

opposed to inert solitary independent particles. This is very similar to the process of flocculation, 

whereby interparticle forces attract these cohesive particles to one another (for example, Mietta and 

others, 2009) and form flocs (for example, Manning and others, 2011; Mehta, 2014) composed of the 

particles and thus contain interparticle voids. As flocs grow in size, they trap more pore water within the 

floc compositional matrix, which means floc effective density ρe (that is, bulk density less the water 

density) generally decreases (Tambo and Watanabe, 1979). Although large flocs have lower effective 

densities than their constituent particles, they also exhibit significantly quicker settling velocities (Ws), 

a result of a Stokes’ law relationship between floc sizes (D) and settling velocity (Ws; Dyer and 

Manning, 1999). This means that the flocs, which comprise a floc population, may exhibit individual 

floc sizes, settling velocities, and densities that range over three to four orders of magnitude (for 

example, Manning and others, 2013). 

The term “floc” is commonly reserved for electrochemically agglomerated clay mineral 

particles. When they contain organic detritus greater than 5–10 percent by weight, floc properties are 

affected: there can be biopolymeric adhesion in addition to electrochemical cohesion, density can be

reduced owing to the lower weight of organic matter, and porosity can increase. When the organic 

content increases to 30–40 percent, the agglomerates are commonly called “organic aggregates” (for 

example, Gowland and others, 2007; Mehta and others, 2009), and this terminology will be used (where 

relevant) in this report; alternatively, they are also known as “biochemical aggregates.” However, the 

parameterized terms “macrofloc” (D >160 μm) and “microfloc” (D <160 μm) will still be used to 

describe the relative properties of each organic aggregate fraction. The density and yield strength of 

organic aggregates (up to about 80–90 percent organic content) have been found to vary monotonically 

with organic content (see Gowland and others, 2007). Their study assessed suspended particulate matter 

with organic matter ranging from 0 to 82 percent. Furthermore, when Mehta and others (2009) 

compared the dry density and organic content of suspended sediments in Lake Apopka (central Florida), 

where organic contents of the suspended matter reached 96 percent, with a wide range of mineral 

sediments, the highly organic sediments closely followed the same trend as predominantly mineral 

sediments, but the organic sediments were much weaker in terms of yield stress (the value of stress at 

which a material ceases to behave elastically). 

UKL is a freshwater system with highly organic suspended matter. Therefore, it can logically be 

speculated that the bonding of any organic aggregates (predominantly cyanobacterial colonies in UKL) 

are primarily biologically based (for example, Kranck and Milligan, 1992; Wolanski, 2007), as opposed 

to classical physical cohesion (that is, electrostatic bonding observed in more brackish waters). This biocohesion was first observed in flocs by Eisma (1986) in the early 1980s and more recently in numerous 

freshwater aquatic environments across the United States, for example, Lake Apopka in central Florida 

(Mehta and others, 2009) and the Sacramento–San Joaquin River Delta in northern California (Manning 

and Schoellhamer, 2013). To reiterate, when organic particles dominate the suspended particulates, the 
30

role of electrochemical cohesion is limited and flocculation is thus not an appropriate term; hence, we 

use the term “biochemical/organic aggregates”, or “organic flocs,” for any such agglomerates formed. 

Mehta and others (2015) report that data from both Lake Okeechobee and Lake Apopka, Florida, 

surprisingly suggest that organic-rich aggregates follow fractal laws developed for mineral flocs (see 

Kranenburg, 1994). Mehta and others (2015) concluded that the variation of yield stress with the 

organic aggregate volume fraction appears to conform to fractal characterization commonly invoked for 

mineral sediment flocs, consistent with a mean fractal dimension value of 2.55. 

Bio-cohesion within UKL is most probably facilitated through the presence of extracellular 

polymeric substances (EPS). EPS are high-molecular-weight compounds that are normally secreted by 

algal microorganisms (Tolhurst and others, 2002) and are mostly composed of polysaccharides (for 

example, mucopolysaccharides, exopolysaccharides) and proteins. In muddy aquatic environments such 

as estuaries, these sticky EPS biofilms are regarded as highly effective stabilizers of muddy sediments 

(de Brouwer and others, 2005; Gerbersdorf and others, 2009; Grabowski and others, 2011; Malarkey 

and others, 2015; Parsons and others, 2016). EPS are the principle constituents that determine the 

physiochemical properties of a biofilm and provide the major portion of a biofilm’s total organic matter. 

Therefore, in a lake that is dominated by high organic content (such as UKL), it is highly probable that 

there are quite high levels of EPS present (when compared to typical estuarine locations), and this 

would enhance any biochemical aggregation of the suspended matter. 

For water bodies such as small and deep lakes in which wind-driven mixing does not resuspend 

the particulate matter (whether it be sediment that is either predominantly mineral or organic based), 

measurements of settling velocity, size, and density in a test column may be artifacts of the 

measurement technique. In such a body of water, gravitational settling has a lesser meaning if the 

aggregates are not resuspended on a regular basis. However, for environments such as large, shallow 

lakes (for example, UKL), a mean circulation (that is, currents) can develop in these environments (for 

example, owing to wind-water surface interaction), and organic aggregates are much more easily 

resuspended than heavier mineral flocs. These resultant hydrodynamics (although it be modest by tidal 

aquatic environment standards) could easily set up interparticle collisions such as those through 

differential settling effects (Lick and others, 1993; Lick and Lick, 1988). Although cyanobacterial

colonies can grow organically, these hydrodynamic mechanisms can potentially stimulate and further 

enhance the resultant organic aggregate/colony growth. 

The size and mass of suspended organic aggregates determines their settling velocity, which in 

turn affects how long suspended particles remain in suspension and the clarity of the water column. 

These organic aggregate parameters also govern aggregate mass transport (Baugh and Manning, 2007; 

Manning and Dyer, 2007). As the bloom converges on maximum phytoplankton densities, senescing 

cells can readily collide and potentially aggregate into flocculant particles (that is, flocs) with elevated 

settling velocity.

As organic aggregates settle to a lakebed, there is a potential for any heterotrophic degradation 

of this settled organic matter to alter redox conditions in the lake water column, although this was not 

assessed in this study. Heterotrophic degradation can release bioavailable nutrients into the water 

column, a process that is more aligned with this study. It is therefore necessary to determine how much 

biochemical aggregation of the organic suspended matter/particles occur in UKL before, during, and 

after the annual algal bloom. The size, settling velocity, and effective density of individual organic 

aggregates can be used to develop numerical models of nutrient transport in dissolved and particulate 

phases. This research is part of a larger Bureau of Reclamation (Reclamation) project for the study of 

nutrient transport in Upper Klamath Lake, Oregon. 
31

Three surveys were conducted in UKL is 2015 as a collaboration between the USGS, 

Reclamation, and H.R. Wallingford (A.J. Manning, written commun., 2016) to measure aggregate 

diameter, settling velocity, and density with a specialist video camera; we summarize the organic 

aggregate observations results in this section. 

Particle Aggregation and Settling Results

Previous studies by the project team have shown that a cyanobacterial bloom forms in UKL 

during the warm summer months. Therefore, the initial suspended particulate matter survey was 

conducted on May 6, 2015—a time period deemed suitably representative of pre-bloom conditions. The 

majority of the algae present in UKL at this time were diatoms and brown algae. Environmental 

variables and organic aggregate population properties are summarized in table 20. The ML site, with a 

local water depth of 4.5 m, was the most turbid region in May 2015, with a SPMC of 140 mg L−1 at the 

dp_90 sampling positions. Although technically the term “turbidity” is an optical property, its use 

survives in scientific/engineering/sediment transport literature as a relative comparative/descriptive 

term. The term turbidity maximum zone; any gravimetric type data are referred to as suspended 

particulate matter concentration (SPMC) in this section of the report. An abundance of organic 

aggregates (364) were observed in the sample 0515_S03 population at the ML site in the near-bed 

region. The scatterplot (fig. 2b) illustrates spherical-equivalent dry-mass-weighted organic-aggregate 

size plotted against settling velocity for the sample. The diagonal lines on the scatterplot represent 

contours of constant organic-floc effective (that is, immersed) density (units = kg m−3; as a reference 

guide, a quartz mineral grain would have a ρe of about 1,600 kg m−3

). These ambient conditions 

produced large, fast-settling organic aggregates, as demonstrated by the May 2015 survey peaks in both 

the mean organic aggregate size (122 μm) and settling velocity (3.28 mg s

−1). The macrofloc-sized 

organic aggregates were 85 percent porous and exhibited an average effective density (ρe_macro) of 188 

kg m−3

, whereas the microfloc-sized organic aggregates exhibited an average effective density 

(ρe_micro) of 628 kg m−3

. However, these macrofloc-sized organic aggregates (73 in number) only 

represented under one-third of both the ambient SPMC and total mass settling fluxes (MSF, 464 mg m−2

s

−1), and the organic macroflocs and smaller microfloc-sized organic aggregates each collectively 

demonstrated similar settling velocities (~3.3 mm s

−1).

At the shallower (water depth = 2.2 m) LS01 site of UKL, the near-bed turbidity was 

significantly lower (SPMC = 11 mg L−1) than ML. Here, the low ambient particle abundance was seen 

to reduce the potential for constructive floc interparticle collisions, and this was reflected in the sample 

0515_S06 flocs (fig. 2d) by both a much smaller Dmean of 79 μm and a slower Wsmean of 0.9 mm s−1

(table 20). This was a zone dominated by microfloc-sized organic aggregates, where 42 of the 44 flocs 

were organic microflocs, and this also translated through to the smaller microfloc-sized organic 

aggregates comprising 86 percent of the aggregate mass and settling flux.

Higher in the LS01water column, the 0515_S04 near-surface (dp_10) floc sample (fig. 2c) was 

composed solely of microfloc-sized organic aggregates, and the average Ws was 0.57 mm s

−1, half the 

mean fall rate of the organic flocs in closer proximity to the bed. No organic macrofloc-sized organic 

aggregates were present.

The mass settling fluxes varied over a number of orders of magnitude between the sites and 

through the water column during the May 2015 sampling period. The MSF at the LS01 surface was 3 

mg m−2

 s

−1 (SPMC = 4.5 mg L−1); this compared to a fourfold increase at the LS01 bed (MSF = 11.5 mg 

m−2

 s

−1). Furthermore, the MSF at the ML near bed was 40 and 150 times higher than the southern 

region littoral habitat near-bed and surface organic flocs, respectively. 
32

b 

c 

h 

g 

f 

e i 

j 

k 

d l 

a 

Figure 2 caption is on page 33. 
33

Figure 2. Upper Klamath Lake organic aggregate (floc) size versus settling velocity population scatterplots. May 

6, 2015: (a) 0515_S01 (ML dp_10), (b) 0515_S03 (ML dp_90), (c) 0515_S04 (LS01 dp_10), (d) 0515_S06 (LS01 

dp_90). July 30, 2015: (e) 0715_S01 (ML dp_10), (f) 0715_S03 (ML dp_90), (g) 0715_S04 (LS01 dp_10), (h) 

0715_S06 (LS01 dp_90). October 6, 2015: (i) 1015_S01 (ML dp_10), (j) 1015_S03 (ML dp_90), (k) 1015_S04 

(LS01 dp_10), and (l) 1015_S06 (LS01 dp_90). The diagonal lines on the Ws versus D scatterplots represent 

contours of constant Stokes equivalent effective density: pink = 1,600 kg m−3, green = 160 kg m−3, and red = 16 kg 

m−3. mm/s, millimeters per second; microns, micrometers.

The summer (July 30, 2015) sampling saw the cyanobacterial bloom well established in UKL 

(fig. 3). The lake was also ~0.6 m shallower than in May 2015. The key organic aggregate parameters 

and environmental conditions for the July 30, 2015, survey are summarized in table 21. The ML site that 

was the most turbid during pre-bloom (May 2015) saw nearly an order of magnitude reduction in near 

bed SPMC, to 21 mg L−1 (sample 0715_S03, fig. 2f). The Dmean had risen by 56 μm from May 

conditions to 178 μm. Also, the July ML flocs were only one-third the average effective density of the 

corresponding floc population observed near bed at ML in the pre-bloom May sampling (that is,

ρe_mean of 170 kg m−3

 compared to 540 kg m−3). It can be speculated that although the SPMC was 

lower at ML in July, the organic flocculation “collision efficiency” (Edzwald and O’Melia, 1975) could 

have risen owing to a higher level of EPS present in the lake, thus ensuring particles that came into 

contact would have a higher probability of sticking together and agglomerating. Although direct 

measurements of EPS were beyond the scope of this 2015 UKL study, one can see that in July 2015 

(table 21), the macrofloc-sized organic aggregates (cyanobacterial colonies) outnumbered the smaller 

microfloc-sized fraction; they represented more than two-thirds of the organic aggregate mass for all 

July 2015 UKL organic aggregates (at both sites and through the water column) and over three-quarters 

of the MSF. 

The near bed (dp_90) region at LS01 saw the peak summer SPMC of 316 mg L−1; this was 

nearly a thirtyfold rise in SPMC from May 2015 in the southern littoral habitat and over twice the 

turbidity encountered during the entire May 2015 organic flocculation survey. Of the 330 sample 

0715_S06 organic aggregates (fig. 2h), 275 were of organic macrofloc-size classification. Dmean peaked 

at 305 μm, and the population average settling velocity was approaching 4 mm s

−1. In fact, if we 

examine the D versus Ws scatterplot for sample 0715_S06, it shows that the highest order organic 

macrofloc-sized organic aggregates/cyanobacterial colonies had grown to almost 1 mm in diameter. The 

majority of the organic flocs were sandwiched between the lower density 160 and 16 kg m−3

 density 

contours, edging close to the density of water (but remaining on the positive side). The delicate and 

fragile structure (most probably containing a large proportion of the resident low density organic-based 

algae) of these macrofloc-sized organic aggregates/large cyanobacterial colonies is indicated by a 

ρe_macro of 75 kg m−3 and an average porosity of 94 percent. 

Some examples of organic aggregate images (from the LS01 site) during the summer 

cyanobacterial bloom are shown in figure 4c. Many of the macrofloc-sized organic aggregates/large 

cyanobacterial colonies present were approaching 1 mm in size, with an irregular clustered but fibrous 

appearance. It is apparent that although these are fast-settling macrofloc-sized organic aggregates/large 

cyanobacterial colonies, they are of low effective density and are highly porous; in fact, it is possible to 

see through many of these organic aggregates. These large summer macrofloc-sized organic 

aggregates/large cyanobacterial colonies are in contrast to the significantly smaller pre-bloom organic 

flocs observed during May 2015 at the ML site (fig. 4a). The May 2015 pre-bloom organic aggregates 

were more rounded in shape, such as flocs typically found in a low turbulent estuarial environment, and 

they appear to be less porous in composition (that is, it is not possible to see through them). 
34

 

a

b

c

e

d

Figure 3. Photographs showing (a) a complete LabSFLOC-2 floc cam setup in a laboratory, with the settling 

column and camera on the left and computer with operating software and recording hard drive on the right. (b) Top 

view of U.S. Geological Survey (USGS) version of the LabSFLOC-2 floc cam setup in stainless steel housing in a 

Pelican waterproof case, with the camera and lens to the left and the settling column to the right. (c) July 2015, site 

LS01—collecting the near-surface floc sample directly from the water column using the modified pipette. Note 

green water color. (d) USGS Van Dorn sampler from same sampling. (e) Professor Manning transferring flocs. 

Photographs by Andrew Manning of Plymouth University, School of Marine Science and Engineering.
35

Figure 4. Floc images from the LabSFLOC-2 floc cam collected during (a) the pre-bloom conditions, when Upper 

Klamath Lake is dominated by brown algae including diatoms (image shows other cyanobacterial colonies). Leaflike structures appear in the July sample (b), assumed to be individual Aphanizomenon flos–aquae (AFA) filaments. 

Organic aggregates during bloom conditions (c) include cyanobacterial colonies. Note: different scales for each 

image.
36

Agglomeration dynamics may be linked (either directly or indirectly) to the depositional flux 

during the cyanobacterial bloom in UKL. For example, 94 percent of the organic floc mass for sample 

0715_S06 were of macrofloc size, and given the elevated Ws for macrofloc of 4.5 mm s−1

, macroflocsized organic aggregates/large cyanobacterial colonies comprised 98.8 percent of the total MSF of 1,580 

mg m−2 s

−1. When comparing the bloom with pre-bloom settling fluxes, this maximum bloom settling 

flux (from LS01) was nearly four times greater than the peak MSF measured during pre-bloom (May 

2015) at ML. 

Even though the organic aggregate/cyanobacterial colonies in the near-bed region of the lake 

were seen to settle fairly swiftly, the presence of particles/organic aggregates throughout the water 

column, in particular near the water surface, is a clear indication that they are there in part owing to 

resuspension. Very little agitation is required to achieve this, especially for low density organic 

aggregate/cyanobacterial colonies. If this wasn’t the case, then the lake should be clear of particles. 

Thus, wind-driven mixing and the resulting lake currents (even a low level contribution) is essential (see 

earlier comments in the “Overview” section).

In addition, many of the LabSFLOC-2 floc cam images observed during the July 2015 bloom 

depicted the presence of long, thin green, leafy strands (fig. 4b); these are thought to be individual AFA

filaments. It is not known whether any of these filaments combine with the organic 

macroflocs/cyanobacterial colonies structures; however, Little (2000) found that when numerous 

phytoplankton threads are sticky with EPS, they can efficiently trap fine grains, thus providing a 

potential transport mechanism.

In terms of the data acquisition techniques, the LabSFLOC-2 floc cam method is primarily 

aimed at measuring settling properties of material that can be defined as sediment (whether it be organic 

or mineral in origin), that is, particles that are generally exchanged at the boundary between the bed and 

the water column. It is known that the movement of the AFA filaments is hugely complex; at times the 

cells are able to produce ballast to sink and gas vacuoles to float. For example, Nakamura and others 

(1993) specifically collapsed the gas vacuoles of AFA filaments to get settling and otherwise got 

flotation. However, with the floc cam measurements of the natural organic filaments and cyanbacterial 

colonies/organic aggregates, only settling was observed, and this would most likely be due to gravity, 

and therefore Stokes’ law would be applicable. The organic macrofloc/large cyanobacterial colonies 

settled significantly quicker than the individual AFA filaments, but all were observed to be settling. 

Countering this, there would have to be observations of upward mobility. If it were present, the 

magnitude of the upward velocity relative to the settling velocity would need to be resolved, but no 

upward movement was observed in any of the floc cam video recordings. 

The large macrofloc-sized organic aggregates/cyanobacterial colonies were of extremely low 

effective density, for example, figure 2h shows that some had ρe of about 8–30 kg m−3. However, they 

still exhibited a density slightly greater than water. If the organic aggregates/cyanobacterial colonies and 

filaments had a density less than water, all the material would float and perhaps cover the entire water 

surface of the lake. If they were neutrally buoyant, they would not fall in a test column, and their settling 

speeds could not be measured. The collected water samples were run in the floc cam test immediately, 

and the particles were photographed as falling; therefore, we have to assume that such settling particles 

exist. For example, Mehta and others (2009) observed in Lake Apopka that the organic aggregates seem 

to contain biogenic silica (and not clays), and this can impart an additional downward velocity. To 

reiterate, as the organic aggregates/cyanobacterial colonies and AFA filaments were seen to fall in the 

floc cam test column, it was most likely due to gravity, and the use of Stokes’ law assessment is deemed 

valid. 
37

The survey conducted in the autumn on October 6, 2015, was regarded as being representative of 

post-bloom conditions. However, there were still distinct traces of both cyanobacterial colonies (organic 

aggregates) and AFA filaments present in UKL, especially in the southern littoral habitat (LS01) region 

of Howard’s Bay. The dry 2015 summer meant that the lake’s water depth had decreased by a further 

0.8-0.9 m in the autumn; this resulted in water depths of 3.1 and 0.7 m at ML and LS01, respectively.

The key floc parameters and environmental conditions for the October 6, 2015, survey are 

summarized in table 22, with the detailed D versus Ws scatterplots illustrated in figures 2i-l. ML was 

only slightly more turbid than the summer 2015 conditions, with a dp_90 SPMC of 62 mg L

−1, and the 

sample 1015_S03 organic aggregates at this ML site in the near-bed region (fig. 2j) demonstrated a 

mean settling velocity of 3.45 mm s

−1, double the Wsmean observed at the same location in July 2015 

during the bloom. 

At the very shallow water column present at LS01, the water sample SPMC of 828 mg L−1

indicated that the dp_90 floc measurements (sample 1015_S06, fig. 2l) were taken in the upper region of 

a benthic nepheloid layer (BNL), also known as a concentrated benthic suspension (CBS) layer, formed

above the lake’s bed. At an SPMC approaching 1 g L−1, the BNL would still tend to exhibit Newtonian 

rheological flow properties, interacting with the ambient flow and possessing a similar viscosity to 

water. The sample 1015_S06 comprised a total of 1,095 individual organic aggregates, of which 718 (or 

65.5 percent) were of macrofloc size. Although some organic aggregates were 1 mm in size, the Dmean

(238 μm) was 67 μm smaller than measured during the July bloom. 

The WsMacro (4.74 mm s−1) was similar to the macrofloc-sized organic aggregate settling 

observed in the July 2015 bloom, but the Wsmicro (2.31 mm s−1) was 1.45 mm s−1 (or 168 percent) 

quicker. However, the October macrofloc-sized organic aggregates (predominantly cyanobacterial 

colonies) comprised 84 percent of the aggregate mass, a 10 percent decrease from July, and this resulted 

in the 1015_S06 Wsmean (3.91 mm s−1) being very similar to the July conditions. In terms of organic 

aggregate structure, the post-bloom organic aggregates within the BNL at site LS01 had a mean 

effective density and porosity of 220 kg m−3

 and 82.5 percent, respectively. This made the October 2015 

BNL organic aggregates generally more dense (140 kg m−3

) and less porous (−12 percent) than the 

preceding bloom organic aggregates at LS01 in the dp_90 region. 

The high SPMC experienced by sample 1015_S06 in the BNL produced a total MSF of 4,139 

mg m−2

 s

−1 (92.1 percent credited to the organic macrofloc-sized aggregate fraction); that was nearly 

three times the corresponding settling flux observed during July and 360 times greater than the prebloom conditions from May 2015 (98.8 percent and 14 percent credited to the macrofloc-sized fraction 

at LS01 from dp_90 for those respective months). 

Based on research conducted in other lakes that experience cyanobacterial blooms, such as Lake 

Apopka in central Florida (Mehta and others, 2009), these studies are in agreement with the presence of 

high SPMCs existing in close proximity to Upper Klamath Lake’s bed (that is, SPMCs increase owing 

to gravity settling), and that would mean that part of the BNL in the lake may demonstrate 

characteristics of a non-Newtonian fluid mud. 

A BNL, or the more turbid fluid mud (FM), is an entirely ephemeral state of mud. It is known to 

occur in large and shallow lakes where the bottom often contains clayey sediment (for example, Lake 

Okeechobee, Fla.). Where the suspended matter is mainly organic (for example, Lake Apopka, Fla.), the 

term “fluid mud” is still often used within scientific/engineering/sediment transport community 

literature even though there is little mud present. The main attributes of a BNL and the more turbid FM 

are that (1) it is a dense slurry defined by hindered settling, that is, settling of the slurry as a whole as 

opposed to individual flocs/aggregates. The rate of settling of the slurry is dependent on the rate at 

which water escapes the slurry upward. The settling of aggregates within FM becomes hindered, and 
38

their terminal velocities progressively slow with rising turbidity (Odd, 1988). When organic-rich 

sediments form such slurries at lake bottoms, they can be 10–30 cm in thickness. 

The BNL or FM slurry (2) has viscoelastic properties defined by yield stress, which can be 

measured in a viscometer. Rheologically, FMs behave as a pseudoplastic; that is, they have high 

viscosity at low shear rates, but their viscosity reduces at high shear rates. Further increases in 

concentration can transform an FM’s rheological properties into a Bingham plastic (Sills and Elder, 

1986), and the FM then becomes a soft bed material. An understanding of FM/BNL distributions and 

their dynamics is critical, principally because of the well-established connection between the dynamics 

of organic aggregates and the resultant transport pathways of any contaminants present within that 

aquatic system (Thorne and Nickless, 1981; Duquesne and others, 2006). This could be an important 

additional topic for future research in UKL.

