Final Report 

For the Project 

Linking Observational and Experimental Approaches for 

the Development of Regional Nutrient Criteria for Wadeable 

Streams 

15 August 2009 

Section 104(b)(3) Water Quality Cooperative Agreement 

#CP-966137-01 

U. S. EPA Region 6 

Prepared by: 

Ryan. S. King, Ph.D. 

Principal Investigator and Project Contact 

Associate Professor, Department of Biology, Baylor University 

One Bear Place #97388, Waco, TX 76798 

Tel: 254.710.2150; E-mail: Ryan_S_King@baylor.edu

Lab webpage: www.baylor.edu/aquaticlab

Co-Investigators: 

Bryan W. Brooks, Ph.D., Associate Professor, Dept. of Environmental Science, Baylor 

Jeffrey A. Back, Ph. D. candidate (King), Dept of Biology, Baylor 

Jason M. Taylor, Ph. D. candidate (King), Dept. of Biology, Baylor 

Barry A. Fulton, M. S. (Brooks), Dept. of Environmental Science, Baylor. 
R.S. King et al. 2009 2 

TABLE OF CONTENTS 

Acknowledgments 3 

Executive Summary 5 

Problem Definition and Background 14 

Section I. Field Study 16 

Study area and sampling methods 17 

Data analyses 22 

Results: Temporal patterns in discharge and nutrient concentrations 25 

Results: Biological responses to TP gradients 33 

Results: Periphyton taxonomic responses to TP: ordinations 59 

Results: Periphyton taxonomic responses to TP: Threshold Indicator Taxa 

Analysis (TITAN) 

65 

Results: Macroinvertebrate taxonomic responses to TP 78 

Section II: Experimental Stream Study 84 

Site Description 85 

Experimental Design 87 

Sampling and Data Analysis 89 

Results: Nutrient concentrations among the experimental streams 91 

Results: Periphyton and filamentous algal biomass response to P dosing 93 

Results: Periphyton nutrient content response to experimental P dosing 98 

Results: Algae species responses to experimental P dosing 102 

Results: Macroinvertebrate taxa responses to experimental P dosing 105 

Conclusions and Recommendations 106 

Literature Cited 107 

Appendix A: Algal and macroinvertebrate taxa codes 110 

Appendix B: Publications supported in part or full by #CP-966137-01 118 
R.S. King et al. 2009 3 

ACKNOWLEDGMENTS 

We thank US EPA Region 6 for the