Collectively, all organic aggregate populations observed within UKL demonstrated a fairly wide 

range in Ws for a constant D (fig. 2) and, similarly, a varying span in D for a constant Ws. This 

indicates the influence of varying organic aggregate effective density and its effect on mass and MSF. 

This also demonstrates the highly significant seasonal effects that the AFA bloom (in particular, the 

formation of the large macrofloc-sized organic aggregates/cyanobacterial colonies) has on the organic 

aggregate depositional fluxes in UKL and highlights the importance of seasonal monitoring of these 

conditions in order to correctly parameterize the wide range in depositional characteristics and organic 

aggregate properties measured throughout UKL. Thus, it is extremely important to use an instrument 

(such as the INSSEV-LF) that is capable of measuring the variability of settling velocity and its relation 

to organic aggregate density and size. 

Methods

Until 2013, benthic-flux studies had been limited spatially to a number of sites in the northern 

region of the lake and to 2–3 samplings per year (Kuwabara and others, 2012a). Virtually nothing was 

known about either the spatial or temporal variability in benthic solute sources for the entire lake. This 

limitation was addressed with a lakewide spatial study for 2013 (fig. 1) and a temporal study with 

weekly sampling for 2014. 

During field work in the spring and summer of 2013, we collected samples from three lake 

regions of approximately equal projected area, using letters “N,” “C,” and “S” in site labels to designate 

northern, central, and southern regions, respectively. Two lake habitats were sampled: three littoral sites 

with labels beginning with the letter “L” and three open-lake sites with labels beginning with the letter 

“O” within each region. Three littoral and three open-lake sites were randomly located in each of the 

three regions. In addition, three sites within the western trench feature of the lake were located in the 

central region and labeled beginning with the letters “TR.” After a reconnaissance trip on May 1, 2013, 

to test new sampling equipment and protocols, all 21 sites were sampled on 3 dates that were 

approximately 3 weeks apart and that encompassed the development of the first AFA bloom. Sampling 

of the 21 sites occurred on May 23, June 13, and July 3, 2013, as the annual AFA bloom developed. 

Analysis of the 2013 SRP benthic-flux measurements unexpectedly indicated no effect of 

location (lake region), habitat, or sampling period. Furthermore, the average lakewide flux values were 

consistent with earlier studies that had been confined to the northern region of the lake and adjacent 

wetlands (Kuwbara and others, 2009; Kuwabara and others, 2012a). The experimental design for the 

2014 temporal study therefore focused on site ON01 within Ball Bay because it (1) had representative 

SRP fluxes in 2013, (2) had representative benthic invertebrate assemblages, (3) had good accessibility 

during periods of high winds within Ball Bay, and (4) allowed the study to focus the allocation of 

available resources to optimize temporal resolution of benthic solute source measurements. Results of 
39

the 27-week study in 2014 were partitioned into three 9-week periods (referred to herein as pre-bloom, 

bloom, and post-bloom periods), based on water-column chlorophyll concentrations: (1) April 15 

through June 10, (2) June 17 through August 13, and (3) August 20 through October 16, 2014, 

respectively. Shaded rows in tables 10 through 15 distinguish results from the bloom period relative to 

the unshaded pre-bloom and post-bloom results. 

Nonmetallic pore-water profilers, designed for nutrient and trace-metal sampling (U.S. Patent 

8,051,727 B1 issued November 8, 2011; Kuwabara and others, 2009), were deployed in triplicate at 

each sampling site. In addition to water just above the sediment-water interface (approximately 1 cm), 

samplers were capable of collecting 0.2-μm-filtered interstitial water from five depths within the top 10 

centimeters of the sediment. That is, a total of six independent sampling circuits per profiler could be 

used to characterize dissolved-solute vertical gradients. In 2013, each profiler was set up to provide two 

samples from the above the sediment water interface, two from 1 cm below that interface, and two from 

2 cm below the interface. The design allowed for two measurements of solute benthic flux per profiler. 

By comparison, in the 2014 temporal study, six profilers were deployed where each profiler sampled 

from just above the sediment-water interface and 1, 2, 3.3, 5.5, and 10 cm below the interface. 

Each of the six sampling circuits collected filtered (0.2 μm) water into 50-mL acid-washed, allplastic syringes. After being lowered onto the sediment, the device was tripped mechanically to begin 

sample collection and then retrieved approximately 24 hours later. In contrast to passive sampling by 

dialysis, samples are slowly drawn through a series of filters into a 60-mL all-plastic, valved syringe. 

Dye experiments indicated that this extended sampling period with low flow rates avoided shortcircuiting of samples between depths and along device surfaces. After retrieval, the sample syringes 

were closed with a valve, double-bagged in argon, and refrigerated in darkness for sample processing, 

splitting, and later chemical analyses.

Flux calculations, based on Fick’s law, assumed that the benthic flux is diffusion controlled with 

solute-specific diffusion coefficients (Li and Gregory, 1974; Applin, 1987; Lead and others, 2000; 

Rebreanu and others, 2008). To provide a conservative estimate for DOC flux, a diffusion coefficient of 

2 × 10−6 cm

2

 s

−1 was used, the lower end of the range from 2 to 3 × 10−6 cm2

 s

−1 reported by Lead and 

others (2000) for DOC diffusivity. Benthic fluxes (Ji in mg m

−2 d−1) were calculated using the equation

Ji = Di,T(φ)(dC/dz)

where Di,T diffusion coefficient of solute i at temperature T in cm

2

 s

−1, 

φ sediment porosity in dimensionless units, and 

 dCi/dz concentration gradient for solute i in the vertical (or z) direction in mg cm−4, 

with calculated flux values converted to meter and day units. Pore-water and overlying water 

concentrations from each profiler were fit to an exponential gradient model as described by Klump and 

Martens (1981). Where nondetectable concentrations were observed, summary statistics for 

concentrations and gradients were calculated using half of the detection limit for that sample (in the case 

of nitrate, 0.005 mg-N L−1), after agreement with Kaplan-Meier analysis (Frome and Watkins, 2004). 

With the exception of silicate (as SiO2), fluxes and concentrations are presented in terms of elements, 

not molecules. For example, SRP flux is provided in terms of P, not phosphate. 

Diffusive fluxes can be influenced by bioturbation, bioirrigation, metabolic cycling, wind 

resuspension, and potential groundwater inflows (Reddy and DeLaune, 2008; Kuwabara and others, 

2009). Hence, the calculated benthic flux of dissolved solutes based on pore-water profiles is a 

conservative estimate that provides lower bounds indicating the potential importance of such internalsolute sources. Tabulated results include standard deviations about mean values for the number of 

replicates specified in the tables (tables 3, 7, 12, and 14). 
40

At each site, data loggers provided water-column profiles of ancillary parameters (pH, salinity, 

temperature, dissolved oxygen, and oxidation-reduction potential), monitored at 15-second periods at 

the time profilers were retrieved. Sampling methods have been previously described (Kuwabara and 

others, 2003; Kuwabara and others, 2009), but details are provided below. At each site, the following 

samples were collected, unless otherwise noted. 

Biological Parameters

Benthic Chlorophyll a

Upon completion of water-column sampling at a site, surficial sediment (that is, the top 

centimeter of bed material) was collected for benthic chlorophyll a analysis from a fresh Ekman grab 

and stored refrigerated in a plastic Petri dish within a sealed plastic bag. Each dish was subsampled in 

triplicate for benthic chlorophyll a. The surficial sediment for each replicate (0.785 cm2

) was collected 

on a glass-fiber filter and buffered with 1 mL of a supersaturated magnesium carbonate suspension (10 g 

L−1). Water was removed from the buffered samples by vacuum at less than 5 pounds per square inch 

(psi) to avoid cell lysis. Samples were then frozen on dry ice and later at −80 °C and held in darkness 

for preservation until analyzed spectrophotometrically (before 2007–2010) or by fluorometer (2013 

samples), which occurred within 3 months (Thompson and others, 1981; Franson, 1985). 

Benthic Invertebrate Sampling

Benthic invertebrates were collected using a tall Ekman grab that was modified to limit its 

penetration into the soft UKL substrate to approximately 10 cm. The volume of the grab was 

approximately 15.2 cm × 15.2 cm × 10 cm deep. All grabs were individually sieved in the field using a 

500-μm mesh sieve to remove excess fine sediment. The material retained on the sieve was preserved in 

10 percent buffered formalin. Each grab was transferred to 70 percent ethanol within a week of 

collecting. Each benthic invertebrate sample is the mean of three Ekman grabs in 2013 and the mean of 

five Ekman grabs in 2014. 

Benthic Invertebrate Sample Processing

Individual Ekman grabs were sorted in the laboratory using a dissecting microscope at 7×–10×

magnification. Samples were stained with Rose Bengal to facilitate sorting and (or) randomly 

subsampled when necessary (less than 1 percent of the grabs). When subsampling was necessary, the 

procedures in Moulton and others (2002) were followed. Representative Chironomidae and Oligochaeta 

were initially identified from slide-mounted specimens, using standard keys. Based on this reference 

collection, all invertebrates were identified to the lowest practicable taxonomic level using the 

dissecting microscope. When necessary, operational taxonomic units (OTUs) were developed. Andersen 

and others (2013), Kathman and Brinkhurst (1998), and Davies and Govedich (2001) were used to 

identify Chironomidae, Oligochaeta and Hirudinea, respectively. Over all samples, 33 percent of taxa 

were identified to species (or equivalent OTU), 41 percent to genus, and 26 percent to greater than 

genus (for example, tribe, subfamily, family). Nonbenthic microcrustaceans were not sorted or 

identified because they represented contamination from sieving the samples in the field. Polychaetes 

(Manayunkia speciosa) and nematodes were not sorted because they were poorly sampled using 500-μm 

mesh. 
41

Benthic Invertebrate Nutrient Release Experiments

Benthic invertebrates used for nutrient release (efflux) experiments were collected using the 

modified Ekman grab. Collected grabs were placed in an ice chest lined with doubled polyethylene 

bags. Lake water was added, and a battery powered pump provided aeration. Samples were cooled to 

maintain the approximate temperature at which benthic invertebrates were collected during transport 

back to the laboratory. Ice was kept in a bag separate from the invertebrates. 

Samples were sieved in the laboratory using lake water. Retained invertebrates were placed in 

Whatman (GF/F) filtered lake water and then artificial lake water (table 18) to remove any attached 

debris. Either individual organisms or multiple organisms of the same taxon were placed in a 15-mL 

acid-washed Teflon container along with 5 mL of artificial lake water. The container was then placed in 

an incubator set at the temperature at which invertebrates were collected. Release rates were typically 

measured over a 4-hour period. 

Once the test period ended, the invertebrate(s) was removed and placed in 70-percent ethyl 

alcohol for later taxonomic confirmation. The remaining test solution was filtered into a 6.5-mL 

Vacuette vial using a 0.22-μm polycarbonate syringe filter. Procedural blanks represented >20 percent

of all samples. Typically, a series of tests was performed on the same day. The number of test vials per 

series ranged from approximately 50 to just over 100. Nutrient analyses are described below.

Chemical Parameters

Dissolved Nutrients

Nutrient samples were collected from profiler syringes or grab samples (~0.5-m depth), then 

filtered (0.2-μm polycarbonate membranes) and immediately refrigerated in darkness without 

acidification. Concentrations were determined for dissolved (0.2-μm filtered) nitrate-nitrite (EPA 

method 353.1), ammonia (EPA method 350.1), orthophosphate (SRP; EPA method 365.2), and silica 

(EPA method 370.1) by batch automated spectrophotometry (Aquakem 250, Thermo Scientific). As 

mentioned above, concentration units for dissolved nutrients are in terms of the element rather than the 

associated molecule, with the exception of silica as SiO2. For example, the concentration unit specified 

herein for dissolved ammonia is milligrams of nitrogen per liter (mg-N L−1), as opposed to an ammonia 

species. 

Certified standards for all nutrients were purchased from Microgenics Corporation (Fremont, 

California), and a new calibration curve is established for each analyte on each analytical day. For 

further quality assurance and quality control (QA/QC), certified standards and blanks are also analyzed 

every 10–20 samples. 

Method detection limits (MDL) for each analyte were determined and established for all samples 

reported. For nitrate and nitrite, the MDL is 0.01 mg-N L−1. For ammonia, the MDL is 0.007 mg-N L−1. 

For orthophosphate (SRP), the MDL is 0.002 mg-P L−1. For silica, the MDL is 0.1 mg-SiO2 L−1. 

Dissolved Organic Carbon (DOC)

Dissolved organic carbon samples were collected in duplicate in baked 60-mL glass bottles with 

acid-washed fluoroethylene-polymer caps and filtered (0.7-μm baked glass-fiber filter) for analysis by 

high-temperature catalytic combustion (Vandenbruwane and others, 2007). 

Potassium phthalate was used as the standard. Low-DOC water (blanks less than 40 μg-C L−1) 

was generated from a double-deionization unit with additional ultraviolet treatment (Milli-Q Gradient, 

Millipore Corporation). A new calibration curve is established on each analytical day. For further 
42

QA/QC, certified standards and blanks are also analyzed every 10–20 samples. For DOC, the MDL is 

0.1 mg-C L−1. 

Dissolved Trace Elements

Water-column samples were collected in duplicate in 250-mL acid-washed high-density 

polyethylene bottles, filtered (0.2-μm polycarbonate membrane) using clean technique (Fitzwater and 

others, 1982), and acidified (pH 2). Pore-water samples were filtered in-line and acidified before 

analysis by the above water-column methods. All solutions were analyzed for trace metals by 

inductively coupled plasma mass spectrometry (ICP-MS, EPA method 200.8) (Topping and Kuwabara, 

1999; Topping and Kuwabara, 2003). As with dissolved macronutrients, concentration units for 

dissolved trace elements are in terms of the element rather than the associated molecule. For example, 

the concentration unit specified herein for dissolved vanadium is μg-V L−1, rather than the vanadate 

ligand. 

Certified standards for all trace metals were purchased from High Purity Standards (Charleston, 

South Carolina), and a new calibration curve is established for each metal on each analytical day. For 

further QA/QC, certified standards and blanks are also analyzed every 10–20 samples. 

Method detection limits (MDL) for each metal were determined and established for all samples 

reported. For nitrate and nitrite, the MDL is 0.01 mg-N L−1. For ammonia, the MDL is 0.007 mg-N L−1. 

For orthophosphate (SRP), the MDL is 0.002 mg-P L−1. For silica, the MDL is 0.1 mg-SiO2 L−1. 

Physical Parameters

Heat Transfer Modeling

As a comparative approach to estimating hydrologic transport across the sediment-water 

interface in the lake, a set of three temperature loggers (fig. 5) was deployed at 6 of the 19 piezometer 

sites. These sites, from north to south, included LN01, ON01, OC01, SPR, OS02, and LS01. 

Temperature loggers were installed onto 1.25-inch, schedule 80 PVC pipe to measure a time series of 

water temperature at (1) the sediment-water interface, (2) 30 cm below that interface, and (3) 150 cm 

below that interface. Depth of the temperature sensors was regulated by a PVC cross member that 

contacted the piezometer plate and served as a vertical stop. The unconsolidated nature of the lakebed 

sediments at these six sites allowed the manual deployment of the thermistor pipes without need of a 

slide hammer. Once deployed, the thermistor pipes were tied with twine to the adjacent piezometer pipe.

Temperature loggers recorded water temperature at 5-minute intervals. Within 2 months of deployment, 

the temperature-sensor pipes at sites OC01 and OS02 were lost owing to pipe breakage or possibly 

vandalism. Therefore, heat-transfer modeling, using program 1DTempPro V2 (Koch and others, 2015), 

was done for the remaining four sites LN01, ON01, SPR, and LS01 (table 19). The temperature data 

were segmented into three 2-week periods. The first was as the bloom developed between July 1 and 14, 

2015. The second was within the cyanobacterial blooms between August 5 and 19, 2015, and the final 

post-bloom period was between September 10 and 24, 2015. The 1DTempPro program uses the 

temperature time series and estimates for sediment characteristics (that is, porosity, thermal 

conductivity, and heat capacity) to model an output of groundwater discharge. The modeled discharge, 

along with the overlying water and 1-cm depth SRP concentrations for the nearest sampling date in 

2013, were used to estimate the advective flux of SRP.
43

Figure 5. Photograph showing a piezometer and thermistor string deployed at one of the northern littoral-zone 

sites. Photograph by Francis Parchaso of the U.S. Geological Survey.

Vertical Hydraulic Gradient.

Previous estimates of heat transfer near the sediment-water interface provided preliminary data 

suggesting the potential importance of physical (hydrologic) transport of solutes between the lakebed 

and overlying water column (Kuwabara and others, 2009). To examine this process further, piezometers 

were deployed at all 18 open-lake and littoral-zone sites studied in the 2013 spatial-variability 

experiment (fig. 5). A piezometer was deployed at an additional seep/spring site (SPR, fig. 1) in 

proximity to: (1) visible groundwater inputs at the lakeshore, and (2) fish-habitat spawning experiments.

Piezometers were deployed to a depth of 150 cm below the lakebed and threaded to 0.75-inch 

galvanized pipe. A transition from 0.75-inch to 1-inch pipe was used to locate a vertical stop for a 

pressure transducer/data logger placed to record the head 150 cm below the lakebed. The piezometer 

depth was set by a 40-cm square PVC plate (1-cm thick) that rested on the lakebed, with a central hole 

that was slipped onto and then clamped onto the piezometer pipe. Stainless steel band clamps secured a 

2-inch schedule 40 PVC stilling well to the piezometer pipe. A pipe cap was perforated at the bottom of 

the stilling well to serve as a vertical stop for a pressure transducer placed to record lake stage. A 381-

μm copper mesh was epoxied to the inner cap surface to retard biofouling of the outer transducer. For 
44

each piezometer deployment site, the time series of water-column heights downloaded from the 

transducers in the piezometer and the stilling well at 5-minute intervals were used to calculate the 

dimensionless vertical hydraulic gradient (VHG, the difference between the inner and outer transducer 

heights divided by the piezometer deployment depth, 150 cm). The product of VHG and the hydraulic 

conductivity (K) provides an estimate of vertical discharge. The installations did not perform as 

intended in that the positions of the pressure transducers were not stable and appeared to shift in 

response to external influences (such as wave action). Moreover, when transducers were removed and 

replaced during site visits, they did not always return to their original positions. Unfortunately, the 

uncertainty in the data owing to this problem is on the same order as the head gradient. Consequently, 

the magnitude and direction of the head gradient cannot be reliably determined from the data. Because 

of this, advective flux estimates presented at this point rely solely on the temperature data (temperature 

loggers were not affected by this problem). This effort did demonstrate the feasibility of measuring head 

gradients between the lake and underlying sediment. The problems experienced with the 2015 data 

collection can be avoided in future studies with modest modifications to the piezometer design and 

periodic field verification of precise transducer positions. 

Flocculation and Settling Methodology

For this work, the term “floc” refers to suspended particles that may aggregate or disaggregate to 

change in size and settling velocity. Three surveys (conducted on May 6, 2015; July 30, 2015; and 

October 6, 2015) collected floc population samples through the water column from two pre-selected 

sites in UKL where flocculation data was required. Site LS01 (Littoral-Zone South) was close to the 

shoreline in Howard’s Bay, and the site ML (Middle Lake) (fig. 1) was in the vicinity of the middle of 

the lake. The sites were selected to correspond with locations where USGS and Reclamation long-term 

instrumentation were deployed and regular observations made throughout 2015 and previous years. 

The floc data was collected at each station in a quasi-Eulerian manner. The research vessel was 

brought close to stationary at each sampling station and remained on station, anchored from the bow, 

during floc data measurements. 

Six floc populations (three from each site) were sampled within UKL during each of the three 

2015 survey using the INSSEV-LF: In-Situ Settling Velocity Instrument. In order to obtain a 

representative picture of how floc characteristics varied through depth at each site in the lake, water 

samples (from which flocs were later extracted) were collected nominally at points 10 percent (dp_10, 

near-surface), 70 percent (dp_70, near-bed), and 90 percent (dp_90, close-to-bed) of the local total water 

column depth. Only results from the dp_10 and dp_90 sampling points are included in this summary 

report. Vessel position and logger time synchronization were established from a portable Global 

Positioning System (GPS) unit. Pacific local time was used to time reference all the survey floc data.

Manning and Schoellhamer (2013) describe the typical INSSEV-LF measurement methods in 

detail, and they are summarized here. The LF (LabSFLOC) version of INSSEV is a hybrid system that

combines two key components: (1) the low intrusive LabSFLOC system, a high resolution video-based 

device to measure the individual floc properties; (2) an in-situ estuarine floc sampler. The LabSFLOC—

Laboratory Spectral Flocculation Characteristics—instrument (Manning, 2006) enables individual floc 

sizes and settling velocities to be measured simultaneously and was set up on a 22-foot (~6.8 m) long, 

shallow draft vessel. The LabSFLOC-2 (version 2; A.J. Manning, written commun., 2016) digital 

camera resolution can practically view particles/flocs down to 5 μm in size (fig. 3) and as large as 8 

mm. Settling velocities generally ranging from 0.01 to 45 mm s

−1 can be measured by LabSFLOC-2, 

and it can operate within suspended particulate matter concentrations (SPMCs) of just a few mg L−1, 

with a practical upper operating limit of ~8.5 g L−1. 
45

A 2.2-L Van Dorn horizontal sampling tube (fig. 3) with a 10-kg weight suspended from the 

underside of the tube was used to collect a water sample nominally at each of the three sampling depths. 

A small subsample containing a floc population was carefully extracted from the horizontal Van Dorn 

using a 0.4-m-long modified pipette (nominal internal diameter = 6 mm). This sample was immediately 

transferred to the LabSFLOC settling chamber, whereby the flocs passed from the vertically held pipette 

to the chamber and settled solely under gravity (fig. 3). Settling velocity and diameter of individual flocs 

were determined from the digital video AVI (Audio Video Interleave) recordings using the H.R. 

Wallingford Ltd DigiFloc software, version 1.0 (T. Benson and A.J. Manning, written commun., 2016). 

Floc effective density (ρe), porosity, and floc mass were calculated using specially derived algorithms 

(Fennessy and others, 1997) adapted by Manning (2004). 

The floc collection and subsampling protocols are both proven floc sampling techniques (A.J. 

Manning, written commun., 2016; Manning and others, 2010a, b; Mehta and others, 2009), which 

permit minimal floc interference and flocs that are representative of the ambient population—especially 

in terms of floc size and settling velocity distributions. Extensive testing of this sampling protocol 

revealed that this technique created minimal floc disruption during acquisition (see Gratiot and 

Manning, 2004). The floc sampling techniques also provide control volumes, which permit settling flux 

estimations (that is, the product of the floc mass and settling velocity). 

In addition to determining sample mean floc properties, a diameter of 160 μm is a convenient 

demarcation between smaller microflocs and larger macroflocs (Manning, 2001); these are typically 

used for numerical sediment transport modeling parameterization (for example, Soulsby and others, 

2013; Winterwerp and others, 2006). 

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Winterwerp, J.C., Manning, A.J., Martens, C., de Mulder, T., and Vanlede, J., 2006, A heuristic formula 

for turbulence-induced flocculation of cohesive sediment: Estuarine, Coastal & Shelf Science, v. 68, 

p. 195–207. 

Wolanski, E., 2007, Estuarine ecohydrology (1st ed.): Amsterdam, Elsevier, 186 p. 

Wong, S.W., Barry, M.J., Aldous, A.R., Rudd, N.T., Hendrixson, H.A., and Doehring, C.M., 2011, 

Nutrient release from a recently flooded delta wetland—Comparison of field measurements to 

laboratory results: Wetlands, v. 31, p. 433–443. 
53

Wood, T.M., and Gartner, J.W., 2010, Use of acoustic backscatter and vertical velocity to estimate 

concentration and dynamics of suspended solids in Upper Klamath Lake, south-central Oregon—

Implications for Aphanizomenon flos-aquae: U.S. Geological Survey Scientific Investigations Report 

2010–5203, 20 p., accessed at http://pubs.usgs.gov/sir/2010/5203/.

Wood, T.M., Hoilman, G.R., and Lindenberg, M.K., 2006, Water-quality conditions in Upper Klamath 

Lake, Oregon, 2002–04: U.S. Geological Survey Scientific Investigations Report 2006–5209, 54 p., 

accessed at http://pubs.usgs.gov/sir/2006/5209/. 

Wood, T.M., Wherry, S.A., Carter, J.L., Kuwabara, J.S., Simone, N.S., and S.A. Rounds, 2013, 

Technical evaluation of a total maximum daily load model for Upper Klamath and Agency Lakes, 

Oregon: U.S. Geological Survey Open-File Report 2013–1262, 75 p., accessed at 

http://pubs.usgs.gov/of/2013/1262/. 
54

Tables

Table 1. Sampling site locations and descriptions within and up gradient of Upper Klamath Lake, Oregon. See 

also figure 1.

Site Description Latitude Longitude

Lake sites WGS 84 Datum

MDN Mid-lake north N42.4391 W122.0122

MDT Mid-lake trench N42.3848 W121.9272

ML Mid-lake N42.3847 W121.8664

SPR Spring within Lake N42.3988 W121.8212

LN01 Littoral north 1 N42.4901 W122.0023

LN02 Littoral north 2 N42.4294 W122.0387

LN03 Littoral north 3 N42.4057 W122.0034

ON01 Open-lake north 1 N42.4051 W122.0206

ON02 Open-lake north 2 N42.4362 W121.9715

ON03 Open-lake north 3 N42.4368 W122.0444

LC01 Littoral central 1 N42.4613 W121.9309

LC02 Littoral central 2 N42.4472 W121.8802

LC03 Littoral central 3 N42.4626 W121.9041

OC01 Open-lake central 1 N42.4219 W121.8866

OC02 Open-lake central 2 N42.4170 W121.8503

OC03 Open-lake central 3 N42.4217 W121.8684

LS01 Littoral south 1 N42.3368 W121.9367

LS02 Littoral south 2 N42.3323 W121.9368

LS03 Littoral south 3 N42.3439 W121.9254

OS01 Open-lake south 1 N42.2864 W121.8526

OS02 Open-lake south 2 N42.3586 W121.8635

OS03 Open-lake south 3 N42.3200 W121.8964

TR01 Trench 1 N42.3679 W121.8937

TR02 Trench 2 N42.3918 W121.9350

TR03 Trench 3 N42.4179 W121.9414

Inlet tributary sites

Sprague River N42.5864 W121.8373

Spring Creek N42.6412 W121.8774

Williamson River N42.7403 W121.8342

Wood River N42.6464 W121.9948
55

Table 2. Summary of results from 2013 sonde deployments in Upper Klamath Lake, Oregon.

[m, meter; μS cm−1, microsiemen per centimeter; % DO, percent dissolved oxygen; mg L−1, milligram per liter; na, data from 

that period is not available. Times listed are Pacific Standard Time. Slightly darkened rows in the table for June 13, 2013, are 

provided to help the reader distinguish between results relative to sampling dates] 

Date Site Time

Depth

m

Specific

conductivity

μS cm−1

Temp.

°C

% DO

saturation

(local)

Vertical % 

DO

saturation

decrease (%)

DO

concentration

(mg L−1) pH

5/1/13 MDN 13:00 0.0 94 11.8 102.2 7.6 9.0 6.9

3.6 109 11.6 94.6 8.4 6.8

5/1/13 MDT 9:54 0.0 100 11.9 95.2 3.5 9.3 7.1

14.1 100 11.3 91.7 9.1 7.0

5/23/13 LN01 15:01 0.0 105 14.1 99.0 −1.8 8.8 8.1

2.1 106 12.3 100.8 9.3 7.8

5/23/13 LN02 13:34 0.0 106 13.7 100.7 1.9 9.0 8.1

1.9 106 13.3 98.8 8.9 7.8

5/23/13 LN03 12:33 0.0 107 13.2 98.3 2.0 8.9 8.0

1.7 107 13.1 96.3 8.7 7.8

5/23/13 ON01 12:59 0.0 106 13.3 101.8 2.0 9.2 8.2

2.6 106 13.1 99.8 9.0 7.8

5/23/13 ON02 11:58 0.0 106 12.7 99.3 0.3 9.1 8.2

3.4 106 12.7 99.0 9.0 7.5

5/23/13 ON03 14:01 0.0 106 13.9 101.1 5.8 9.0 8.1

2.4 106 13.4 95.3 8.6 7.8

5/23/13 LC01 15:35 0.0 105 13.6 100.9 −1.5 9.0 8.1

1.5 106 12.3 102.4 9.4 7.9

5/23/13 LC02 16:19 0.0 215 14.6 101.7 6.6 8.9 8.0

1.8 188 12.8 95.1 8.7 7.7

5/23/13 LC03 15:59 0.0 168 13.3 100.4 1.4 9.0 8.0

1.6 159 12.8 99.0 9.0 8.1

5/23/13 OC01 9:58 0.0 115 12.4 61.4 3.3 6.6 8.3

2.9 114 12.2 58.1 6.2 8.2

5/23/13 OC02 10:31 0.0 111 14.9 62.4 6.3 6.3 8.1

2.3 111 13.2 56.1 5.9 8.0

5/23/13 OC03 10:16 0.0 110 13.2 59.3 2.1 6.2 8.2

2.9 116 12.7 57.2 6.1 8.1

5/23/13 LS01 13:29 0.0 172 13.1 99.5 34.9 9.0 8.3

1.7 209 13.1 64.6 5.9 8.3

5/23/13 LS02 13:45 0.0 204 12.3 99.3 −3.7 9.2 8.3

1.9 205 12.6 103.0 9.5 8.3

5/23/13 LS03 13:10 0.0 209 13.5 100.0 49.9 9.0 8.3

2.0 211 13.6 50.1 4.5 8.3

5/23/13 OS01 12:14 0.0 107 12.4 98.1 6.6 9.8 8.1

2.5 108 12.6 91.5 8.4 8.0

5/23/13 OS02 12:41 0.0 107 12.5 102.3 6.7 9.4 8.1

2.4 106 12.5 95.6 8.8 8.1
56

Date Site Time

Depth

m

Specific

conductivity

μS cm−1

Temp.

°C

% DO

saturation

(local)

Vertical % 

DO

saturation

decrease (%)

DO

concentration

(mg L−1) pH

5/23/13 OS03 11:44 0.0 230 13.1 93.5 76.4 8.5 8.0

4.1 277 13.2 17.1 1.5 7.5

5/23/13 TR01 7:51 0.0 113 13.2 53.3 −0.2 5.6 8.1

6.9 113 13.1 53.5 5.6 8.0

5/23/13 TR02 8:46 0.0 113 13.5 58.2 2.5 6.1 8.2

13.8 113 13.3 55.7 5.8 8.1

5/23/13 TR03 9:24 0.0 113 13.8 60.8 5.1 6.3 8.2

7.7 113 13.4 55.7 5.8 8.1

6/13/13 LN01 9:41 0.0 110 17.1 117.2 6.3 9.7 9.3

2.0 108 16.7 110.9 9.2 9.0

6/13/13 LN02 9:00 0.0 113 17.8 122.4 4.8 10.0 9.4

1.7 113 17.7 117.6 9.6 9.0

6/13/13 LN03 8:17 0.0 114 17.6 107.6 1.0 8.8 9.8

2.0 114 17.6 106.6 8.7 9.7

6/13/13 ON01 8:39 0.0 116 17.4 117.5 29.3 9.7 9.4

2.7 119 17.6 88.2 7.2 8.3

6/13/13 ON02 7:42 0.0 115 17.4 123.6 19.2 10.2 9.4

3.4 119 17.3 104.4 8.6 8.1

6/13/13 ON03 9:15 0.0 113 17.9 118.8 61.0 9.7 9.3

2.9 113 17.8 57.8 4.7 7.8

6/13/13 LC01 10:14 0.0 112 17.6 129.1 45.1 10.6 9.5

1.7 111 16.3 84.0 7.1 8.6

6/13/13 LC02 10:51 0.0 113 18.0 125.5 12.6 10.2 9.3

2.0 114 17.7 112.9 9.2 9.2

6/13/13 LC03 10:32 0.0 115 17.5 137.0 25.6 11.2 9.4

1.9 112 16.8 111.4 9.3 8.9

6/13/13 OC01 11:03 0.0 128 17.7 108.2 9.6 10.3 na

2.5 123 17.1 98.6 9.5 na

6/13/13 OC02 11:30 0.0 125 17.6 104.1 13.6 9.9 na

2.8 315 17.5 90.5 8.6 na

6/13/13 OC03 11:14 0.0 119 17.3 104.8 14.8 10.0 na

3.1 303 16.7 90.0 8.7 na

6/13/13 LS01 13:36 0.0 113 17.5 114.7 18.9 9.5 9.6

1.6 115 17.2 95.8 8.0 9.5

6/13/13 LS02 13:56 0.0 112 17.3 111.4 5.2 9.3 9.6

1.8 112 17.3 106.2 8.9 9.6

6/13/13 LS03 13:13 0.0 116 17.8 112.3 107.5 9.3 9.6

1.9 114 17.6 4.8 0.4 8.0

6/13/13 OS01 12:14 0.0 125 16.7 99.9 3.1 8.4 9.4

2.0 124 16.5 96.8 8.2 9.3

6/13/13 OS02 12:48 0.0 115 17.4 101.2 −0.1 8.4 9.4

2.1 116 17.4 101.3 8.4 9.4
57

Date Site Time

Depth

m

Specific

conductivity

μS cm−1

Temp.

°C

% DO

saturation

(local)

Vertical % 

DO

saturation

decrease (%)

DO

concentration

(mg L−1) pH

6/13/13 OS03 11:42 0.0 143 16.8 101.0 3.9 8.5 9.4

3.8 138 16.4 97.1 8.3 9.3

6/13/13 TR01 9:03 0.0 110 17.5 96.2 20.3 9.1 7.0

6.3 117 17.0 75.9 7.3 6.4

6/13/13 TR02 9:59 0.0 na 17.6 92.8 3.6 8.8 6.8

13.8 na 17.7 89.2 8.4 6.9

6/13/13 TR03 10:27 0.0 na 18.1 103.4 13.6 9.7 7.3

7.8 na 17.9 89.8 8.5 7.1

7/3/13 LN01 13:18 0.0 265 25.5 140.4 86.2 11.5 9.9

1.5 247 22.6 54.2 4.7 9.5

7/3/13 LN02 12:32 0.0 255 21.9 91.5 39.9 8.0 7.0

1.3 248 20.1 51.6 4.7 9.5

7/3/13 LN03 11:54 0.0 312 25.8 105.5 38.3 8.6 10.2

1.7 260 24.7 67.2 5.6 9.7

7/3/13 ON01 12:19 0.0 273 24.0 112.8 87.9 9.5 10.0

2.5 251 20.3 24.9 2.3 9.3

7/3/13 ON02 11:18 0.0 256 23.0 82.6 47.7 7.2 9.0

3.3 254 20.5 34.9 3.1 9.4

7/3/13 ON03 12:54 0.0 275 23.3 101.2 54.6 8.6 10.0

2.2 248 19.8 46.6 4.3 9.5

7/3/13 LC01 13:39 0.0 254 24.6 114.5 75.6 9.5 9.6

1.6 239 21.2 38.9 3.5 9.5

7/3/13 LC02 14:17 0.0 274 23.2 119.1 4.4 10.2 9.9

1.5 261 21.0 114.7 10.2 9.9

7/3/13 LC03 14:06 0.0 253 23.6 118.9 81.7 10.1 9.7

1.5 244 20.7 37.2 3.3 9.5

7/3/13 OC01 9:15 0.0 124 22.8 134.3 65.6 10.0 10.1

2.5 116 18.8 68.7 5.5 9.7

7/3/13 OC02 9:56 0.0 128 23.0 127.3 55.6 9.4 10.1

2.7 118 21.1 71.7 5.5 9.8

7/3/13 OC03 9:35 0.0 128 22.5 140.3 126.0 10.5 10.3

3.4 116 18.4 14.3 1.2 9.5

7/3/13 LS01 10:21 0.0 174 25.7 na na 10.0

1.6 245 23.5 na na 8.5

7/3/13 LS02 10:38 0.0 130 26.5 na na 9.5

1.6 136 24.1 na na 9.3

7/3/13 LS03 9:57 0.0 227 25.2 na na 9.1

1.5 373 23.6 na na 8.2

7/3/13 OS01 9:02 0.0 119 22.4 na na 9.6

2.2 206 23.3 na na 9.6

7/3/13 OS02 9:36 0.0 na 23.8 na na 9.2

2.2 na 23.5 na na 9.2
58

Date Site Time

Depth

m

Specific

conductivity

μS cm−1

Temp.

°C

% DO

saturation

(local)

Vertical % 

DO

saturation

decrease (%)

DO

concentration

(mg L−1) pH

7/3/13 OS03 8:34 0.0 256 24.1 45.2 41.9 3.8 9.8

3.5 385 22.2 3.3 0.3 9.5

7/3/13 TR01 7:30 0.0 120 23.2 74.9 2.1 5.5 9.8

6.0 121 22.4 72.8 5.5 9.6

7/3/13 TR02 8:08 0.0 121 23.1 111.3 108.5 8.2 9.9

14.2 125 19.5 2.8 0.2 9.5

7/3/13 TR03 8:37 0.0 122 22.8 105.5 91.1 7.8 9.8

7.9 121 20.0 14.4 1.1 9.2
59

Table 3. 2013 dissolved (0.2-micrometer filtered) macronutrient benthic fluxes from profiler deployments.

[mg m−2 d−1, milligram per square meter per day; nd, nondetectable benthic flux owing to concentrations from profiler samples that were consistently below 

detection limits for that solute.; blank cells, flux values that are not available, typically owing to a problem with profiler deployment; S.D., standard deviation for 

the specified n replicates. Slightly darkened rows in the table are provided to help the reader distinguish results between sampling dates]

Diffusive flux

SRP as P Ammonia as N Nitrate as N Silica as SiO2 DOC as C

mg m−2 d−1 mg m−2 d−1 mg m−2 d−1 mg m−2 d−1 mg m−2 d−1

Date SiteName Mean n S.D. Mean n S.D. Mean n S.D. Mean n S.D. Mean n S.D.

4/30/13 MDN_A 2.19 2 0.03 10.21 2 7.16 −9.61 2 2.27 0.72 2 0.05

4/30/13 MDN_B 2.64 2 0.30 17.23 2 9.11 −10.82 2 1.70 −0.36 2 0.02

4/30/13 MDN_C 1.74 2 0.06 17.11 2 2.84 −12.42 2 0.57 0.77 2 0.87

4/30/13 TRRecon 4.55 2 1.61 12.26 2 1.24 −2.33 2 1.62 0.61 2 0.09

5/23/13 LN01 12.73 2 3.72 10.40 2 3.84 nd 2 88.5 2 15.4 0.64 2 0.10

5/23/13 LN02 11.03 2 6.91 8.38 2 3.03 nd 2 57.5 2 56.1 0.91 2 0.33

5/23/13 LN03 4.42 2 0.68 17.38 2 3.51 nd 2 188.0 2 10.1 2.15 2 1.34

5/23/13 ON01 2.86 2 2.31 4.13 2 1.98 nd 2 62.0 2 28.2 0.52 2 0.26

5/23/13 ON02 2.14 2 0.27 6.69 2 3.56 nd 2 43.5 2 0.9 0.02 2 0.43

5/23/13 ON03 1.72 2 0.33 6.36 2 0.68 nd 2 76.4 2 6.5 −0.18 2 0.49

5/23/13 LC01 6.71 2 0.39 15.89 2 0.29 nd 2 200.5 2 2.2 −0.53 2 0.25

5/23/13 LC02 0.13 2 0.06 4.01 2 0.24 nd 2 37.1 2 11.8 0.77 2 0.58

5/23/13 LC03 1.90 2 1.14 4.11 2 0.10 nd 2 24.6 2 16.8 0.70 2 0.01

5/23/13 OC01 1.49 2 0.43 9.63 2 1.51 nd 2 54.1 2 22.3 −0.62 2 0.16

5/23/13 OC02 0.36 2 0.45 3.01 1 nd 2 15.8 2 17.7 −0.43 2 0.13

5/23/13 OC03

5/23/13 LS01 0.94 2 0.41 6.19 2 4.29 nd 2 52.7 2 5.0 −0.87 2 0.79

5/23/13 LS02 1.33 2 0.59 1.93 2 1.52 nd 2 31.4 2 8.7 −0.25 2 0.13

5/23/13 LS03 0.63 2 0.37 6.61 1 nd 2 18.2 2 4.8 −0.54 2 0.19

5/23/13 OS01 1.28 2 0.16 12.13 2 8.27 nd 2 101.6 2 46.9 −1.15 2 0.01

5/23/13 OS02 2.92 2 3.17 4.25 2 4.08 nd 2 145.5 2 0.9 −0.09 2 0.62

5/23/13 OS03 10.20 2 1.74 9.85 2 3.58 nd 2 145.1 2 8.7 1.22 2 0.15

5/23/13 TR01 6.12 2 0.73 21.60 2 6.93 nd 2 93.5 2 8.9 −0.28 2 0.08

5/23/13 TR02

5/23/13 TR03 4.38 2 0.40 10.70 2 0.18 nd 2 90.8 2 24.0 −0.44 2 0.39
60

Diffusive flux

SRP as P Ammonia as N Nitrate as N Silica as SiO2 DOC as C

mg m−2 d−1 mg m−2 d−1 mg m−2 d−1 mg m−2 d−1 mg m−2 d−1

Date SiteName Mean n S.D. Mean n S.D. Mean n S.D. Mean n S.D. Mean n S.D.

6/13/13 LN01 4.57 2 2.51 4.64 2 2.23 nd 2 64.5 2 5.6 0.78 2 0.30

6/13/13 LN02 4.85 2 0.61 8.04 2 0.60 nd 2 48.2 2 1.0 0.69 2 0.16

6/13/13 LN03 33.27 2 14.88 34.27 2 22.83 nd 2 176.2 2 8.9 1.96 2 0.03

6/13/13 ON01 4.60 2 1.82 5.98 2 2.78 nd 2 65.3 2 44.7 −1.19 2 0.85

6/13/13 ON02 2.45 2 1.72 2.02 2 0.27 −0.03 2 0.05 43.8 2 3.8 0.29 2 0.15

6/13/13 ON03 6.46 2 3.81 2.01 2 0.66 nd 2 38.0 2 0.3 0.62 2 0.88

6/13/13 LC01 3.95 2 0.47 8.57 2 0.60 nd 2 105.3 2 1.0 0.00 2 0.25

6/13/13 LC02 0.43 2 0.19 2.22 2 1.53 nd 2 35.7 2 2.8 1.11 2 1.25

6/13/13 LC03 4.22 2 1.26 3.67 2 0.60 nd 2 86.8 2 0.5 0.00 2 0.25

6/13/13 OC01 1.80 2 0.09 6.01 2 0.50 nd 2 80.7 2 3.1 −0.72 2 0.16

6/13/13 OC02 4.21 2 0.43 6.02 2 0.74 nd 2 117.1 2 2.6 −0.29 2 0.41

6/13/13 OC03 4.84 2 1.75 2.93 2 2.00 nd 2 75.2 2 18.8 0.27 2 0.13

6/13/13 LS01 1.16 2 0.15 10.12 2 4.85 nd 2 40.2 2 17.1 −1.01 2 0.26

6/13/13 LS02 1.64 2 0.11 14.65 2 6.59 nd 2 88.7 2 3.6 −1.60 2 0.04

6/13/13 LS03 1.17 2 0.70 8.57 2 0.67 nd 2 106.0 2 26.0 −0.94 2 0.15

6/13/13 OS01 1.27 2 0.56 3.77 2 0.93 nd 2 95.2 2 34.4 −1.09 2 0.72

6/13/13 OS02 1.78 2 0.06 8.07 2 2.74 nd 2 107.6 2 20.3 −2.09 2 0.01

6/13/13 OS03 8.82 2 1.90 12.30 2 2.25 nd 2 105.6 2 30.3 0.36 2 0.00

6/13/13 TR01 3.83 2 0.04 5.65 2 3.16 nd 2 68.2 2 7.2 0.18 2 0.00

6/13/13 TR02 4.00 2 1.04 7.82 2 0.11 nd 2 105.9 2 5.7 −0.27 2 0.15

6/13/13 TR03 10.37 2 6.66 12.30 2 9.57 nd 2 224.6 2 27.4 0.33 2 1.02

7/3/13 LN01 2.50 2 0.04 6.05 2 2.30 nd 2 95.0 2 22.5 −2.03 2 0.38

7/3/13 LN02 2.61 2 0.65 10.39 2 1.23 −0.11 2 0.16 186.3 2 36.9 −2.03 2 0.52

7/3/13 LN03 12.78 2 8.11 42.67 2 16.58 nd 2 579.2 2 426.5 −1.11 2 2.09

7/3/13 ON01 0.68 2 0.55 5.89 2 0.98 nd 2 52.6 2 42.3 −1.84 2 2.08

7/3/13 ON02 1.43 2 0.11 7.06 2 1.35 nd 2 68.5 2 61.0 −0.87 2 0.60

7/3/13 ON03 5.62 2 0.55 3.25 2 1.49 nd 2 193.0 2 18.9 −0.65 2 0.30

7/3/13 LC01 2.71 2 1.66 8.49 2 6.94 nd 2 104.0 2 23.7 0.44 2 0.30

7/3/13 LC02 0.99 2 0.80 22.32 2 3.12 nd 2 185.5 2 75.4 1.62 2 0.89

7/3/13 LC03 4.97 2 1.68 16.67 2 8.98 nd 2 138.7 2 5.6 −1.63 2 0.50
61

Diffusive flux

SRP as P Ammonia as N Nitrate as N Silica as SiO2 DOC as C

mg m−2 d−1 mg m−2 d−1 mg m−2 d−1 mg m−2 d−1 mg m−2 d−1

Date SiteName Mean n S.D. Mean n S.D. Mean n S.D. Mean n S.D. Mean n S.D.

7/3/13 OC01 2.40 2 0.60 11.05 2 1.06 −0.11 2 0.16 109.1 2 31.3 −0.54 2 0.20

7/3/13 OC02 2.77 2 0.33 11.23 2 2.64 −0.11 2 0.16 285.6 2 64.7 −0.95 2 0.06

7/3/13 OC03 6.71 2 3.12 14.57 2 11.81 −0.22 2 0.00 137.9 2 12.5 −1.63 2 1.30

7/3/13 LS01 0.54 2 0.34 6.00 2 5.43 nd 2 23.8 2 15.3 −0.95 2 2.02

7/3/13 LS02 0.60 2 0.50 11.95 2 8.35 nd 2 69.2 2 13.1 −5.17 2 4.93

7/3/13 LS03 1.57 2 0.24 6.05 2 1.60 nd 2 201.1 2 44.8 −2.39 2 0.72

7/3/13 OS01 2.75 2 0.82 17.02 2 0.86 nd 2 110.5 2 33.4 −2.08 2 1.11

7/3/13 OS02 1.14 2 0.02 7.66 2 1.63 −0.12 2 0.16 52.2 2 8.6 −2.10 2 0.94

7/3/13 OS03 10.09 2 6.62 30.55 2 3.11 nd 2 175.7 2 32.5 −1.81 2 0.69

7/3/13 TR01 7.97 2 0.46 29.30 2 2.69 nd 2 142.8 2 7.6 −1.88 2 0.70

7/3/13 TR02 3.75 2 1.30 14.45 2 3.35 nd 2 107.1 2 21.4 −2.51 2 1.34

7/3/13 TR03 9.72 2 1.94 25.03 2 2.05 nd 2 143.2 2 25.1 −1.24 2 0.71
62

Table 4. Riverine-flux estimates for dissolved (0.2-micrometer filtered) solutes into Upper Klamath Lake, Oregon.

[Available at http://dx.doi.org/10.3133/ofr20161175.] 
63

Table 5. 2013 dissolved (0.2-micrometer filtered) macronutrient concentrations in the water column of Upper Klamath Lake, Oregon.

[Because the lake is generally shallow (site depths <3 meters [m]), water-column samples were collected just below the surface (~0.5 m), with the exception of 

the deeper trench (TR) sites (6.0–14.2 m depth). Trench sites were sampled at both the surface and ~1 m above the lakebed. Blank cells indicate values that are 

not available. Un-ionized ammonia (NH3

0

) concentrations were calculated from temperature and pH effects on ammonia speciation at their respective depths 

(Canadian Council of Ministers of the Environment, 2010). SRP, soluble reactive phosphorus; P, phosphorus; N, nitrogen; DOC, dissolved organic carbon; C, 

carbon; mg L−1

, milligram per liter; S.D. indicates standard deviation for the specified n replicates; na, data not available from that time period. Slightly darkened 

rows in the table for June 13, 2013, are provided to help the reader distinguish results between sampling dates]

SRP as P Ammonia as N Nitrate as N Silica as SiO2 DOC as C

mg L−1 mg L−1 mg L−1 mg L−1 mg L−1 mg L−1

Date SiteName Mean n S.D. Mean n S.D.

Unionized Mean n S.D. Mean n S.D. Mean n S.D.

4/30/13 MDN_A 0.028 2 0.000 0.205 2 0.000 0.000 0.20 2 0.00 3.79 2 0.03

4/30/13 MDN_B 0.028 2 0.000 0.205 2 0.000 0.000 0.20 2 0.00

4/30/13 MDN_C 0.028 2 0.000 0.205 2 0.000 0.000 0.20 2 0.00

4/30/13 TRRecon 0.290 2 0.000 0.155 2 0.000 0.000 0.18 2 0.00 3.84 2 0.05

4/30/13 TRRecon 0.360 2 0.000 0.187 2 0.000 0.001 0.18 2 0.00 3.83 2 0.02

5/23/13 LN01 0.078 2 0.000 0.063 2 0.001 0.002 <0.01 2 36.9 2 0.7 4.67 2 0.02

5/23/13 LN02 0.046 2 0.001 0.048 2 0.016 0.001 0.02 2 0.01 37.3 2 0.8 3.77 2 0.14

5/23/13 LN03 0.047 2 0.006 0.040 2 0.019 0.001 <0.01 2 38.0 2 0.2 3.83 2 0.08

5/23/13 ON01 0.021 2 0.017 0.054 2 0.008 0.002 <0.01 2 37.8 2 0.1 3.65 2 0.09

5/23/13 ON02 0.097 2 0.070 0.037 2 0.019 0.001 <0.01 2 37.4 2 0.0 3.58 2 0.01

5/23/13 ON03 0.048 2 0.000 0.042 2 0.015 0.001 <0.01 2 36.5 2 0.3 3.66 2 0.11

5/23/13 LC01 0.092 2 0.042 0.081 2 0.019 0.002 <0.01 2 36.8 2 0.4 3.90 2 0.06

5/23/13 LC02 0.077 2 0.015 0.035 2 0.006 0.001 <0.01 2 36.7 2 0.7 3.89 2 0.05

5/23/13 LC03 0.087 2 0.000 0.056 2 0.009 0.001 <0.01 2 39.8 2 0.4 3.70 2 0.06

5/23/13 OC01 0.041 2 0.001 0.061 2 0.001 0.002 0.01 2 0.00 38.0 2 0.1 3.64 2 0.01

5/23/13 OC02 0.080 2 0.000 0.071 2 0.033 0.002 0.01 2 0.01 37.8 2 0.3 3.84 2 0.00

5/23/13 OC03 0.164 2 0.000 0.055 2 0.001 0.002 <0.01 2 0.00 36.0 2 0.3 3.76 2 0.01

5/23/13 LS01 0.042 2 0.002 0.048 2 0.008 0.002 0.01 2 0.01 35.3 2 0.6 3.76 2 0.16

5/23/13 LS02 0.044 2 0.001 0.047 2 0.008 0.002 <0.01 2 0.00 36.0 2 0.1 3.61 2 0.01

5/23/13 LS03 0.042 2 0.000 0.062 2 0.001 0.003 <0.01 2 0.00 36.0 2 0.4 3.73 2 0.04

5/23/13 OS01 0.073 2 0.038 0.048 2 0.010 0.001 <0.01 2 0.00 35.8 2 0.3 3.81 2 0.09

5/23/13 OS02 0.063 2 0.030 0.080 2 0.041 0.002 <0.01 2 0.00 38.6 2 0.6 3.62 2 0.02

5/23/13 OS03 0.088 2 0.058 0.071 2 0.007 0.002 <0.01 2 0.00 36.9 2 0.1 3.64 2 0.01

5/23/13 TR01 0.099 2 0.068 0.052 2 0.002 0.002 <0.01 2 0.00 37.6 2 0.1 3.76 4 0.05

5/23/13 TR01 0.219 2 0.053 0.059 2 0.001 0.001 <0.01 2 0.00 37.6 2 0.2 na
64

SRP as P Ammonia as N Nitrate as N Silica as SiO2 DOC as C

mg L−1 mg L−1 mg L−1 mg L−1 mg L−1 mg L−1

Date SiteName Mean n S.D. Mean n S.D.

Unionized Mean n S.D. Mean n S.D. Mean n S.D.

5/23/13 TR02 0.083 2 0.045 0.078 2 0.037 0.003 <0.01 2 0.00 37.2 2 0.6 3.64 2 0.01

5/23/13 TR02 0.212 2 0.079 0.035 2 0.012 0.001 <0.01 2 0.00 38.0 2 0.4 3.66 2 0.01

5/23/13 TR03 0.097 2 0.054 0.044 2 0.016 0.001 <0.01 2 0.00 37.0 2 1.0 3.66 2 0.00

5/23/13 TR03 0.195 2 0.042 0.057 2 0.006 0.002 <0.01 2 0.00 38.7 2 0.4 3.72 2 0.08

6/13/13 LN01 0.049 2 0.001 <0.007 2 0.002 <0.01 2 0.00 31.2 2 0.3 3.92 2 0.02

6/13/13 LN02 0.026 2 0.005 0.029 2 0.011 0.013 0.04 2 0.01 33.1 2 0.3 4.07 2 0.09

6/13/13 LN03 0.018 2 0.000 0.024 2 0.031 0.016 0.01 2 0.00 35.5 2 0.2 4.97 2 0.16

6/13/13 ON01 0.037 2 0.001 <0.007 2 0.002 0.01 2 0.00 33.2 2 0.3 4.00 2 0.01

6/13/13 ON02 0.037 2 0.001 0.008 2 0.005 0.004 0.01 2 0.00 33.2 2 0.1 3.88 2 0.01

6/13/13 ON03 0.033 2 0.006 0.021 2 0.024 0.008 0.03 2 0.01 32.8 2 0.1 4.02 2 0.04

6/13/13 LC01 0.042 2 0.003 0.016 2 0.001 0.008 0.02 2 0.02 31.2 2 0.6 4.09 2 0.02

6/13/13 LC02 0.032 2 0.002 0.008 2 0.001 0.003 0.03 2 0.01 31.1 2 0.4 3.95 2 0.07

6/13/13 LC03 0.047 2 0.004 0.050 2 0.059 0.023 0.03 2 0.01 31.4 2 0.4 3.83 2 0.01

6/13/13 OC01 0.046 2 0.001 <0.007 2 0.000 0.01 2 0.00 31.6 2 0.6 4.11 2 0.03

6/13/13 OC02 0.039 2 0.001 0.009 2 0.000 0.000 0.01 2 0.00 30.9 2 0.3 4.11 2 0.03

6/13/13 OC03 0.045 2 0.006 <0.007 2 0.000 0.01 2 0.00 31.4 2 0.2 4.05 2 0.00

6/13/13 LS01 0.027 2 0.002 <0.007 2 0.002 0.01 2 0.00 30.7 2 0.2 4.05 2 0.03

6/13/13 LS02 0.024 2 0.003 0.014 2 0.001 0.008 0.02 2 0.01 31.1 2 0.1 3.96 2 0.01

6/13/13 LS03 0.028 2 0.001 0.011 2 0.006 0.006 0.03 2 0.01 31.7 2 0.4 4.10 2 0.02

6/13/13 OS01 0.025 2 0.002 <0.007 2 0.002 0.01 2 0.00 29.6 2 0.3 4.06 2 0.12

6/13/13 OS02 0.030 2 0.003 <0.007 2 0.002 0.01 2 0.00 33.2 2 0.6 4.03 2 0.01

6/13/13 OS03 0.027 2 0.002 0.011 2 0.007 0.005 0.01 2 0.00 31.0 2 0.5 4.15 2 0.06

6/13/13 TR01 0.029 2 0.001 0.030 2 0.024 0.000 0.03 2 0.01 32.7 2 0.2 4.03 2 0.01

6/13/13 TR01 0.039 2 0.001 0.021 2 0.001 0.000 0.06 2 0.04 33.0 2 0.1 4.03 2 0.02

6/13/13 TR02 0.027 2 0.000 0.026 2 0.015 0.000 0.05 2 0.00 31.8 2 0.4 4.08 2 0.11

6/13/13 TR02 0.034 2 0.004 0.029 2 0.001 0.000 0.06 2 0.01 32.1 2 0.6 3.98 2 0.05

6/13/13 TR03 0.029 2 0.002 0.009 2 0.006 0.000 0.03 2 0.01 31.9 2 0.6 4.07 2 0.01

6/13/13 TR03 0.035 2 0.006 0.021 2 0.005 0.000 0.02 2 0.01 31.8 2 0.3 4.04 2 0.06

7/3/13 LN01 0.113 2 0.000 0.046 2 0.001 0.037 0.01 2 0.00 38.4 2 0.6 5.73 2 0.01

7/3/13 LN02 0.072 2 0.002 0.051 2 0.014 0.000 0.01 2 0.00 35.0 2 4.6 5.42 2 0.02

7/3/13 LN03 0.020 2 0.006 0.101 2 0.035 0.092 0.01 2 0.00 39.1 2 0.3 6.47 2 0.13
65

SRP as P Ammonia as N Nitrate as N Silica as SiO2 DOC as C

mg L−1 mg L−1 mg L−1 mg L−1 mg L−1 mg L−1

Date SiteName Mean n S.D. Mean n S.D.

Unionized Mean n S.D. Mean n S.D. Mean n S.D.

7/3/13 ON01 0.064 2 0.001 0.017 2 0.001 0.014 0.01 2 0.00 37.4 2 0.2 5.86 2 0.03

7/3/13 ON02 0.082 2 0.001 0.087 2 0.008 0.028 0.02 2 0.02 37.8 2 0.0 5.20 2 0.07

7/3/13 ON03 0.055 2 0.007 0.046 2 0.008 0.038 0.01 2 0.00 37.3 2 0.1 5.20 2 0.06

7/3/13 LC01 0.125 2 0.004 0.058 2 0.018 0.038 0.01 2 0.00 39.7 2 0.5 5.06 2 0.05

7/3/13 LC02 0.045 2 0.001 0.029 2 0.005 0.023 0.01 2 0.00 37.6 2 0.2 4.94 2 0.01

7/3/13 LC03 0.101 2 0.001 0.034 2 0.011 0.024 0.01 2 0.00 38.2 2 1.6 5.18 2 0.02

7/3/13 OC01 0.075 2 0.005 0.048 2 0.004 0.041 0.02 2 0.01 38.4 2 0.3 4.98 2 0.01

7/3/13 OC02 0.057 2 0.003 0.035 2 0.008 0.030 0.01 2 0.01 37.6 2 0.0 4.98 2 0.15

7/3/13 OC03 0.062 2 0.004 0.021 2 0.021 0.019 0.01 2 0.00 37.7 2 0.3 4.84 2 0.05

7/3/13 LS01 0.044 2 0.000 0.763 2 0.040 0.647 0.01 2 0.00 38.0 2 0.1 6.04 2 0.04

7/3/13 LS02 0.055 2 0.010 0.969 2 0.174 0.660 0.01 2 0.00 38.9 2 0.4 6.88 2 0.01

7/3/13 LS03 0.075 2 0.005 0.048 2 0.004 0.020 0.02 2 0.01 38.4 2 0.3 6.42 2 0.14

7/3/13 OS01 0.078 2 0.001 0.433 2 0.046 0.280 0.01 2 0.00 39.7 2 0.1 5.93 2 0.21

7/3/13 OS02 0.071 2 0.001 0.843 2 0.057 0.367 0.01 2 0.00 38.1 2 0.3 6.34 2 0.12

7/3/13 OS03 0.103 2 0.018 0.530 2 0.228 0.410 0.01 2 0.00 39.7 2 0.1 5.53 2 0.01

7/3/13 TR01 0.107 2 0.018 0.512 2 0.248 0.386 0.01 2 0.00 38.1 2 0.1 5.22 2 0.05

7/3/13 TR01 0.085 2 0.002 0.216 2 0.015 0.139 0.02 2 0.00 39.4 2 0.5 5.37 2 0.08

7/3/13 TR02 0.088 2 0.000 0.748 2 0.069 0.585 0.01 2 0.00 39.2 2 0.4 5.44 2 0.09

7/3/13 TR02 0.090 2 0.001 0.299 2 0.013 0.155 0.02 2 0.00 39.2 2 0.1 5.31 2 0.01

7/3/13 TR03 0.071 2 0.004 1.031 2 0.227 0.764 0.01 2 0.00 38.3 2 1.5 5.52 2 0.05

7/3/13 TR03 0.092 2 0.003 0.265 2 0.017 0.108 0.01 2 0.00 38.2 2 1.1 5.38 2 0.01
66

Table 6. 2013 dissolved (0.2-micrometer filtered) trace-element benthic fluxes from profiler deployments.

[Fe, iron; Mn, manganese; As, arsenic; V, vanadium; Co, cobalt; Ni, nickel; Cu, copper; Zn, zinc; Cd, cadmium; Pb, lead; μg m−2 d−1, microgram per square 

meter per day; nd, nondetectable benthic flux due to concentrations from profiler samples that were consistently below detection limits for that solute; na, data 

not available from that time period; blank cells, values that are not available; S.D., standard deviation for the specified n replicates. Slightly darkened rows in the 

table are provided to help the reader distinguish results between sampling dates]

Fe Mn As V Co

μg m−2 d−1 μg m−2 d−1 μg m−2 d−1 μg m−2 d−1 μg m−2 d−1

Date Site Mean n S.D. Mean n S.D. Mean n S.D. Mean n S.D. Mean n S.D.

4/30/13 MDN_A 4,875 2 61 823 2 117 33 2 7 −27 2 21 0.67 2 0.09

4/30/13 MDN_B 7,560 2 694 1,074 2 12 33 2 15 −32 2 8 0.51 2 0.24

4/30/13 MDN_C 5,105 2 47 596 2 186 25 2 13 −34 2 12 0.29 2 0.09

4/30/13 TRRecon 6,479 2 1,064 1,227 2 30 131 2 34 −54 2 16 0.82 2 0.03

5/23/13 LN01 16,756 2 10,492 2,602 2 598 209 2 100 −37 2 1 0.42 2 0.08

5/23/13 LN02 5,734 2 407 1,711 2 727 44 2 35 −42 2 16 0.49 2 0.61

5/23/13 LN03 2,662 2 251 1,407 2 163 74 2 52 −30 2 32 0.47 2 0.22

5/23/13 ON01 9,982 2 785 915 2 226 −14 2 12 −23 2 5 −0.07 2 1.15

5/23/13 ON02 6,915 2 1,336 490 2 69 −11 2 13 −20 2 0 −0.15 2 0.22

5/23/13 ON03 4,233 2 1,843 598 2 312 5 2 12 −45 2 6 −0.05 2 0.02

5/23/13 LC01 4,342 2 1,472 2,462 2 321 25 2 10 −55 2 41 2.29 2 0.11

5/23/13 LC02 53 2 4 1,419 2 742 11 2 6 −15 2 4 1.84 2 0.14

5/23/13 LC03 5,699 2 339 1,617 2 1,231 18 2 0 −35 2 10 0.63 2 0.02

5/23/13 OC01 4,710 2 3,042 1,271 2 103 −4 2 20 −18 2 22 −0.02 2 0.47

5/23/13 OC02 638 2 1,010 378 2 198 18 2 25 −8 2 11 −0.03 2 0.00

5/23/13 OC03 na na na na na

5/23/13 LS01 738 2 208 104 2 85 −6 2 14 −33 2 36 0.06 2 0.04

5/23/13 LS02 2,584 2 1,630 143 2 194 −5 2 1 −14 2 0 0.05 2 0.10

5/23/13 LS03 251 2 616 116 2 100 −77 2 27 −3 2 4 −0.06 2 0.06

5/23/13 OS01 4,892 2 193 606 2 167 −4 2 2 −30 2 7 0.16 2 0.07

5/23/13 OS02 995 2 616 1,119 2 305 −96 2 76 −4 2 0 0.36 2 0.08

5/23/13 OS03 11,188 2 7,580 951 2 675 −12 2 6 −19 2 23 0.49 2 0.25

5/23/13 TR01 13,569 2 4,091 2,811 2 471 26 2 8 −74 2 25 1.55 2 0.43

5/23/13 TR02 na na na na na

5/23/13 TR03 6,467 2 340 686 2 372 56 2 71 −48 2 1 0.31 2 0.06

6/13/13 LN01 1,784 2 1,397 506 2 47 58 2 15 −45 2 34 0.20 2 0.12

6/13/13 LN02 2,285 2 441 746 2 400 14 2 1 −34 2 20 0.18 2 0.07
67

Fe Mn As V Co

μg m−2 d−1 μg m−2 d−1 μg m−2 d−1 μg m−2 d−1 μg m−2 d−1

Date Site Mean n S.D. Mean n S.D. Mean n S.D. Mean n S.D. Mean n S.D.

6/13/13 LN03 2,351 2 606 1,767 2 1,171 253 2 65 −102 2 8 3.52 2 2.45

6/13/13 ON01 1,782 2 1,286 598 2 244 −19 2 2 −33 2 29 0.14 2 0.08

6/13/13 ON02 462 2 238 1,194 2 38 49 2 0 −64 2 6 0.30 2 0.06

6/13/13 ON03 1,302 2 567 441 2 192 74 2 80 −13 2 9 0.21 2 0.08

6/13/13 LC01 3,033 2 1,976 1,724 2 329 23 2 0 −62 2 5 1.10 2 0.30

6/13/13 LC02 20 2 9 1,801 2 142 28 2 3 −25 2 27 2.97 2 0.18

6/13/13 LC03 1,975 2 583 389 2 151 13 2 4 −65 2 13 0.07 2 0.05

6/13/13 OC01 2,810 2 783 976 2 261 5 2 14 −72 2 56 0.14 2 0.00

6/13/13 OC02 5,176 2 3,441 2,636 2 536 53 2 18 −64 2 49 0.43 2 0.21

6/13/13 OC03 5,540 2 3,478 1,511 2 345 45 2 25 −39 2 34 0.19 2 0.07

6/13/13 LS01 563 2 347 97 2 23 −26 2 1 −79 2 17 −0.04 2 0.02

6/13/13 LS02 277 2 43 63 2 69 −117 2 2 −70 2 19 −0.02 2 0.03

6/13/13 LS03 240 2 219 41 2 25 −54 2 4 −98 2 121 0.07 2 0.02

6/13/13 OS01 792 2 187 598 2 215 −11 2 10 −101 2 8 0.12 2 0.03

6/13/13 OS02 3,350 2 139 1,390 2 640 −2 2 7 −63 2 3 0.00 2 0.12

6/13/13 OS03 3,447 2 25 263 2 226 −47 2 34 −13 2 6 0.54 2 0.04

6/13/13 TR01 3,398 2 479 1,058 2 495 4 2 7 −48 2 13 0.03 2 0.04

6/13/13 TR02 1,539 2 834 1,603 2 172 121 2 26 −62 2 2 0.34 2 0.31

6/13/13 TR03 4,314 2 2,571 2,730 2 517 82 2 65 −71 2 8 0.39 2 0.28

7/3/13 LN01 1,351 2 253 341 2 215 39 2 1 −70 2 37 0.00 2 0.09

7/3/13 LN02 1,295 2 715 454 2 9 −9 2 4 −99 2 68 0.03 2 0.03

7/3/13 LN03 7,760 2 1,919 2,011 2 517 123 2 82 −79 2 74 1.26 2 0.05

7/3/13 ON01 2,203 2 1,298 430 2 327 −22 2 18 −34 2 39 0.10 2 0.02

7/3/13 ON02 1,982 2 978 539 2 299 −5 2 2 −23 2 29 0.07 2 0.07

7/3/13 ON03 929 2 277 367 2 33 −2 2 7 −72 2 25 −0.09 2 0.02

7/3/13 LC01 4,879 2 3,757 1,327 2 118 4 2 8 −39 2 40 0.22 2 0.30

7/3/13 LC02 908 2 143 5,455 2 1,211 53 2 23 −37 2 24 4.40 2 0.21

7/3/13 LC03 6,652 2 2,681 1,897 2 474 −15 2 10 −106 2 5 0.90 2 0.45

7/3/13 OC01 866 2 371 996 2 136 0 2 3 −119 2 5 −0.27 2 0.03

7/3/13 OC02 3,456 2 424 1,329 2 71 4 2 2 −179 2 98 0.10 2 0.18

7/3/13 OC03 2,188 2 848 1,059 2 198 51 2 11 −142 2 39 −0.10 2 0.09

7/3/13 LS01 287 2 164 187 2 68 −14 2 7 −12 2 15 0.00 2 0.04
68

Fe Mn As V Co

μg m−2 d−1 μg m−2 d−1 μg m−2 d−1 μg m−2 d−1 μg m−2 d−1

Date Site Mean n S.D. Mean n S.D. Mean n S.D. Mean n S.D. Mean n S.D.

7/3/13 LS02 179 2 20 23 2 8 −76 2 16 −28 2 20 0.04 2 0.01

7/3/13 LS03 135 2 26 193 2 107 −12 2 1 −6 2 3 0.03 2 0.10

7/3/13 OS01 1,410 2 1,189 867 2 471 −16 2 16 −69 2 76 −0.04 2 0.05

7/3/13 OS02 165 2 82 155 2 12 −14 2 3 −101 2 23 −0.09 2 0.06

7/3/13 OS03 3,101 2 691 773 2 110 −32 2 1 −27 2 32 0.33 2 0.05

7/3/13 TR01 5,651 2 879 1,219 2 24 68 2 1 −95 2 2 0.00 2 0.00

7/3/13 TR02 2,433 2 554 149 2 135 −11 2 4 −98 2 34 −0.08 2 0.01

7/3/13 TR03 3,953 2 502 1,241 2 267 53 2 10 −81 2 8 0.12 2 0.06
69

Ni Cu Zn Cd Pb

μg m−2 d−1 μg m−2 d−1 μg m−2 d−1 μg m−2 d−1 μg m−2 d−1

Date Site Mean n S.D. Mean n S.D. Mean n S.D. Mean n S.D. Mean n S.D.

4/30/13 MDN_A −0.45 2 0.23 −1.24 2 0.50 0.21 2 1.13 0.00 2 0.00 −0.02 2 0.05

4/30/13 MDN_B −0.44 2 0.43 −0.77 2 0.09 0.23 2 0.52 0.00 2 0.00 −0.02 2 0.03

4/30/13 MDN_C −0.56 2 1.10 −12.50 2 15.67 −0.02 2 0.13 0.00 2 0.00 0.01 2 0.02

4/30/13 TRRecon 2.14 2 0.44 −1.85 2 1.47 4.15 2 2.46 0.00 2 0.00 −0.01 2 0.03

5/23/13 LN01 0.85 2 0.26 −1.56 2 0.45 −4.08 2 1.38 0.01 2 0.00 −1.02 2 0.04

5/23/13 LN02 1.09 2 1.27 −3.90 2 2.15 −3.40 2 0.98 0.01 2 0.00 −0.51 2 0.66

5/23/13 LN03 0.94 2 0.80 −0.63 2 0.02 −1.81 2 0.16 0.01 2 0.00 0.01 2 0.10

5/23/13 ON01 −0.28 2 0.41 −0.79 2 0.40 −0.74 2 0.44 0.01 2 0.00 −0.13 2 0.18

5/23/13 ON02 −0.15 2 0.24 −1.13 2 0.45 −0.97 2 1.36 0.01 2 0.00 0.00 2 0.03

5/23/13 ON03 0.22 2 0.38 −1.69 2 0.19 0.00 2 0.00 0.01 2 0.00 0.00 2 0.00

5/23/13 LC01 2.16 2 1.19 −1.53 2 0.51 3.75 2 0.89 0.01 2 0.00 −0.05 2 0.09

5/23/13 LC02 2.91 2 0.01 −0.35 2 0.11 4.27 2 0.68 0.01 2 0.00 0.11 2 0.02

5/23/13 LC03 1.18 2 0.50 −1.00 2 0.06 3.39 2 0.00 0.01 2 0.00 0.04 2 0.00

5/23/13 OC01 0.16 2 0.60 −1.72 2 0.71 0.00 2 0.00 0.01 2 0.00 0.00 2 0.03

5/23/13 OC02 0.16 2 0.19 −0.51 2 0.73 −1.26 2 1.78 0.01 2 0.00 0.09 2 0.12

5/23/13 OC03 na na na na na na

5/23/13 LS01 0.97 2 2.53 −1.10 2 1.16 −6.62 2 7.17 0.01 2 0.00 −0.08 2 0.13

5/23/13 LS02 −0.63 2 0.36 −0.49 2 0.25 −3.74 2 5.29 0.01 1 −0.01 1

5/23/13 LS03 −1.15 2 0.67 −0.55 2 0.14 0.00 2 0.00 0.01 1 0.01 2 0.04

5/23/13 OS01 0.60 2 0.97 −1.40 2 0.63 −24.93 2 10.14 0.01 2 0.00 0.03 2 0.04

5/23/13 OS02 0.68 2 0.41 −0.34 2 0.30 −2.69 2 0.49 0.01 2 0.00 0.00 2 0.04

5/23/13 OS03 0.39 2 0.05 −0.94 2 0.88 −1.08 2 1.52 0.01 2 0.00 0.00 2 0.00

5/23/13 TR01 0.45 2 0.10 −1.97 2 0.09 1.68 2 1.72 0.01 2 0.00 0.02 2 0.00

5/23/13 TR02 na na na na na na

5/23/13 TR03 0.58 2 0.10 −2.26 2 0.99 5.92 2 3.64 0.01 2 0.00 0.00 2 0.01

6/13/13 LN01 0.25 2 0.06 −0.56 2 0.24 nd 2 0.00 2 0.00 0.01 2 0.03

6/13/13 LN02 −0.36 2 0.55 −0.41 2 0.08 nd 2 0.00 2 0.00 −0.01 2 0.01

6/13/13 LN03 2.07 2 0.71 −3.50 2 0.29 nd 2 0.00 2 0.00 0.01 2 0.02

6/13/13 ON01 0.32 2 0.09 −0.51 2 0.66 nd 2 0.00 2 0.00 0.00 2 0.01

6/13/13 ON02 0.46 2 0.33 −1.09 2 0.42 nd 2 0.00 2 0.00 0.01 2 0.01

6/13/13 ON03 0.17 2 0.02 −0.17 2 0.04 nd 2 0.00 2 0.00 0.00 2 0.00
70

Ni Cu Zn Cd Pb

μg m−2 d−1 μg m−2 d−1 μg m−2 d−1 μg m−2 d−1 μg m−2 d−1

Date Site Mean n S.D. Mean n S.D. Mean n S.D. Mean n S.D. Mean n S.D.

6/13/13 LC01 1.24 2 0.59 −1.53 2 0.24 nd 2 0.00 2 0.00 −0.01 2 0.00

6/13/13 LC02 2.39 2 0.23 −0.33 2 0.13 nd 2 0.00 2 0.00 0.00 2 0.00

6/13/13 LC03 0.28 2 0.02 −0.64 2 0.22 nd 2 0.00 2 0.00 0.01 2 0.01

6/13/13 OC01 0.13 2 0.03 −0.49 2 0.16 nd 2 0.00 2 0.00 −0.03 2 0.06

6/13/13 OC02 0.67 2 0.13 −0.64 2 0.10 nd 2 0.00 2 0.00 0.00 2 0.00

6/13/13 OC03 0.43 2 0.18 −0.39 2 0.22 nd 2 0.00 2 0.00 0.00 2 0.00

6/13/13 LS01 0.16 2 0.00 −0.67 2 0.19 nd 2 0.00 2 0.00 0.00 2 0.00

6/13/13 LS02 0.13 2 0.03 −0.96 2 0.80 nd 2 0.00 2 0.00 0.00 2 0.00

6/13/13 LS03 −0.16 2 0.09 −1.04 2 0.09 nd 2 0.00 2 0.00 −0.02 2 0.03

6/13/13 OS01 −0.14 2 0.05 −2.49 2 1.89 nd 2 0.00 2 0.00 0.01 2 0.01

6/13/13 OS02 0.44 2 0.08 −3.73 2 2.40 nd 2 0.00 2 0.00 0.00 2 0.01

6/13/13 OS03 0.10 2 0.14 −0.20 2 0.02 nd 2 0.00 2 0.00 0.00 2 0.01

6/13/13 TR01 0.12 2 0.25 −0.49 2 0.02 nd 2 0.00 2 0.00 0.00 2 0.00

6/13/13 TR02 0.58 2 0.03 −0.79 2 0.31 nd 2 0.00 2 0.00 0.01 2 0.01

6/13/13 TR03 0.77 2 0.57 −1.10 2 0.46 nd 2 0.00 2 0.00 0.00 2 0.00

7/3/13 LN01 −0.09 2 0.12 −5.45 2 3.89 −29.12 2 40.63 −0.02 2 0.01 −0.28 2 0.00

7/3/13 LN02 0.06 2 0.23 −1.77 2 0.26 3.71 2 5.48 0.00 2 0.00 −0.07 2 0.04

7/3/13 LN03 3.36 2 1.96 −1.52 2 0.66 15.41 2 13.20 0.03 2 0.04 0.01 2 0.06

7/3/13 ON01 0.40 2 0.31 −4.00 2 0.45 −6.82 2 8.94 −0.01 2 0.02 −0.21 2 0.25

7/3/13 ON02 −1.01 2 0.72 −7.65 2 7.21 −20.47 2 10.76 −0.01 2 0.01 0.27 2 0.08

7/3/13 ON03 −1.32 2 1.78 −7.94 2 5.72 −22.53 2 19.16 −0.01 2 0.02 −0.28 2 0.32

7/3/13 LC01 0.43 2 0.03 −1.69 2 1.65 −1.10 2 0.93 0.00 2 0.00 0.02 2 0.09

7/3/13 LC02 3.35 2 2.13 −3.03 2 0.46 5.52 2 2.20 0.01 2 0.01 0.10 2 0.06

7/3/13 LC03 1.41 2 0.68 −3.25 2 0.02 −1.87 1 0.00 2 0.00 0.23 2 0.08

7/3/13 OC01 −0.20 2 0.15 −3.62 2 1.26 5.10 1 0.00 2 0.00 0.03 2 0.02

7/3/13 OC02 −2.15 2 1.85 −28.13 2 36.65 7.34 2 9.55 0.01 2 0.02 0.09 2 0.19

7/3/13 OC03 0.41 2 0.76 −4.35 2 4.51 −2.61 2 1.01 0.00 2 0.00 −0.03 2 0.21

7/3/13 LS01 −1.15 2 1.03 −0.87 2 0.09 0.94 1 0.00 2 0.00 0.02 2 0.01

7/3/13 LS02 −0.23 2 0.07 −0.47 2 0.08 2.06 1 0.00 2 0.00 −0.01 2 0.01

7/3/13 LS03 0.41 2 0.47 −1.12 2 0.43 −3.61 2 1.46 0.00 2 0.00 0.02 2 0.17

7/3/13 OS01 0.37 2 0.02 −2.92 2 0.53 −1.13 2 0.87 0.00 2 0.00 0.93 2 1.16

7/3/13 OS02 −0.37 2 0.53 −1.02 2 0.10 nd 2 0.00 2 0.00 −0.05 2 0.02
71

Ni Cu Zn Cd Pb

μg m−2 d−1 μg m−2 d−1 μg m−2 d−1 μg m−2 d−1 μg m−2 d−1

Date Site Mean n S.D. Mean n S.D. Mean n S.D. Mean n S.D. Mean n S.D.

7/3/13 OS03 0.29 2 0.10 −1.08 2 1.44 nd 2 −0.15 2 0.21 −0.15 2 0.11

7/3/13 TR01 0.62 2 1.09 −1.68 2 0.55 3.33 2 1.89 0.00 2 0.00 −0.07 2 0.01

7/3/13 TR02 0.17 2 0.09 −0.72 2 0.91 10.93 2 4.95 0.00 2 0.00 −0.02 2 0.18

7/3/13 TR03 1.13 2 0.14 −1.09 2 0.61 1.66 2 1.04 0.00 2 0.00 −0.06 2 0.02
72

Table 7. 2013 dissolved (0.2-micrometer filtered) trace-element concentrations in the water column Upper Klamath Lake, Oregon.

[Fe, iron; Mn, manganese; As, arsenic; V, vanadium; Co, cobalt; Ni, nickel; Cu, copper; Zn, zinc; Cd, cadmium; Pb, lead; μg L−1, microgram per liter; blank 

cells, values that are not available; S.D., standard deviation for the specified n replicates. Slightly darkened rows in the table for June 13, 2013, are provided to 

help the reader distinguish results between sampling dates]

Fe Mn As V Co

μg L−1 μg L−1 μg L−1 μg L−1 μg L−1

Date Site Depth Mean n S.D. Mean n S.D. Mean n S.D. Mean n S.D. Mean n S.D.

4/30/13 MDN surface 29.3 2 0.1 1.19 2 0.29 3.72 2 0.24 10.74 2 0.15 0.086 2 0.011

4/30/13 Recon TR surface 16.8 2 0.1 0.96 2 0.48 3.62 2 0.03 9.69 2 0.00 0.031 2 0.006

4/30/13 Recon TR bottom 28.8 2 15.4 1.60 2 0.32 3.65 2 0.03 9.65 2 0.14 0.035 2 0.004

5/23/13 LC01 surface 15.3 2 0.5 7.27 2 7.08 8.05 2 0.42 11.01 2 0.08 0.045 2 0.013

5/23/13 LC02 surface 22.6 2 7.2 0.86 2 0.33 6.75 2 0.01 11.06 2 0.10 0.033 2 0.002

5/23/13 LC03 surface 15.9 2 1.2 2.47 2 0.43 4.84 2 0.29 13.60 2 0.19 0.053 2 0.015

5/23/13 LN01 surface 13.4 2 0.1 3.53 2 0.24 10.35 2 0.20 7.55 2 0.12 0.035 2 0.000

5/23/13 LN02 surface 7.9 2 0.3 0.80 2 0.14 4.97 2 0.15 8.18 2 0.29 0.038 2 0.001

5/23/13 LN03 surface 7.6 2 0.3 0.61 2 0.04 4.85 2 0.04 8.58 2 0.15 0.038 2 0.000

5/23/13 LS01 surface 9.7 2 3.5 0.31 2 0.16 6.21 2 0.15 9.09 2 0.04 0.026 2 0.000

5/23/13 LS02 surface 7.4 2 0.9 0.27 2 0.02 6.22 2 0.03 9.00 2 0.03 0.025 2 0.002

5/23/13 LS03 surface 6.7 2 0.4 0.34 2 0.02 6.48 2 0.00 9.47 2 0.07 0.029 2 0.002

5/23/13 OC01 surface 6.5 2 1.0 0.57 2 0.14 5.06 2 0.00 7.97 2 0.03 0.028 2 0.005

5/23/13 OC02 surface 15.2 2 1.0 1.36 2 0.09 8.09 2 0.16 11.92 2 0.20 0.053 2 0.000

5/23/13 OC03 surface 27.7 2 19.6 1.85 2 1.92 6.00 2 0.10 11.19 2 0.11 0.042 2 0.005

5/23/13 ON01 surface 14.5 2 2.1 1.24 2 0.06 4.80 2 0.04 8.34 2 0.13 0.032 2 0.000

5/23/13 ON02 surface 6.7 2 0.8 0.55 2 0.04 4.82 2 0.01 7.80 2 0.00 0.030 2 0.001

5/23/13 ON03 surface 7.8 2 0.0 0.69 2 0.00 4.76 2 0.03 7.73 2 0.07 0.032 2 0.001

5/23/13 OS01 surface 5.5 2 0.2 0.25 2 0.17 4.91 2 0.03 7.68 2 0.03 0.022 2 0.002

5/23/13 OS02 surface 6.2 2 0.5 0.32 2 0.18 4.95 2 0.01 7.76 2 0.10 0.024 2 0.002

5/23/13 OS03 surface 6.7 2 0.6 0.31 2 0.10 4.83 2 0.04 7.66 2 0.27 0.025 2 0.008

5/23/13 TR01 surface 5.4 2 0.5 0.14 2 0.01 5.13 2 0.04 7.86 2 0.06 0.022 2 0.001

5/23/13 TR01 bottom 6.9 2 0.7 0.20 2 0.03 5.56 2 0.05 7.98 2 0.15 0.023 2 0.001

5/23/13 TR02 surface 5.8 2 0.3 0.23 2 0.10 4.99 2 0.17 7.86 2 0.17 0.027 2 0.002

5/23/13 TR02 bottom 5.6 2 0.2 0.20 2 0.02 5.03 2 0.06 7.92 2 0.11 0.024 2 0.000

5/23/13 TR03 surface 6.3 2 0.3 0.35 2 0.30 5.02 2 0.04 7.81 2 0.17 0.023 2 0.004

5/23/13 TR03 bottom 6.2 2 0.2 0.37 2 0.11 5.06 2 0.05 8.01 2 0.10 0.029 2 0.003
73

Fe Mn As V Co

μg L−1 μg L−1 μg L−1 μg L−1 μg L−1

Date Site Depth Mean n S.D. Mean n S.D. Mean n S.D. Mean n S.D. Mean n S.D.

6/13/13 LC01 surface 8.3 2 3.2 0.37 2 0.32 7.85 2 0.36 10.43 2 0.23 0.033 2 0.001

6/13/13 LC02 surface 6.3 2 0.2 0.22 2 0.05 7.65 2 0.04 9.83 2 0.09 0.038 2 0.001

6/13/13 LC03 surface 7.6 2 2.2 0.45 2 0.35 6.86 2 0.07 10.89 2 0.08 0.050 2 0.005

6/13/13 LN01 surface 8.3 2 1.9 0.27 2 0.32 6.41 2 0.09 8.64 2 0.25 0.027 2 0.002

6/13/13 LN02 surface 6.3 2 0.2 0.22 2 0.05 7.65 2 0.04 9.83 2 0.09 0.038 2 0.001

6/13/13 LN03 surface 7.3 2 1.0 0.74 2 0.32 7.36 2 0.06 11.32 2 0.06 0.049 2 0.004

6/13/13 LS01 surface 7.6 2 0.1 0.44 2 0.35 7.30 2 0.03 9.59 2 0.01 0.042 2 0.004

6/13/13 LS02 surface 8.0 2 1.0 0.37 2 0.05 7.37 2 0.00 9.52 2 0.03 0.038 2 0.004

6/13/13 LS03 surface 8.4 2 2.1 0.48 2 0.24 7.41 2 0.07 9.79 2 0.03 0.039 2 0.001

6/13/13 OC01 surface 9.3 2 1.1 0.63 2 0.38 7.98 2 0.03 8.90 2 0.03 0.034 2 0.005

6/13/13 OC02 surface 7.7 2 0.3 0.26 2 0.01 7.60 2 0.03 9.19 2 0.01 0.037 2 0.001

6/13/13 OC03 surface 8.5 2 0.6 0.61 2 0.04 7.63 2 0.02 9.16 2 0.00 0.037 2 0.003

6/13/13 ON01 surface 10.2 2 4.1 0.66 2 0.47 6.96 2 0.04 9.90 2 0.05 0.036 2 0.002

6/13/13 ON02 surface 8.4 2 0.0 0.61 2 0.01 6.88 2 0.02 9.71 2 0.02 0.038 2 0.005

6/13/13 ON03 surface 7.2 2 0.2 0.42 2 0.29 7.04 2 0.04 9.91 2 0.13 0.039 2 0.003

6/13/13 OS01 surface 7.6 2 0.9 0.39 2 0.15 7.47 2 0.29 9.54 2 0.18 0.041 2 0.003

6/13/13 OS02 surface 6.8 2 0.2 0.39 2 0.01 6.80 2 0.05 9.33 2 0.01 0.043 2 0.001

6/13/13 OS03 surface 7.1 2 0.3 0.43 2 0.05 7.15 2 0.02 9.64 2 0.21 0.047 2 0.002

6/13/13 TR01 surface 6.6 2 0.0 0.13 2 0.00 7.02 2 0.05 9.31 2 0.12 0.032 2 0.001

6/13/13 TR01 bottom 6.6 2 0.2 0.23 2 0.01 6.94 2 0.02 9.16 2 0.11 0.032 2 0.000

6/13/13 TR02 surface 7.0 2 0.2 0.34 2 0.21 7.01 2 0.07 9.19 2 0.18 0.035 2 0.003

6/13/13 TR02 bottom 7.0 2 0.6 0.20 2 0.02 6.99 2 0.04 9.10 2 0.07 0.033 2 0.002

6/13/13 TR03 surface 8.0 2 0.1 0.67 2 0.03 7.24 2 0.01 9.76 2 0.02 0.047 2 0.001

6/13/13 TR03 bottom 7.0 2 0.7 0.52 2 0.10 7.33 2 0.02 9.90 2 0.07 0.044 2 0.008

7/3/13 LC01 surface 10.1 2 0.4 0.47 2 0.01 9.75 2 0.06 10.29 2 0.10 0.056 2 0.001

7/3/13 LC02 surface 10.6 2 0.9 0.53 2 0.22 8.30 2 0.04 10.41 2 0.01 0.054 2 0.004

7/3/13 LC03 surface 11.8 2 0.5 0.98 2 0.44 8.63 2 0.06 11.58 2 0.10 0.087 2 0.003

7/3/13 LN01 surface 6.7 2 0.2 0.99 2 0.10 12.58 2 0.05 11.19 2 0.04 0.069 2 0.004

7/3/13 LN02 surface 6.0 2 0.2 1.07 2 0.20 9.41 2 0.01 11.37 2 0.07 0.068 2 0.000

7/3/13 LN03 surface 5.6 2 0.4 0.66 2 0.15 9.43 2 0.08 11.51 2 0.11 0.067 2 0.002

7/3/13 LS01 surface 6.1 2 0.1 0.62 2 0.16 9.16 2 0.06 10.58 2 0.02 0.064 2 0.010
74

Fe Mn As V Co

μg L−1 μg L−1 μg L−1 μg L−1 μg L−1

Date Site Depth Mean n S.D. Mean n S.D. Mean n S.D. Mean n S.D. Mean n S.D.

7/3/13 LS02 surface 5.8 2 0.2 0.43 2 0.03 9.16 2 0.09 10.56 2 0.03 0.058 2 0.001

7/3/13 LS03 surface 9.1 2 3.2 1.66 2 1.54 9.28 2 0.12 10.51 2 0.16 0.058 2 0.005

7/3/13 OC01 surface 11.1 2 0.1 0.57 2 0.48 9.18 2 0.12 11.11 2 0.35 0.065 2 0.010

7/3/13 OC02 surface 13.7 2 4.7 0.78 2 0.60 8.39 2 0.12 10.46 2 0.08 0.064 2 0.003

7/3/13 OC03 surface 15.4 2 0.1 1.07 2 0.06 8.51 2 0.15 10.34 2 0.20 0.066 2 0.003

7/3/13 ON01 surface 6.5 2 0.5 0.78 2 0.16 9.30 2 0.00 11.40 2 0.04 0.064 2 0.007

7/3/13 ON02 surface 6.4 2 1.4 0.80 2 0.36 9.54 2 0.01 11.17 2 0.02 0.058 2 0.002

7/3/13 ON03 surface 5.7 2 0.1 0.84 2 0.09 9.23 2 0.01 11.30 2 0.04 0.063 2 0.004

7/3/13 OS01 surface 5.0 2 0.2 0.28 2 0.17 9.73 2 0.01 11.29 2 0.01 0.060 2 0.000

7/3/13 OS02 surface 6.1 2 0.9 1.26 2 0.56 9.24 2 0.03 10.38 2 0.02 0.052 2 0.008

7/3/13 OS03 surface 7.4 2 2.3 1.35 2 1.18 9.58 2 0.10 11.26 2 0.11 0.079 2 0.023

7/3/13 TR01 surface 5.9 2 0.4 0.44 2 0.02 10.10 2 0.00 11.51 2 0.06 0.061 2 0.005

7/3/13 TR01 bottom 7.1 2 0.7 0.71 2 0.15 10.04 2 0.10 11.23 2 0.01 0.066 2 0.014

7/3/13 TR02 surface 4.5 2 0.2 0.87 2 0.00 9.95 2 0.03 11.29 2 0.12 0.073 2 0.001

7/3/13 TR02 bottom 7.1 2 1.8 1.29 2 0.45 10.09 2 0.18 11.68 2 0.42 0.076 2 0.001

7/3/13 TR03 surface 5.2 2 0.3 1.20 2 0.23 9.72 2 0.26 10.88 2 0.33 0.071 2 0.002

7/3/13 TR03 bottom 5.6 2 1.0 1.06 2 0.07 9.92 2 0.03 11.13 2 0.01 0.071 2 0.006
75

Ni Cu Zn Cd Pb

μg L−1 μg L−1 μg L−1 μg L−1 μg L−1

Date Site Depth Mean n S.D. Mean n S.D. Mean n S.D. Mean n S.D. Mean n S.D.

4/30/13 MDN surface 0.89 2 0.37 1.05 2 0.12 1.29 2 0.09 <0.002 2 <0.002 2

4/30/13 Recon TR surface 0.46 2 0.00 0.68 2 0.03 0.57 2 0.11 <0.002 2 <0.002 2

4/30/13 Recon TR bottom 0.45 2 0.02 0.66 2 0.04 0.36 2 0.07 <0.002 2 0.008 1

5/23/13 LC01 surface 0.12 2 0.01 0.22 2 0.04 0.54 2 0.16 <0.002 2 <0.002 2

5/23/13 LC02 surface 0.12 2 0.01 0.27 2 0.10 0.22 2 0.14 <0.002 2 0.006 1

5/23/13 LC03 surface 0.16 2 0.01 0.38 2 0.01 0.84 2 0.32 0.004 2 0.001 0.004 1

5/23/13 LN01 surface <0.05 2 0.02 2 0.01 0.18 1 <0.002 2 0.002 1

5/23/13 LN02 surface <0.05 2 0.05 2 0.02 0.29 2 0.04 <0.002 2 0.004 2 0.002

5/23/13 LN03 surface <0.05 2 0.05 2 0.00 0.52 1 <0.002 2 0.004 1

5/23/13 LS01 surface <0.05 2 0.16 2 0.01 1.25 1 <0.002 2 0.004 2 0.000

5/23/13 LS02 surface <0.05 2 0.16 2 0.00 0.10 1 <0.002 2 0.004 2 0.002

5/23/13 LS03 surface <0.05 2 0.17 2 0.01 <0.10 2 <0.002 2 0.005 2 0.000

5/23/13 OC01 surface <0.05 2 0.10 2 0.03 0.27 1 <0.002 2 0.004 1

5/23/13 OC02 surface 0.15 2 0.01 0.34 2 0.00 0.57 2 0.07 0.023 1 0.003 2 0.002

5/23/13 OC03 surface 0.08 2 0.02 0.21 2 0.07 0.16 1 <0.002 2 0.019 1

5/23/13 ON01 surface <0.05 2 0.04 2 0.00 1.54 2 0.52 <0.002 2 0.008 2 0.002

5/23/13 ON02 surface <0.05 2 0.05 2 0.00 0.18 2 0.06 <0.002 2 <0.002 2

5/23/13 ON03 surface <0.05 2 0.05 2 0.01 <0.10 2 <0.002 2 0.006 1

5/23/13 OS01 surface <0.05 2 0.05 2 0.02 0.16 1 <0.002 2 <0.002 2

5/23/13 OS02 surface <0.05 2 0.07 2 0.02 0.39 2 0.23 <0.002 2 0.006 1

5/23/13 OS03 surface 0.05 1 0.08 2 0.01 0.63 2 0.48 0.007 1 0.007 1

5/23/13 TR01 surface <0.05 2 0.05 2 0.00 0.18 2 0.00 0.002 2 0.000 <0.002 2

5/23/13 TR01 bottom <0.05 2 0.05 2 0.00 0.29 2 0.00 <0.002 2 <0.002 2

5/23/13 TR02 surface <0.05 2 0.05 2 0.01 0.19 2 0.00 <0.002 2 <0.002 2

5/23/13 TR02 bottom <0.05 2 0.06 2 0.00 <0.10 2 <0.002 2 <0.002 2

5/23/13 TR03 surface <0.05 2 0.06 2 0.01 0.55 2 0.05 <0.002 2 <0.002 2

5/23/13 TR03 bottom <0.05 2 0.08 2 0.00 0.54 2 0.04 <0.002 2 0.002 2 0.000

6/13/13 LC01 surface <0.05 2 0.18 2 0.08 <0.10 2 <0.002 2 0.006 1

6/13/13 LC02 surface <0.05 2 0.13 2 0.04 <0.10 2 <0.002 2 <0.002 2

6/13/13 LC03 surface <0.05 2 0.15 2 0.01 <0.10 2 <0.002 2 0.004 1

6/13/13 LN01 surface <0.05 2 0.08 2 0.01 <0.10 2 <0.002 2 0.003 2 0.001
76

Ni Cu Zn Cd Pb

μg L−1 μg L−1 μg L−1 μg L−1 μg L−1

Date Site Depth Mean n S.D. Mean n S.D. Mean n S.D. Mean n S.D. Mean n S.D.

6/13/13 LN02 surface <0.05 2 0.13 2 0.04 <0.10 2 <0.002 2 <0.002 2

6/13/13 LN03 surface <0.05 2 0.26 2 0.05 <0.10 2 <0.002 2 0.006 2 0.001

6/13/13 LS01 surface <0.05 2 0.21 2 0.01 <0.10 2 <0.002 2 0.005 1

6/13/13 LS02 surface <0.05 2 0.17 2 0.03 <0.10 2 <0.002 2 0.003 2 0.001

6/13/13 LS03 surface <0.05 2 0.24 2 0.07 <0.10 2 <0.002 2 0.005 2 0.003

6/13/13 OC01 surface <0.05 2 0.13 2 0.01 <0.10 2 <0.002 2 0.003 2 0.001

6/13/13 OC02 surface <0.05 2 0.11 2 0.01 <0.10 2 <0.002 2 0.003 1

6/13/13 OC03 surface <0.05 2 0.09 2 0.01 <0.10 2 <0.002 2 0.002 1

6/13/13 ON01 surface <0.05 2 0.11 2 0.01 <0.10 2 <0.002 2 0.004 2 0.003

6/13/13 ON02 surface <0.05 2 0.15 2 0.02 <0.10 2 <0.002 2 0.004 2 0.001

6/13/13 ON03 surface <0.05 2 0.11 2 0.00 <0.10 2 <0.002 2 0.002 2 0.000

6/13/13 OS01 surface <0.05 2 0.15 2 0.04 <0.10 2 <0.002 2 0.003 2 0.001

6/13/13 OS02 surface <0.05 2 0.09 2 0.01 <0.10 2 <0.002 2 <0.002 2

6/13/13 OS03 surface <0.05 2 0.12 2 0.01 <0.10 2 <0.002 2 0.002 1

6/13/13 TR01 surface <0.05 2 0.19 2 0.03 <0.10 2 <0.002 2 <0.002 2

6/13/13 TR01 bottom <0.05 2 1.22 2 0.01 <0.10 2 <0.002 2 0.003 2 0.000

6/13/13 TR02 surface <0.05 2 0.15 2 0.04 <0.10 2 <0.002 2 0.003 2 0.001

6/13/13 TR02 bottom <0.05 2 0.16 2 0.02 <0.10 2 <0.002 2 0.003 2 0.001

6/13/13 TR03 surface <0.05 2 0.12 2 0.02 <0.10 2 <0.002 2 0.002 1

6/13/13 TR03 bottom <0.05 2 0.17 2 0.00 <0.10 2 <0.002 2 0.003 2 0.001

7/3/13 LC01 surface <0.05 2 0.25 2 0.07 2.35 2 0.01 <0.002 2 0.003 2 0.002

7/3/13 LC02 surface <0.05 2 0.35 2 0.17 0.35 2 0.30 <0.002 2 0.011 1

7/3/13 LC03 surface 0.08 2 0.00 0.33 2 0.02 0.38 1 <0.002 2 0.003 1

7/3/13 LN01 surface <0.05 2 0.35 2 0.06 9.27 2 7.81 <0.002 2 0.012 2 0.005

7/3/13 LN02 surface <0.05 2 0.24 2 0.00 0.31 2 0.18 <0.002 2 0.004 2 0.001

7/3/13 LN03 surface <0.05 2 0.28 2 0.02 1.57 2 0.17 <0.002 2 0.007 2 0.000

7/3/13 LS01 surface <0.05 2 0.27 2 0.02 1.77 2 0.31 <0.002 2 0.008 2 0.003

7/3/13 LS02 surface <0.05 2 0.23 2 0.00 1.24 2 0.30 <0.002 2 0.007 2 0.001

7/3/13 LS03 surface 0.05 2 0.00 0.41 2 0.01 2.92 2 0.19 <0.002 2 0.013 2 0.002

7/3/13 OC01 surface 0.07 2 0.02 0.44 2 0.07 1.25 2 0.46 <0.002 2 0.007 2 0.002

7/3/13 OC02 surface 0.03 2 0.29 2 0.08 7.73 2 1.53 <0.002 2 0.006 2 0.004

7/3/13 OC03 surface 0.05 2 0.01 0.26 2 0.04 3.65 2 4.90 <0.002 2 0.007 2 0.003
77

Ni Cu Zn Cd Pb

μg L−1 μg L−1 μg L−1 μg L−1 μg L−1

Date Site Depth Mean n S.D. Mean n S.D. Mean n S.D. Mean n S.D. Mean n S.D.

7/3/13 ON01 surface <0.05 2 0.40 2 0.22 2.66 2 0.18 <0.002 2 0.010 2 0.006

7/3/13 ON02 surface <0.05 2 0.22 2 0.02 0.73 2 0.10 <0.002 2 0.004 2 0.001

7/3/13 ON03 surface <0.05 2 0.27 2 0.01 1.02 2 0.32 <0.002 2 0.006 2 0.000

7/3/13 OS01 surface <0.05 2 0.31 2 0.02 1.97 2 1.03 <0.002 2 0.007 2 0.003

7/3/13 OS02 surface 0.15 1 0.55 2 0.41 2.34 2 0.69 0.005 1 0.009 2 0.003

7/3/13 OS03 surface <0.05 2 0.73 2 0.65 4.39 2 4.51 0.649 1 0.180 2 0.243

7/3/13 TR01 surface <0.05 2 0.36 2 0.05 1.89 2 1.16 <0.002 2 0.009 2 0.000

7/3/13 TR01 bottom <0.05 2 0.28 2 0.05 0.55 2 0.38 <0.002 2 0.006 2 0.001

7/3/13 TR02 surface <0.05 2 0.40 2 0.03 2.42 2 1.69 <0.002 2 0.011 2 0.002

7/3/13 TR02 bottom <0.05 2 0.38 2 0.07 1.02 1 <0.002 2 0.010 2 0.000

7/3/13 TR03 surface <0.05 2 0.33 2 0.06 4.82 2 5.60 <0.002 2 0.011 2 0.003

7/3/13 TR03 bottom <0.05 2 0.42 2 0.03 4.27 2 2.48 <0.002 2 0.017 2 0.010
78

Table 8. 2013 benthic chlorophyll and pheophytin concentrations in Upper Klamath Lake, Oregon, with associated temperature and specificconductivity measurements. [μS cm−1

, microsiemen per centimeter; μg cm−2, microgram per square centimeter; Chl-a, chlorophyll a; blank cells, values that are not available; S.D., standard 

deviation for the specified n replicates; na, data not available from that time period. Slightly darkened rows in the table for June 13, 2013, are provided to help 

the reader distinguish results between sampling dates]

Specific Benthic Benthic Benthic

Temperature pHconductivity chlorophyll pheophytin Chl-a/Chl-a

oC μS cm−1 μg cm−2 μg cm−2 + Pheophytin

Date SiteName Mean Mean Mean Mean n S.D. Mean n S.D. Mean

4/30/13 MDN_A 11.5 6.7 109 1.2 2 0.2 4.8 2 0.4 0.20

4/30/13 MDN_B 11.5 6.7 109 1.2 2 0.2 4.8 2 0.4 0.20

4/30/13 MDN_C 11.5 6.7 109 1.2 2 0.2 4.8 2 0.4 0.20

4/30/13 TRRecon 11.2 7.0 100 0.6 2 0.1 2.4 2 0.5 0.20

5/23/13 LN01 12.3 7.8 107 8.8 2 3.8 15.9 2 4.6 0.36

5/23/13 LN02 13.3 7.8 106 7.8 2 2.6 14.9 2 3.5 0.34

5/23/13 LN03 13.1 7.8 107 7.8 2 1.8 24 2 6.1 0.25

5/23/13 ON01 13.1 7.8 106 3.1 2 0.6 11.1 2 1.8 0.22

5/23/13 ON02 12.7 7.5 106 1.8 2 0.2 7.8 2 0.2 0.19

5/23/13 ON03 13.4 7.8 106 2.1 2 0.1 8.5 2 0.7 0.20

5/23/13 LC01 12.3 7.9 106 7.7 2 1.3 4.4 2 2.7 0.64

5/23/13 LC02 12.8 7.7 196 9.1 2 4.3 9.3 2 0.7 0.49

5/23/13 LC03 12.8 8.0 164 9.5 2 5.3 23 2 8.1 0.29

5/23/13 OC01 12.2 8.1 114 1.9 2 0.8 5.2 2 2.3 0.27

5/23/13 OC02 13.2 8.0 111 1.0 2 0.1 3.5 2 1.2 0.22

5/23/13 OC03 12.7 8.1 116 2.1 2 0.4 6.8 2 1.3 0.24

5/23/13 LS01 13.0 8.3 209 2.6 2 0.2 9.9 2 0.8 0.21

5/23/13 LS02 12.3 8.3 205 3.7 2 1.1 13.2 2 1.4 0.22

5/23/13 LS03 13.5 8.2 215 3.5 2 1.0 8.7 2 3.0 0.29

5/23/13 OS01 12.6 7.7 113 1.1 2 0.4 6.2 2 1.6 0.15

5/23/13 OS02 12.5 8.1 106 0.5 2 0.2 3.5 2 0.5 0.13

5/23/13 OS03 12.9 7.5 277 1.5 2 0.3 10.5 2 1.7 0.13

5/23/13 TR01 13.1 7.9 113 1.6 2 1.1 8.6 2 5.3 0.16

5/23/13 TR02 13.3 8.1 113 1.7 2 1.0 4.6 2 0.9 0.27

5/23/13 TR03 13.4 8.1 113 1.4 2 0.3 8.1 2 0.8 0.15
79

Specific Benthic Benthic Benthic

Temperature pHconductivity chlorophyll pheophytin Chl-a/Chl-a

oC μS cm−1 μg cm−2 μg cm−2 + Pheophytin

Date SiteName Mean Mean Mean Mean n S.D. Mean n S.D. Mean

6/13/13 LN01 16.7 8.7 102 6.7 2 0.6 9.2 2 1.8 0.42

6/13/13 LN02 17.7 9.0 113 9.3 2 4.4 10.7 2 2.1 0.47

6/13/13 LN03 17.6 8.7 114 8.3 2 6.9 9.9 2 3.7 0.46

6/13/13 ON01 17.4 8.3 119 4.1 2 0.9 10.2 2 3.3 0.29

6/13/13 ON02 17.3 8.1 119 2.6 2 0.6 5.5 2 0.7 0.32

6/13/13 ON03 17.8 7.8 113 4.6 2 2.5 4.3 2 0.7 0.52

6/13/13 LC01 16.3 8.6 111 5.3 2 0.7 9.5 2 2.3 0.36

6/13/13 LC02 17.6 9.2 114 11.4 2 3.7 6.5 2 1.1 0.64

6/13/13 LC03 16.8 8.9 112 7.1 2 5.6 9.5 2 3.0 0.43

6/13/13 OC01 17.1 6.9 122 4.0 2 0.9 7.6 2 4.1 0.34

6/13/13 OC02 17.4 7.0 315 1.5 2 0.1 5 2 0.5 0.23

6/13/13 OC03 16.7 6.9 305 0.9 2 0.0 4 2 0.3 0.18

6/13/13 LS01 17.1 7.9 113 6.3 2 1.7 10.1 2 2.5 0.38

6/13/13 LS02 17.2 8.2 112 6.3 2 1.0 10.4 2 3.1 0.38

6/13/13 LS03 17.5 7.9 114 25.1 2 13.8 7.2 2 7.8 0.78

6/13/13 OS01 16.5 9.3 124 5.3 2 1.9 9.7 2 1.9 0.35

6/13/13 OS02 17.4 8.3 115 1.7 2 0.2 8.2 2 1.6 0.17

6/13/13 OS03 16.4 9.3 138 52.8 2 37.4 29.2 2 8.9 0.64

6/13/13 TR01 17.0 6.4 117 1.2 2 0.3 6.5 2 1.6 0.16

6/13/13 TR02 17.6 6.9 460 0.6 2 0.1 1.9 2 0.1 0.24

6/13/13 TR03 17.9 7.1 317 0.6 2 0.1 5.6 2 0.3 0.10

7/3/13 LN01 22.6 9.5 247

7/3/13 LN02 20.1 9.5 248

7/3/13 LN03 24.7 9.7 260

7/3/13 ON01 20.2 9.3 251

7/3/13 ON02 20.4 9.4 253

7/3/13 ON03 19.8 9.4 248

7/3/13 LC01 21.2 9.5 239

7/3/13 LC02 21.0 9.9 260

7/3/13 LC03 20.4 9.4 244
80

Specific Benthic Benthic Benthic

Temperature pHconductivity chlorophyll pheophytin Chl-a/Chl-a

oC μS cm−1 μg cm−2 μg cm−2 + Pheophytin

Date SiteName Mean Mean Mean Mean n S.D. Mean n S.D. Mean

7/3/13 OC01 18.8 9.7 116

7/3/13 OC02 21.1 9.8 118

7/3/13 OC03 18.4 9.5 116

7/3/13 LS01 23.5 8.5 245

7/3/13 LS02 24.1 9.2 134

7/3/13 LS03 23.5 8.2 373

7/3/13 OS01 22.4 9.6 220

7/3/13 OS02 23.5 9.2 na

7/3/13 OS03 22.2 9.5 385

7/3/13 TR01 22.4 9.6 121

7/3/13 TR02 19.5 9.5 125

7/3/13 TR03 20.0 9.2 121
81

Table 9. Mean density of macroinvertebrates per square meter (m−2), constancy (presence per sample), and percentage composition of 63 

samples (21 samples per date) during 2013. 

[Sample density was estimated as the mean of three Ekman grabs per sample. spp., species (plural); cf., confer (a species that closely matches another species); 

nr., near (taxonomically); sp., species; undet., undetermined; e.i., early instar] 

Mean density

for the three

Mean density per date sampling dates Constancy Percentage

(individuals m−2) (individuals m−2) composition

Taxon 5/23/13 6/13/13 7/3/13

Hydra 12 13 0 8 7 0.07

Dugesia 0 1 0 0 1 0.00

microturbellarians undet. 1 0 1 0 2 0.00

mermithids 6 43 16 21 16 0.17

Enchytraeidae 1 0 0 0 1 0.00

Arcteonais lomondi 3 4 1 3 8 0.02

Dero spp. 28 62 105 65 33 0.52

Nais spp. 0 4 1 2 5 0.01

Slavina appendiculata 0 1 1 1 2 0.01

Stylaria 0 1 0 0 1 0.00

Naidinae undet. 3 2 52 19 4 0.15

Aulodrilus pigueti 195 253 308 252 23 2.00

Ilyodrilus frantzi 1,922 1,844 1,544 1,770 63 14.04

cf. Limnodrilus hoffmeisteri 1,275 837 545 886 61 7.02

Limnodrilus silvani 54 44 25 41 14 0.32

Quistadrilus multisetosus 35 56 77 56 21 0.45

Rhyacodrilus 6 4 6 6 7 0.04

Spirosperma ferox 7 17 4 9 4 0.07

Spirosperma nikolskyi 0 2 0 1 2 0.01

Varichaetadrilus pacificus 234 293 251 259 21 2.05

Varichaetadrilus nr. pacificus 1,764 1,385 1,390 1,513 53 12.00

Tubificinae with hairs small 320 701 690 570 50 4.52

Bothrioneurum veidovskyanum 1 16 1 6 5 0.05

Tubificinae without hairs small 1,054 782 372 736 60 5.84

Tubificinae very small 126 49 97 91 44 0.72

Altmanella freidris 202 166 143 170 39 1.35
82

Mean density

for the three

Mean density per date sampling dates Constancy Percentage

(individuals m−2) (individuals m−2) composition

Taxon 5/23/13 6/13/13 7/3/13

Lumbriculus 0 1 1 0 2 0.00

Rhynchelmis klamathensis 306 276 198 260 57 2.06

Rhynchelmis coccoons 0 0 0 0 0 0.00

Glossiphonia complanata 1 0 2 1 3 0.01

Helobdella bowermani 94 494 530 372 57 2.95

Helobdella nr. bowermani 82 181 479 247 50 1.96

Helobdella nr. robusta 151 137 545 277 48 2.20

Helobdella stagnalis 15 97 113 75 31 0.59

Helobdella hatchlings undet. 162 1,568 872 867 50 6.88

Theromyzon sp. 0 0 1 0 1 0.00

Mooreobdella microstoma 4 20 13 12 18 0.10

Erpobdellidae undet. 0 6 0 2 3 0.02

Piscicolidae 4 4 34 14 19 0.11

Caecidotea cf. occidentalis 1 8 4 4 4 0.04

Gammarus 0 0 0 0 0 0.00

Hyalella 1 6 1 3 7 0.02

Baetidae undet. 0 0 0 0 0 0.00

Caenis 9 22 5 12 10 0.10

Coenagrionidae undet. 1 0 0 0 1 0.00

Isoperla 0 1 0 0 1 0.00

Corixidae undet. 0 0 0 0 0 0.00

Sialis 1 0 0 0 1 0.00

Hydroptila 0 1 0 0 1 0.00

Limnephilus 1 0 0 0 2 0.00

Mystacides 1 2 1 1 5 0.01

Oecetis 4 4 1 3 6 0.02

Brychius 0 1 0 0 1 0.00

Dytiscidae 0 1 0 0 1 0.00

Dubiraphia 2 0 1 1 2 0.01

Bezzia 13 33 20 22 16 0.18
83

Mean density

for the three

Mean density per date sampling dates Constancy Percentage

(individuals m−2) (individuals m−2) composition

Taxon 5/23/13 6/13/13 7/3/13

Procladius sp. 1 1,359 1,753 943 1,352 63 10.72

Orthocladiinae 0 1 3 1 3 0.01

Chironomus cf. plumosus 65 49 25 47 23 0.37

Chironomus cf. utahensis 27 13 45 28 22 0.22

cf. Chironomus e.i. 1 84 1,438 508 34 4.03

Cryptochironomus 56 34 14 35 25 0.27

Cryptotendipes 256 193 56 169 47 1.34

Dicrotentipes 1 4 1 2 3 0.01

Glyptotendipes sp. 1 3 9 43 18 6 0.15

Parachironomus 3 7 13 8 16 0.06

Chironomini "Harnischia comp." e.i. 1 0 1 1 3 0.01

Chironomini undet 5 6 262 91 25 0.72

Cladotanytarsus 1,754 607 188 850 35 6.74

Tanytarsini 40 440 1,305 595 35 4.72

cf. Hygrobates 6 2 2 3 8 0.03

cf. Piona 1 1 0 1 3 0.01

Mite undet. 4 4 4 4 10 0.03

Valvata 14 11 18 15 8 0.12

Fluminicola 47 56 51 51 25 0.41

Pyrgulopsis 8 4 6 6 6 0.05

Planorbidae spp. 4 13 1 6 11 0.05

Sphaeriidae undet. 135 214 127 159 26 1.26

Total mean abundance 11,888 12,945 12,998 12,610

Total richness 61.0 65.0 60.0 74.0
84

Table 10. Summary of results from 2014 sonde deployments near site ON01 in Upper Klamath Lake, Oregon.

 [m, meter; μS cm−1, microsiemen per centimeter; % DO, percent dissolved oxygen; mg L−1, milligram per liter; blank cells, values that are not available. Times 

shown are in Pacific Standard Time. Slightly darkened rows in the table distinguish the bloom period from the pre-bloom and post-bloom periods]

Specific % DO DO Vertical % DO

Depth conductivity Temperature saturation concentration pH saturation 

Date Time m μS cm−1 °C (local) mg L−1 decrease (%)

4/16/14 10:00 0.0 112 12.9 100.8 9.0 7.8 1.1

10:05 2.7 112 12.5 99.7 8.9 7.7

4/23/14 8:19 0.0 115 11.7 97.0 8.9 7.4 1.1

8:16 2.5 115 11.7 95.9 8.8 7.4

4/30/14 9:59 0.0 112 12.9 125.9 11.5 8.6 27.6

9:54 2.2 112 10.8 98.3 9.4 8.0

5/8/14 9:07 0.0 113 12.8 100.8 9.2 7.9 3.6

9:13 2.4 113 12.6 97.2 8.9 7.8

5/14/14 9:26 0.0 116 15.6 111.1 9.5 7.8 30.2

9:32 2.9 116 12.1 80.9 7.5 7.2

5/21/14 10:05 0.0 115 15.8 101.5 8.7 7.9 4.0

10:00 2.6 116 15.1 97.5 8.4 7.8

5/29/14 9:53 0.0 117 15.9 101.1 8.6 8.1 79.7

9:49 2.6 117 15.4 21.4 1.8 7.9

6/4/14 9:21 0.0 118 17.6 96.8 8.0 7.9 2.0

9:15 2.2 118 17.4 94.8 7.8 7.9

6/11/14 9:32 0.0 120 20.1 128.9 10.1 9.2 20.2

9:25 2.3 119 19.4 108.7 8.6 8.9

6/18/14 8:30 0.0 122 15.9 145.2 12.4 9.5 18.1

8:34 2.3 120 15.6 127.1 10.9 9.7

6/25/14 9:23 0.0 127 19.3 139.1 11.0 10.1 33.0

9:30 2.0 124 19.0 106.1 8.5 9.9

7/2/14 9:07 0.0 142 23.1 10.3

9:13 2.1 121 20.5 9.8

7/9/14 9:43 0.0 121 23.6 101.5 8.6 10.0 72.4

9:40 2.0 118 22.3 29.1 2.5 9.6

7/16/14 9:47 0.0 121 25.7 9.2

9:56 2.0 122 24.7 8.9
85

Specific % DO DO Vertical % DO

Depth conductivity Temperature saturation concentration pH saturation 

Date Time m μS cm−1 °C (local) mg L−1 decrease (%)

7/23/14 9:05 0.0 120 21.7 8.8

9:10 1.9 120 21.7 8.8

7/30/14 9:10 0.0 116 21.3 100.1 8.1 9.3 29.5

9:18 1.6 116 20.9 70.6 5.8 9.2

8/6/14 9:48 0.0 117 22.1 100.0 7.1 9.1 36.1

9:53 1.5 121 21.5 63.9 4.6 8.7

8/13/14 10:06 0.0 118 20.9 100.3 6.7 9.0 9.1

10:13 1.6 117 20.7 91.1 6.1 8.9

8/20/14 9:34 0.0 124 19.6 103.9 4.9 7.7 23.9

9:39 1.5 124 20.5 80.0 3.7 7.7

8/27/14 9:06 0.0 125 20.2 100.0 6.4 8.1 16.9

9:15 1.5 125 20.0 83.1 5.3 7.7

9/4/14 9:40 0.0 124 17.3 101.0 8.0 8.5 3.4

9:36 1.4 125 17.3 97.6 7.7 8.5

9/10/14 9:29 0.0 126 16.9 101.3 7.3 8.4 1.2

9:22 1.5 126 16.8 100.1 7.2 8.3

9/17/14 9:16 0.0 125 17.1 100.0 8.4 8.8 21.4

9:22 1.4 129 18.0 78.6 6.5 8.0

9/24/14 9:50 0.0 136 16.7 100.0 6.9 6.4 21.8

9:57 1.4 136 16.9 78.2 5.3 7.6

10/1/14 9:18 0.0 127 13.0 98.6 6.7 7.4 0.2

9:13 1.5 128 12.9 98.4 6.7 7.4

10/8/14 9:41 0.0 131 14.1 98.9 5.7 7.4 1.4

9:49 1.3 131 14.0 97.5 5.6 7.4

10/16/14 9:30 0.0 132 10.9 99.7 7.7 7.0 7.0

9:38 1.4 131 10.8 92.7 7.2 7.4
86

Table 11. 2014 water-column and pheophytin concentrations in Upper Klamath Lake, Oregon, near site ON01. 

[μg L−1, microgram per liter; Chl-a, chlorophyll a; S.D., standard deviation for the specified n replicates. Slightly darkened rows in the table distinguish the 

bloom period from the pre-bloom and post-bloom periods]

Chl-a Pheophytin Chl-a/Chl-a

μg L−1 μg L−1 + Pheophytin

Mean n S.D. Mean n S.D.

4/16/14 5.66 3 0.25 2.34 3 0.33 0.71

4/23/14 6.32 3 0.50 2.83 3 0.43 0.69

4/30/14 12.96 3 0.30 3.04 3 0.31 0.81

5/6/14 5.49 3 0.64 1.72 3 0.14 0.76

5/14/14 4.45 3 0.52 1.42 3 0.18 0.76

5/21/14 8.69 3 0.08 2.74 3 0.44 0.76

5/28/14 14.46 3 0.43 2.98 3 0.27 0.83

6/4/14 18.40 3 13.70 2.89 3 0.67 0.86

6/11/14 66.91 3 26.69 5.74 3 3.17 0.92

6/18/14 207.27 3 21.53 120.07 3 87.09 0.63

6/25/14 98.59 3 44.18 42.89 3 42.17 0.70

7/2/14 257.13 3 24.51 156.06 3 96.18 0.62

7/8/14 249.75 3 137.73 75.36 3 36.39 0.77

7/15/14 67.00 3 12.76 20.57 3 14.03 0.77

7/23/14 169.10 3 70.12 17.76 3 7.18 0.90

7/30/14 124.52 3 42.90 26.19 3 16.85 0.83

8/6/14 103.28 3 54.86 11.59 3 4.60 0.90

8/13/14 200.76 3 35.10 26.47 3 26.65 0.88

8/20/14 42.11 3 14.69 18.46 3 12.30 0.70

8/27/14 44.95 3 1.53 4.00 3 3.02 0.92

9/3/14 31.34 3 4.67 4.60 3 3.74 0.87

9/10/14 28.11 3 2.69 3.92 3 1.14 0.88

9/17/14 21.38 3 4.92 3.68 3 1.14 0.85

9/24/14 26.66 3 0.40 4.23 3 0.66 0.86

10/1/14 12.22 3 4.39 1.60 3 0.14 0.88

10/8/14 6.17 3 1.55 1.69 3 0.23 0.79

10/16/14 11.35 3 1.81 5.37 3 1.68 0.68
87

Table 12. 2014 Dissolved-macronutrient benthic fluxes from profiler deployments in Upper Klamath Lake, Oregon, near site ON01.

 [mg m−2 d−1, milligram per square meter per day; nd, nondetectable benthic flux owing to concentrations from profiler samples that were consistently below 

detection limits for that solute.; blank cells, flux values that are not available, typically owing to a problem with profiler deployment; S.D., standard deviation for 

the specified n replicates. Slightly darkened rows in the table distinguish the bloom period from the pre-bloom and post-bloom periods] 

SRP as P Ammonia as N Nitrate as N Silica as SiO2 DOC as C

mg m−2 d−1 mg m−2 d−1 mg m−2 d−1 mg m−2 d−1 mg m−2 d−1

Date Mean n S.D. Mean n S.D. Mean n S.D. Mean n S.D. Mean n S.D.

4/15/14 4.06 6 2.90 4.27 6 4.87 −0.45 6 0.33 47.0 6 47.0 0.59 6 1.67

4/22/14 1.73 6 1.43 3.91 6 3.20 −2.16 6 0.98 18.7 6 11.0 −0.80 6 1.16

4/29/14 1.55 6 0.70 3.23 6 1.25 −0.97 6 0.62 14.0 6 13.0 −3.63 6 2.87

5/7/14 2.65 6 1.12 5.84 6 7.03 −0.01 1 10.6 6 6.3 0.70 6 1.09

5/13/14 3.42 6 2.40 4.08 6 1.95 nd 6 51.9 6 46.7 −0.38 6 1.13

5/20/14 2.82 6 0.64 9.41 6 9.66 −0.01 1 64.9 6 55.8 0.79 6 2.47

5/28/14 4.26 6 3.99 12.86 6 13.43 −0.03 3 0.04 72.4 6 57.8 −0.57 6 1.53

6/3/14 2.21 6 1.33 7.57 6 4.23 −0.10 4 0.05 53.5 6 38.6 −1.88 6 1.14

6/10/14 3.37 6 2.74 9.59 6 7.87 −0.07 4 0.05 42.3 6 30.4 −0.62 6 3.36

6/17/14 1.17 6 0.44 5.87 6 1.78 −0.03 5 0.04 34.5 6 27.9 −2.61 6 1.39

6/24/14 1.95 6 0.69 7.57 6 4.15 −0.36 2 0.47 43.3 6 25.1 −10.60 6 4.80

7/1/14 5.77 6 4.59 11.27 6 4.93 −0.69 5 0.75 48.8 6 25.6 −6.90 6 2.29

7/8/14 1.70 6 0.63 10.58 6 4.81 nd 6 27.2 6 17.8 −9.13 6 8.80

7/15/14 1.62 6 1.06 3.95 6 2.18 nd 6 61.6 6 58.2 −5.81 6 2.68

7/22/14 1.68 6 0.74 16.26 6 6.35 −1.00 3 1.00 40.5 6 35.1 −9.51 6 5.49

7/29/14 5.46 6 2.34 13.50 6 11.22 nd 6 82.8 6 52.5 −14.37 6 7.27

8/5/14 3.33 6 1.43 20.62 6 5.48 −0.33 5 0.19 106.5 6 60.4 −11.41 6 6.10

8/13/14 2.53 6 1.87 10.33 6 7.27 −1.45 6 1.21 35.2 6 35.2 −6.79 6 5.08

8/20/14 1.82 6 1.16 13.03 6 11.06 −6.70 3 6.59 33.0 6 27.5 −4.83 6 1.58

8/27/14 2.95 6 2.31 12.75 6 6.56 −4.08 5 1.11 32.8 6 22.5 −5.52 6 2.42

9/4/14 1.02 6 0.57 4.05 6 2.11 −3.50 6 3.76 11.9 6 6.7 −4.08 6 3.37

9/10/14 1.01 6 0.58 2.97 6 2.43 −0.04 1 6.4 6 3.7 −1.30 6 3.63

9/17/14 1.21 6 0.56 1.87 6 0.79 nd 6 8.4 6 6.8 11.52 6 9.07

9/24/14 1.36 6 1.19 1.97 6 2.02 −0.08 5 0.03 8.5 6 5.6 5.32 6 9.38

10/1/14 0.60 6 0.60 1.74 6 2.86 −0.52 6 0.24 7.7 6 8.0 0.17 6 2.81

10/8/14 0.98 6 0.34 8.91 6 9.85 −0.40 6 0.29 12.4 6 8.2 −1.69 6 2.18

10/16/14 1.74 6 1.21 6.48 6 4.07 −0.34 5 0.22 18.1 6 12.1 −1.60 6 1.76
88

Table 13. Dissolved (0.2-micrometer filtered) macronutrient concentrations in the water column of Upper Klamath Lake, Oregon, near site ON01.

[Because the lake is generally shallow (site depths <3 meters [m]), water-column samples were collected just below the surface (~0.5 m), with the exception of 

the deeper trench (TR) sites (6.0–14.2 m depth). Trench sites were sampled at both the surface and ~1 m above the lakebed. Blank cells indicate values that are 

not available. Un-ionized ammonia (NH3

0

) concentrations were calculated from temperature and pH effects on ammonia speciation at their respective depths 

(Canadian Council of Ministers of the Environment, 2010). SRP, soluble reactive phosphorus; P, phosphorus; N, nitrogen; DOC, dissolved organic carbon; C, 

carbon; mg L−1

, milligram per liter; S.D. indicates standard deviation for the specified n replicates. Slightly darkened rows in the table distinguish the bloom 

period from the pre-bloom and post-bloom periods]

SRP as P Ammonia as N Nitrate as N Silica as SiO2 DOC as C

mg L−1 mg L−1 mg L−1 mg L−1 mg L−1 mg L−1

Date Mean n S.D. Mean n S.D. Un-ionized Mean n S.D. Mean n S.D. Mean n S.D.

4/15/14 0.026 2 0.001 <0.007 2 0.12 2 0.00 45.5 2 0.3 3.2 2 0.1

4/22/14 0.049 2 0.004 0.036 2 0.007 0.00 0.12 2 0.01 43.2 2 0.8 3.8 2 0.1

4/29/14 0.024 2 0.002 <0.007 2 0.03 2 0.01 41.0 2 0.5 3.2 2 0.0

5/7/14 0.046 2 0.001 0.036 2 0.001 0.00 0.02 1 48.4 2 0.0 3.9 2 0.1

5/13/14 0.051 2 0.005 <0.007 2 <0.01 2 44.7 2 0.1 4.0 2 0.1

5/20/14 0.056 2 0.000 <0.007 2 <0.01 2 43.4 2 1.1 4.1 2 0.1

5/28/14 0.056 2 0.017 0.056 2 0.001 0.00 0.06 2 0.04 36.7 2 1.0 3.8 2 0.0

6/3/14 0.091 2 0.002 0.070 2 0.005 0.00 0.04 2 0.02 36.0 2 0.5 4.1 2 0.1

6/10/14 0.058 2 0.000 0.017 2 0.001 0.01 <0.01 2 36.5 2 0.0 4.4 2 0.1

6/17/14 0.042 2 0.001 <0.007 2 <0.01 2 36.8 2 0.4 4.2 2 0.1

6/24/14 0.054 2 0.008 <0.007 2 0.02 2 0.00 40.4 2 0.3 5.5 2 0.1

7/1/14 0.106 2 0.021 <0.007 2 0.01 2 0.00 37.6 2 2.7 9.3 2 0.2

7/8/14 0.158 2 0.005 0.083 2 0.011 0.07 0.05 2 0.01 43.8 2 0.2 9.1 2 0.2

7/15/14 0.246 2 0.001 0.600 2 0.011 0.27 0.03 2 0.01 47.7 2 0.3 8.5 2 0.0

7/22/14 0.244 2 0.001 0.575 2 0.002 0.12 0.06 2 0.01 51.9 2 0.2 7.4 2 0.1

7/29/14 0.198 2 0.006 0.135 2 0.004 0.06 0.06 2 0.01 53.9 2 0.4 7.3 2 0.0

8/5/14 0.179 1 0.185 2 0.014 0.07 0.07 2 0.00 60.2 2 0.6 7.0 2 0.0

8/13/14 0.134 2 0.004 0.119 2 0.035 0.03 0.12 2 0.01 60.6 2 0.4 6.7 2 0.1

8/20/14 0.112 2 0.000 0.243 2 0.001 0.00 0.20 2 0.01 61.6 2 0.3 6.4 2 0.0

8/27/14 0.028 2 0.010 0.037 2 0.022 0.00 0.17 2 0.01 60.9 2 0.4 5.8 2 0.0

9/4/14 0.033 2 0.000 0.034 2 0.011 0.00 0.06 2 0.00 63.0 2 0.9 5.2 2 0.0

9/10/14 0.029 2 0.000 0.042 2 0.006 0.00 0.02 2 0.00 64.0 2 0.1 6.9 2 0.0

9/17/14 0.027 2 0.001 0.041 2 0.001 0.01 <0.01 61.5 2 0.9 4.7 2 0.0

9/24/14 0.032 2 0.001 0.025 2 0.002 0.00 0.07 2 0.00 60.6 2 0.6 5.1 2 0.0

10/1/14 0.023 2 0.001 0.090 2 0.000 0.00 0.17 2 0.01 56.3 2 0.3 4.5 2 0.0

10/8/14 0.029 2 0.000 0.157 2 0.004 0.00 0.14 2 0.00 56.4 2 0.5 4.5 2 0.0

10/16/14 0.019 2 0.001 0.236 2 0.004 0.00 0.12 2 0.00 55.3 2 0.2 4.4 2 0.0
89

Table 14. 2014 dissolved trace-element benthic fluxes from profiler deployments near site ON01.

[Fe, iron; Mn, manganese; As, arsenic; V, vanadium; Co, cobalt; Ni, nickel; Cu, copper; Zn, zinc; Cd, cadmium; Pb, lead; μg m−2 d−1, microgram per square 

meter per day; nd, nondetectable benthic flux due to concentrations from profiler samples that were consistently below detection limits for that solute; blank 

cells, values that are not available; S.D., standard deviation for the specified n replicates. Slightly darkened rows in the table distinguish the bloom period from 

the pre-bloom and post-bloom periods] 

Fe Mn As V Co

μg m−2 d−1 μg m−2 d−1 μg m−2 d−1 μg m−2 d−1 μg m−2 d−1

Date Mean n S.D. Mean n S.D. Mean n S.D. Mean n S.D. Mean n S.D.

4/15/14 2,408 6 1,744 573 6 401 26.2 6 25.4 −42.19 6 59.38 0.65 6 0.58

4/22/14 2,489 6 1,076 997 6 463 1.6 6 15.8 −41.40 6 40.67 0.22 6 0.19

4/29/14 1,917 6 1,644 489 6 296 8.4 6 13.2 −40.21 6 37.59 0.09 6 0.08

5/7/14 2,171 6 1,537 1,290 6 735 25.9 6 43.9 −78.76 6 78.64 0.19 6 0.31

5/13/14 4,203 6 2,432 793 6 352 1.4 6 5.7 −14.88 6 15.94 0.33 6 0.34

5/20/14 3,852 6 2,897 532 6 173 -5.7 6 7.6 −63.82 6 54.09 0.29 6 0.45

5/28/14 3,904 6 2,688 331 6 316 -2.7 6 2.5 −24.36 6 17.66 0.15 6 0.17

6/3/14 6,059 6 6,341 833 6 965 -8.5 6 5.5 −97.57 6 91.38 0.15 6 0.17

6/10/14 5,235 6 2,243 550 6 277 -7.1 6 18.8 −66.45 6 72.23 0.25 6 0.28

6/17/14 2,991 6 1,284 321 6 243 -24.9 6 39.8 −60.76 6 76.63 0.05 6 0.10

6/24/14 2,696 6 1,752 280 6 236 -12.7 6 5.8 −91.42 6 146.22 0.01 6 0.14

7/1/14 3,731 6 2,943 430 6 349 -14.4 6 7.9 −35.75 6 10.61 0.45 6 0.63

7/8/14 1,051 6 392 -6 6 232 -13.8 6 11.8 −21.81 6 21.90 0.03 6 0.11

7/15/14 1,028 6 668 316 6 309 -17.8 6 14.3 −38.76 6 39.77 0.12 6 0.07

7/22/14 1,061 6 729 312 6 348 -39.3 6 26.3 −125.05 6 183.02 0.07 6 0.12

7/29/14 2,166 6 1,440 274 6 233 -34.8 6 18.4 −18.26 6 7.51 0.16 6 0.08

8/5/14 1,804 6 929 388 6 165 -26.9 6 15.4 −120.44 6 80.68 0.16 6 0.15

8/12/14 971 6 431 222 6 110 -31.0 6 9.3 −44.50 6 44.93 −0.05 6 0.13

8/20/14 948 6 894 173 6 184 -47.9 6 41.9 −36.81 6 34.36 0.09 6 0.22

8/27/14 2,748 6 1,561 461 6 368 -16.7 6 8.2 −73.75 6 54.46 0.04 6 0.09

9/4/14 1,334 6 1,162 412 6 214 70.6 6 37.4 −95.95 6 33.32 0.45 6 0.31

9/10/14 1,265 6 2,181 337 6 294 15.3 6 47.5 −61.59 6 50.12 0.25 6 0.30

9/17/14 1,487 6 819 591 6 379 68.3 6 23.0 −55.46 6 28.00 0.45 6 0.30

9/24/14 1,308 6 972 302 6 337 57.8 6 51.8 −33.32 6 19.36 0.43 6 0.58

10/1/14 2,445 5 2,010 279 5 187 27.4 5 21.8 −27.47 5 17.48 0.09 6 0.11

10/8/14 3,530 6 4,662 135 6 74 -29.3 6 38.5 −48.05 6 51.33 0.05 6 0.09

10/16/14 4,290 6 5,328 322 6 202 -4.0 6 88.3 −37.71 6 65.20 0.22 6 0.25
90

Ni Cu Zn Cd Pb

μg m−2 d−1 μg m−2 d−1 μg m−2 d−1 μg m−2 d−1 μg m−2 d−1

Date Mean n S.D. Mean n S.D. Mean n S.D. Mean n S.D. Mean n S.D.

4/15/14 0.49 6 0.49 −0.90 6 0.41 1.24 6 2.99 0.00 6 0.00 0.00 6 0.00

4/22/14 −0.20 6 0.45 −1.27 6 0.61 −0.54 6 3.78 0.00 6 0.00 0.00 6 0.00

4/29/14 −0.03 6 0.22 −1.84 6 1.06 −0.72 6 0.79 0.00 6 0.00 0.00 6 0.00

5/7/14 0.12 6 0.30 −0.65 6 0.43 0.00 6 0.00 0.00 6 0.00 0.00 6 0.00

5/13/14 0.28 6 0.58 −0.58 6 0.31 0.00 6 0.00 0.00 6 0.00 0.00 6 0.00

5/20/14 −0.04 6 0.26 −1.85 6 1.47 −6.56 6 7.55 0.00 6 0.00 0.00 6 0.00

5/28/14 −0.14 6 0.50 −1.63 6 1.30 −0.63 6 1.21 0.00 6 0.00 0.00 6 0.00

6/3/14 −0.10 6 0.42 −4.13 6 7.73 −19.85 6 46.26 0.00 6 0.00 0.00 6 0.00

6/10/14 0.17 6 0.33 −0.63 6 0.48 −0.17 6 1.23 0.00 6 0.00 0.00 6 0.00

6/17/14 −0.02 6 0.09 −0.89 6 0.77 0.12 6 0.75 0.00 6 0.00 0.00 6 0.00

6/24/14 −0.03 6 0.14 −1.35 6 0.91 1.68 6 4.98 0.00 6 0.00 0.00 6 0.00

7/1/14 0.06 6 0.39 −1.36 6 0.56 −2.14 6 7.35 0.00 6 0.00 0.00 6 0.00

7/8/14 −0.05 6 0.15 −0.71 6 0.75 −1.49 6 4.86 0.00 6 0.00 0.00 6 0.00

7/15/14 −0.08 6 0.37 −0.80 6 1.25 −0.06 6 0.76 0.00 6 0.00 −0.06 6 0.06

7/22/14 −0.03 6 0.11 −0.26 6 1.47 −0.29 6 1.44 0.00 6 0.00 −0.02 6 0.10

7/29/14 0.04 6 0.34 −0.61 6 0.58 −0.19 6 2.41 0.00 6 0.00 0.00 6 0.00

8/5/14 −0.07 6 0.14 −1.15 6 0.88 −0.24 6 2.60 0.00 6 0.00 −0.07 6 0.06

8/12/14 −0.14 6 0.06 −1.42 6 1.39 −5.10 6 7.90 0.00 6 0.00 −0.04 6 0.04

8/20/14 −0.09 6 0.06 9.23 6 25.04 −3.61 6 4.25 0.00 6 0.00 −0.02 6 0.01

8/27/14 −0.18 6 0.14 −1.79 6 0.79 2.29 6 7.50 0.00 6 0.00 −0.05 6 0.02

9/4/14 0.03 6 0.18 −1.05 6 1.40 −8.48 6 28.49 0.00 6 0.00 0.00 6 0.00

9/10/14 0.06 6 0.20 −0.78 6 0.71 1.46 6 3.02 0.00 6 0.00 −0.01 6 0.02

9/17/14 0.39 6 0.48 −1.65 6 1.08 0.50 6 1.08 0.00 6 0.00 0.00 6 0.00

9/24/14 0.21 6 0.19 0.49 6 0.88 0.81 6 15.15 0.00 6 0.00 0.03 6 0.17

10/1/14 0.00 6 0.00 0.00 5 0.00 0.79 5 2.19 0.00 5 0.00 0.00 5 0.00

10/8/14 0.00 6 0.00 0.00 6 0.00 0.06 6 0.15 0.00 6 0.00 −1.17 6 2.42

10/16/14 0.14 6 0.18 −0.27 6 0.21 0.73 6 1.52 0.00 6 0.00 0.00 6 0.05
91

Table 15. 2014 dissolved (0.2-micrometer filtered) trace-element concentrations in the water column of Upper Klamath Lake, Oregon, near site 

ON01.

[[Fe, iron; Mn, manganese; As, arsenic; V, vanadium; Co, cobalt; Ni, nickel; Cu, copper; Zn, zinc; Cd, cadmium; Pb, lead; μg L−1, microgram per liter; blank 

cells, values that are not available; S.D., standard deviation for the specified n replicates; na, data not available from that time period. Slightly darkened rows in 

the table distinguish the bloom period from the pre-bloom and post-bloom periods] 

Fe Mn As V Co

ug L−1 ug L−1 ug L−1 ug L−1 ug L−1

Date Mean n S.D. Mean n S.D. Mean n S.D. Mean n S.D. Mean n S.D.

4/15/14 26.8 2 1.9 2.4 2 0.1 3.39 2 0.01 10.09 2 0.09 0.042 2 0.003

4/22/14 5.5 2 0.3 0.9 2 0.2 2.79 2 0.00 5.42 2 0.02 0.017 2 0.002

4/29/14 6.2 2 0.2 <0.1 2 2.91 2 0.03 5.60 2 0.11 0.019 2 0.007

5/7/14 6.1 2 1.2 <0.1 2 3.30 2 0.00 5.65 2 0.05 0.019 2 0.005

5/13/14 16.4 2 0.2 0.3 2 0.0 4.11 2 0.04 9.28 2 0.05 0.031 2 0.001

5/20/14 15.8 2 0.0 0.4 2 0.0 4.28 2 0.00 9.35 2 0.08 0.030 2 0.002

5/28/14 15.9 2 0.6 1.3 2 0.8 6.44 2 0.21 9.07 2 0.19 0.032 2 0.008

6/3/14 19.1 2 0.1 3.0 2 2.2 4.80 2 0.00 9.07 2 0.07 0.041 2 0.006

6/10/14 16.9 2 0.8 1.3 2 0.1 5.09 2 0.10 9.79 2 0.22 0.047 2 0.000

6/17/14 16.4 2 0.9 0.7 2 0.0 5.43 2 0.02 10.43 2 0.05 0.056 2 0.002

6/24/14 na na na na na

7/1/14 15.8 2 0.8 1.2 2 0.1 6.21 2 0.01 10.54 2 0.04 0.081 2 0.002

7/8/14 18.1 2 9.8 1.7 2 0.7 5.60 2 0.00 6.18 2 0.10 0.057 2 0.004

7/15/14 34.5 2 6.6 1.8 2 0.1 9.15 2 0.03 8.99 2 0.06 0.107 2 0.001

7/22/14 18.9 2 6.2 1.1 2 0.1 10.19 2 0.07 7.78 2 0.06 0.070 2 0.005

7/29/14 16.4 2 0.6 4.0 2 0.1 10.16 2 0.02 6.66 2 0.02 0.078 2 0.000

8/5/14 25.6 2 1.2 2.0 2 0.2 11.94 2 0.07 7.55 2 0.16 0.087 2 0.000

8/12/14 23.8 2 1.2 1.0 2 0.0 11.65 2 0.10 7.18 2 0.02 0.075 2 0.002

8/19/14 31.1 2 9.9 3.8 2 0.3 10.09 2 0.01 5.83 2 0.22 0.068 2 0.001

8/26/14 28.4 2 4.6 3.3 2 0.0 8.50 2 0.00 8.19 2 0.01 0.066 2 0.000

9/3/14 27.9 2 8.0 0.9 2 0.3 7.66 2 0.04 9.76 2 0.04 0.046 2 0.000

9/9/14 14.5 2 1.9 0.4 2 0.1 7.32 2 0.09 9.73 2 0.03 0.044 2 0.002

9/16/14 13.6 2 0.5 0.8 2 0.1 7.29 2 0.01 8.63 2 0.00 0.036 2 0.001

9/23/14 33.3 2 3.0 3.4 2 0.0 8.26 2 0.04 7.87 2 0.08 0.050 2 0.001

9/30/14 29.6 2 0.3 3.8 2 0.0 6.16 2 0.02 9.00 2 0.01 0.047 2 0.000

10/7/14 42.4 2 19.1 10.3 2 1.4 6.76 2 0.09 9.36 2 0.07 0.051 2 0.001

10/14/14 27.6 2 0.6 3.5 2 0.1 6.68 2 0.07 11.34 2 0.20 0.038 2 0.002
92

Ni Cu Zn Cd Pb

ug L−1 ug L−1 ug L−1 ug L−1 ug L−1

Date Mean n S.D. Mean n S.D. Mean n S.D. Mean n S.D. Mean n S.D.

4/15/14 0.35 2 0.02 0.737 2 0.050 1.4 2 0.3 <0.002 2 0.004 1

4/22/14 <0.05 2 0.316 2 0.023 1.6 2 0.2 <0.002 2 <0.002 2

4/29/14 <0.05 2 0.364 2 0.001 4.0 2 1.0 <0.002 2 0.011 2 0.005

5/7/14 <0.05 2 0.304 2 0.010 1.8 2 1.2 <0.002 2 <0.002 2

5/13/14 0.19 2 0.00 0.425 2 0.001 0.6 2 0.1 <0.002 2 0.002 1

5/20/14 0.20 2 0.00 0.416 2 0.016 1.7 2 0.0 <0.002 2 <0.002 2

5/28/14 0.05 2 0.02 0.207 2 0.007 1.3 2 0.2 <0.002 2 <0.002 2

6/3/14 0.23 2 0.03 0.437 2 0.033 2.3 2 1.5 <0.002 2 0.019 2 0.023

6/10/14 0.22 2 0.02 0.428 2 0.025 1.7 2 0.7 <0.002 2 0.013 2 0.013

6/17/14 0.20 2 0.00 0.422 2 0.003 0.9 2 0.1 <0.002 2 <0.002 2

6/24/14 na na na na na

7/1/14 0.22 2 0.01 0.532 2 0.011 2.8 2 1.1 <0.002 2 0.012 2 0.008

7/8/14 <0.05 2 0.393 2 0.011 8.5 2 6.2 0.003 1 0.124 2 0.159

7/15/14 0.18 2 0.03 0.895 2 0.062 11.7 2 3.3 0.004 1 0.035 2 0.030

7/22/14 <0.05 2 0.048 2 0.000 5.2 2 0.0 <0.002 2 0.011 1

7/29/14 0.19 2 0.05 0.088 2 0.004 9.7 2 0.1 <0.002 2 0.019 2 0.025

8/5/14 0.30 2 0.00 0.446 2 0.008 4.4 2 0.5 <0.002 2 0.012 2 0.002

8/12/14 0.30 2 0.00 0.468 2 0.072 2.0 2 0.9 <0.002 2 0.009 2 0.003

8/19/14 0.33 2 0.01 0.438 2 0.012 1.1 2 0.5 <0.002 2 0.008 2 0.002

8/26/14 0.35 2 0.01 0.368 2 0.002 2.2 2 1.0 <0.002 2 0.006 2 0.003

9/3/14 0.07 2 0.01 <0.01 2 0.5 2 0.3 <0.002 2 <0.002 2

9/9/14 0.06 2 0.01 0.148 2 0.079 6.8 2 3.1 <0.002 2 <0.002 2

9/16/14 0.18 2 0.04 0.259 2 0.212 3.7 2 2.0 <0.002 2 0.032 1

9/23/14 0.23 2 0.01 0.193 2 0.056 4.0 2 1.0 <0.002 2 <0.002 2

9/30/14 0.26 2 0.01 0.542 2 0.482 5.0 2 0.6 <0.002 2 <0.002 2

10/7/14 0.31 2 0.05 0.334 2 0.063 5.2 2 1.5 <0.002 2 0.006 1

10/14/14 0.20 2 0.00 0.455 2 0.307 0.8 2 0.5 <0.002 2 <0.002 2
93

Table 16. Mean density of macroinvertebrates per square meter (m−2), mean density per sampling interval, constancy (presence per sample), and 

percentage composition of 27 weekly samples collected during 2014 at site ON01. 

[Sample density was estimated as the mean of five Ekman grabs per sample. Each interval included nine collecting dates: pre-bloom period between April 15 and 

June 10, 2014; bloom period between June 16 and August 12, 2014; and post-bloom period between August 19 and October 16, 2014. spp., species (plural); cf., 

confer (a species that closely matches another species); nr., near (taxonomically); sp., species; undet., undetermined; e.i., early instar] 

Mean density

for the three

Mean density

sampling 

periods Constancy Percentage

(individuals m−2) (individuals m−2) composition

Taxon Pre-bloom Bloom Post-bloom

Hydra 0 0 0 0 0 0.00

Dugesia 0 0 0 0 0 0.00

microturbellarians undet. 0 0 0 0 0 0.00

mermithids 4 27 44 25 20 0.13

Enchytraeidae 0 0 0 0 0 0.00

Arcteonais lomondi 0 0 0 0 0 0.00

Dero spp. 60 838 13,367 4,755 27 24.02

Nais spp. 0 0 0 0 0 0.00

Slavina appendiculata 0 0 0 0 0 0.00

Stylaria 0 0 0 0 0 0.00

Naidinae undet. 0 0 0 0 0 0.00

Aulodrilus pigueti 76 200 2,175 817 26 4.13

Ilyodrilus frantzi 2,582 2,175 6,050 3,602 27 18.20

cf. Limnodrilus hoffmeisteri 31 49 115 65 24 0.33

Limnodrilus silvani 0 0 0 0 0 0.00

Quistadrilus multisetosus 2 9 69 27 11 0.13

Rhyacodrilus 0 0 0 0 0 0.00

Spirosperma ferox 0 0 0 0 0 0.00

Spirosperma nikolskyi 0 0 0 0 0 0.00

Varichaetadrilus pacificus 26 44 53 41 24 0.21

Varichaetadrilus nr. pacificus 2,141 1,318 2,140 1,866 27 9.43

Tubificinae with hairs small 2,043 1,246 527 1,272 27 6.43

Bothrioneurum veidovskyanum 0 0 0 0 0 0.00

Tubificinae without hairs small 298 229 1,457 661 27 3.34

Tubificinae very small 128 497 489 371 26 1.88
94

Mean density

for the three

Mean density

sampling 

periods Constancy Percentage

(individuals m−2) (individuals m−2) composition

Taxon Pre-bloom Bloom Post-bloom

Altmanella freidris 64 157 210 144 27 0.73

Lumbriculus 0 0 0 0 0 0.00

Rhynchelmis klamathensis 49 66 55 57 27 0.29

Rhynchelmis cocoons 0 0 105 35 4 0.18

Glossiphonia complanata 0 0 0 0 0 0.00

Helobdella bowermani 241 523 453 406 27 2.05

Helobdella nr. bowermani 206 349 1,714 756 27 3.82

Helobdella nr. robusta 352 702 1,027 694 27 3.50

Helobdella stagnalis 0 0 0 0 0 0.00

Helobdella hatchlings undet. 338 643 975 652 27 3.29

Theromyzon sp. 0 0 1 0 1 0.00

Mooreobdella microstoma 0 0 2 1 2 0.00

Erpobdellidae undet. 0 0 0 0 0 0.00

Piscicolidae 10 6 71 29 16 0.15

Caecidotea cf. occidentalis 0 0 0 0 0 0.00

Gammarus 0 1 1 1 2 0.00

Hyalella 0 2 1 1 3 0.00

Baetidae undet. 1 0 0 0 1 0.00

Caenis 0 0 0 0 0 0.00

Coenagrionidae undet. 1 0 0 0 1 0.00

Isoperla 0 0 0 0 0 0.00

Corixidae undet. 0 1 2 1 3 0.00

Sialis 0 0 0 0 0 0.00

Hydroptila 0 0 0 0 0 0.00

Limnephilus 0 0 0 0 0 0.00

Mystacides 0 0 0 0 0 0.00

Oecetis 0 0 2 1 2 0.00

Brychius 0 0 0 0 0 0.00

Dytiscidae 0 0 0 0 0 0.00
95

Mean density

for the three

Mean density

sampling 

periods Constancy Percentage

(individuals m−2) (individuals m−2) composition

Taxon Pre-bloom Bloom Post-bloom

Dubiraphia 0 0 0 0 0 0.00

Bezzia 0 0 0 0 0 0.00

Procladius sp. 1 1,210 694 476 793 27 4.01

Orthocladiinae 0 0 1 0 1 0.00

Chironomus cf. plumosus 1 152 2,251 801 15 4.05

Chironomus cf. utahensis 0 83 62 48 13 0.24

cf. Chironomus e.i. 1 561 790 451 19 2.28

Cryptochironomus 2 37 5 14 11 0.07

Cryptotendipes 377 1,278 205 620 26 3.13

Dicrotentipes 0 27 6 11 10 0.05

Glyptotendipes sp. 1 0 2 2 1 3 0.01

Parachironomus 1 21 22 14 10 0.07

Chironomini "Harnischia comp." e.i. 0 0 0 0 0 0.00

Chironomini undet 4 179 117 100 21 0.50

Cladotanytarsus 370 82 90 181 22 0.91

Tanytarsini 23 467 937 476 22 2.40

cf. Hygrobates 0 0 0 0 0 0.00

cf. Piona 0 0 2 1 2 0.00

Mite undet. 0 1 2 1 3 0.00

Valvata 0 0 0 0 0 0.00

Fluminicola 0 1 0 0 1 0.00

Pyrgulopsis 0 0 0 0 0 0.00

Planorbidae spp. 0 1 0 0 1 0.00

Sphaeriidae undet. 0 0 4 1 3 0.01

Total mean abundance 10,643 12,668 36,078 19,796

Total richness 29.0 36.0 41.0 45.0
96

Table 17. Taxonomic list of benthic invertebrates collected in 2013 and 2014 collections. [spp., species (plural); cf., confer (a species that closely matches another species); nr., near (taxonomically); sp., species; undet., undetermined; e.i., early instar] 

Phylum Class Order Family Subfamily Taxon

Cnidaria Hydrozoa Anthoathecatae Hydridae Hydra

Platyhelminthes Trepaxonemata Neoophora Planariidae Dugesia

Platyhelminthes microturbellarians undet.

Nematoda Adenophorea Mermithida Mermithidae mermithids

Annelida Oligochaeta Enchytraeida Enchytraeidae Enchytraeidae

Annelida Oligochaeta Tubificida Naididae Arcteonais lomondi

Annelida Oligochaeta Tubificida Naididae Dero spp.

Annelida Oligochaeta Tubificida Naididae Nais spp.

Annelida Oligochaeta Tubificida Naididae Slavina appendiculata

Annelida Oligochaeta Tubificida Naididae Stylaria

Annelida Oligochaeta Tubificida Naididae Naidinae undet.

Annelida Oligochaeta Tubificida Naididae Aulodrilus pigueti

Annelida Oligochaeta Tubificida Naididae Ilyodrilus frantzi

Annelida Oligochaeta Tubificida Naididae cf. Limnodrilus hoffmeisteri

Annelida Oligochaeta Tubificida Naididae Limnodrilus silvani

Annelida Oligochaeta Tubificida Naididae Quistadrilus multisetosus

Annelida Oligochaeta Tubificida Naididae Rhyacodrilus

Annelida Oligochaeta Tubificida Naididae Spirosperma ferox

Annelida Oligochaeta Tubificida Naididae Spirosperma nikolskyi

Annelida Oligochaeta Tubificida Naididae Varichaetadrilus pacificus

Annelida Oligochaeta Tubificida Naididae Varichaetadrilus nr. pacificus

Annelida Oligochaeta Tubificida Naididae Tubificinae with hairs small

Annelida Oligochaeta Tubificida Naididae Bothrioneurum veidovskyanum

Annelida Oligochaeta Tubificida Naididae Tubificinae without hairs small

Annelida Oligochaeta Tubificida Naididae Tubificinae very small

Annelida Oligochaeta Lumbriculida Lumbriculidae Altmanella freidris

Annelida Oligochaeta Lumbriculida Lumbriculidae Lumbriculus

Annelida Oligochaeta Lumbriculida Lumbriculidae Rhynchelmis klamathensis

Annelida Oligochaeta Lumbriculida Lumbriculidae Rhynchelmis coccoons

Annelida Hirudinea Rhynchobdellida Glossiphoniidae Glossiphonia complanata

Annelida Hirudinea Rhynchobdellida Glossiphoniidae Helobdella bowermani

Annelida Hirudinea Rhynchobdellida Glossiphoniidae Helobdella nr. bowermani
97

Phylum Class Order Family Subfamily Taxon

Annelida Hirudinea Rhynchobdellida Glossiphoniidae Helobdella nr. robusta

Annelida Hirudinea Rhynchobdellida Glossiphoniidae Helobdella stagnalis

Annelida Hirudinea Rhynchobdellida Glossiphoniidae Helobdella hatchlings undet.

Annelida Hirudinea Rhynchobdellida Glossiphoniidae Theromyzon sp.

Annelida Hirudinea Arhynchobdellida Erpobdellidae Mooreobdella microstoma

Annelida Hirudinea Arhynchobdellida Erpobdellidae Erpobdellidae undet.

Annelida Hirudinea Rhynchobdellida Piscicolidae Piscicolidae

Arthopoda Malacostraca Isopoda Asellidae Caecidotea cf. occidentalis

Arthopoda Malacostraca Amphipoda Gammaridae Gammarus

Arthopoda Malacostraca Amphipoda Hyalellidae Hyalella

Arthopoda Insecta Ephemeroptera Baetidae Baetidae undet.

Arthopoda Insecta Ephemeroptera Caenidae Caenis

Arthopoda Insecta Odonata Coenagrionidae Coenagrionidae undet.

Arthopoda Insecta Plecoptera Perlodidae Isoperla

Arthopoda Insecta Hemiptera Corixidae Corixidae undet.

Arthopoda Insecta Megaloptera Sialidae Sialis

Arthopoda Insecta Trichoptera Hydroptilidae Hydroptila

Arthopoda Insecta Trichoptera Limnephilidae Limnephilus

Arthopoda Insecta Trichoptera Leptoceridae Mystacides

Arthopoda Insecta Trichoptera Leptoceridae Oecetis

Arthopoda Insecta Coleoptera Haliplidae Brychius

Arthopoda Insecta Coleoptera Dytiscidae Dytiscidae

Arthopoda Insecta Coleoptera Elmidae Dubiraphia

Arthopoda Insecta Diptera Ceratopogonidae Bezzia

Arthopoda Insecta Diptera Chironomidae Tanypodinae Procladius sp. 1

Arthopoda Insecta Diptera Chironomidae Orthocladiinae Orthocladiinae

Arthopoda Insecta Diptera Chironomidae Chironominae Chironomus cf. plumosus

Arthopoda Insecta Diptera Chironomidae Chironominae Chironomus cf. utahensis

Arthopoda Insecta Diptera Chironomidae Chironominae cf. Chironomus e.i.

Arthopoda Insecta Diptera Chironomidae Chironominae Cryptochironomus

Arthopoda Insecta Diptera Chironomidae Chironominae Cryptotendipes

Arthopoda Insecta Diptera Chironomidae Chironominae Dicrotentipes

Arthopoda Insecta Diptera Chironomidae Chironominae Glyptotendipes sp. 1

Arthopoda Insecta Diptera Chironomidae Chironominae Parachironomus
98

Phylum Class Order Family Subfamily Taxon

Arthopoda Insecta Diptera Chironomidae Chironominae Chironomini "Harnischia comp." e.i.

Arthopoda Insecta Diptera Chironomidae Chironominae Chironomini undet

Arthopoda Insecta Diptera Chironomidae Chironominae Cladotanytarsus

Arthopoda Insecta Diptera Chironomidae Chironominae Tanytarsini

Arthopoda Arachnida Trombidiformes Hygrobatidae cf. Hygrobates

Arthopoda Arachnida Trombidiformes Pionidae cf. Piona

Arthopoda Arachnida Mite undet.

Mollusca Gastropoda Hererostropha Valvatidae Valvata

Mollusca Gastropoda Neotaenioglossa Hydrobiidae Fluminicola

Mollusca Gastropoda Neotaenioglossa Hydrobiidae Pyrgulopsis

Mollusca Gastropoda Basommatophora Planorbidae Planorbidae spp.

Mollusca Bivalvia Veneroida Pisidiidae Sphaeriidae undet.
99

Table 18. Phosphorus release (efflux) by some of the most numerically dominant and widespread taxa residing in Upper Klamath Lake.

[μg SRP-P ind−1 d−1, microgram of soluble reactive phosphorus as phosphorus per individual per day; inds m−2, individuals per square meter; S.D., standard 

deviation for the specified n estimates; mg L−1, milligram per liter; nr., near (taxonomically); cf., confer (a species that closely matches another species); e.i., 

early instar. Density is based on mean density of taxa determined from the 2013 spatial collections. Spatial collections occurred on three dates: May 23, June 13, 

and July 3, 2013, at 21 randomly selected sites throughout the lake. Although some Helobdella individuals were too small to identify to species, efflux was 

estimated for the taxon, but no density data were available to scale their contribution to total efflux]

Mean efflux S.D. Density Efflux

Taxon n μg SRP-P ind−1 d−1 inds m−2 mg SRP-P m−2 d−1

Ilyodrilus frantzi 56 0.055 0.091 1,770 0.098

Varichaetadrilus nr. pacificus 32 0.118 0.121 1,513 0.178

Rhynchelmis klamathensis 16 1.250 0.824 260 0.325

Helobdella 22 1.408 0.579

Helobdella nr. bowermani 3 1.747 1.118 686 1.198

Helobdella robusta 4 0.880 0.749 281 0.247

Mooreobdella 3 17.139 3.872 12 0.214

Procladius 2 0.546 0.108 1,352 0.739

Chironomus plumosus 297 0.843 1.092 47 0.039

Chironomus cf. plumosus e.i. 6 0.213 0.104 508 0.108

Chironomus utahensis 2 0.141 0.097 28 0.004

Cryptochironomus 18 1.920 1.253 35 0.066

Cryptotendipes 1 0.053 169 0.009

Fluminicola 8 0.669 0.415 51 0.034

Total 470 6,711 3.259

Formulation of Artificial Lake Water

for efflux experiments

Constituent mg L−1

KCl 3.73

MgSO−4 12.32

CaCl2 16.65

NaHCO3 23.52
100

Table 19. Potential importance of solute advective flux across the sediment-water interface. 

[Available at http://dx.doi.org/10.3133/ofr20161175.] 
101

Table 20. Environmental variables and floc population properties, Upper Klamath Lake, Oregon, May 6, 

2015.

[SPMC, suspended particulate matter concentration; mg L−1, milligram per liter; m, meter; mm, millimeter; mm s−1, 

millimeter per second; kg m−3

, kilogram per cubic meter; D, particle size; Ws, settling velocity; ρe, density; MSF, 

mass settling flux; mg m−2

 s−1, milligram per square meter per second]

Sample # 0515_S01 0515_S03 0515_S04 0515_S06

Station and sampling depth ML dp_10 ML dp_90 LS01 dp_10 LS01 dp_90

SPMC (mg L−1) 25 140 4.5 11

Water depth (m) 4.5 4.5 2.2 2.2

Number of flocs 94 364 22 44

Number of macroflocs 5 73 - 2 

Dmean (mm) 84 122 66 79

Wsmean (mm s−1) 1.62 3.28 0.57 0.9

ρe_mean (kg m−3) 560 540 263 365

Porositymean (%) 53 57 79 71

Wsmacro (mm s−1) 6.35 3.34 - 1.1

Wsmicro (mm s−1) 1.36 3.26 0.57 0.89

ρe_macro (kg m−3) 103 188 - 39

Porositymacro (%) 92 85 - 97

Macroflocs (% by mass) 21.3% 31.1% - 13.8%

MSFmacro (% of total MSF) 54.7% 30.6% - 14.0%

Mass setting flux (mg m−2 s−1) 62 464 2.82 11.5
102

Table 21. Environmental variables and floc population properties, Upper Klamath Lake, Oregon, July 30, 

2015.

[SPMC, suspended particulate matter concentration; mg L−1, milligram per liter; m, meter; mm, millimeter; mm s−1, 

millimeter per second; kg m−3

, kilogram per cubic meter; D, particle size; Ws, settling velocity; ρe, density; MSF, 

mass settling flux; mg m−2

 s−1, milligram per square meter per second]

Sample # 0715_S01 0715_S03 0715_S04 0715_S06

Station and sampling depth ML dp_10 ML dp_90 LS01 dp_10 LS01 dp_90

SPMC (mg L−1) 8 21 32 316

Water depth (m) 3.9 3.9 1.6 1.6

Number of flocs 17 38 58 330

Number of macroflocs 9 23 27 275

Dmean (mm) 167 178 175 305

Wsmean (mm s−1) 1.48 1.67 0.40 3.86

ρe_mean (kg m−3) 92 170 43 81

Porositymean (%) 93 86 97 94

Wsmacro (mm s−1) 2.31 1.83 0.49 4.46

Wsmicro (mm s−1) 0.55 1.41 0.33 0.86

ρe_macro (kg m−3) 79 72 15 75

Porositymacro (%) 94 94 99 94

Macroflocs (% by mass) 69.3% 77.9% 66.8% 93.8%

MSFmacro (% of total MSF) 91.5% 79.7% 75.2% 98.8%

Mass setting flux (mg m−2 s−1) 14.8 36.6 14.8 1,580
103

Table 22. Environmental variables and floc population properties, Upper Klamath Lake, Oregon, October 

6, 2015.

[SPMC, suspended particulate matter concentration; mg L−1, milligram per liter; m, meter; mm, millimeter; mm s−1, 

millimeter per second; kg m−3

, kilogram per cubic meter; D, particle size; Ws, settling velocity; ρe, density; MSF, 

mass settling flux; mg m−2

 s−1, milligram per square meter per second]

Sample # 1015_S01 1015_S03 1015_S04 1015_S06

Station and sampling depth ML dp_10 ML dp_90 LS01 dp_10 LS01 dp_90

SPMC (mg L−1) 10 62 49 828

Water depth (m) 3.1 3.1 0.7 0.7

Number of flocs 17 101 112 1095

Number of Macroflocs 8 64 33 718

Dmean (mm) 186 195 138.5 238.5

Wsmean (mm s−1) 3.72 3.45 1.32 3.91

ρe_mean (kg m−3) 302 167 190 220

Porositymean (%) 76 87 85 83

Wsmacro (mm s−1) 5.67 4.57 2.37 4.74

Wsmicro (mm s−1) 1.99 1.51 0.88 2.31

ρe_macro (kg m−3) 214 155 53 122

Porositymacro (%) 83 88 96 90

Macroflocs (% by mass) 71.3% 77.2% 57.1% 84.2%

MSFmacro (% of total MSF) 85.5% 91.3% 78.9% 92.1%

Mass setting flux (mg m−2 s−1) 43.94 258.03 99.85 4,139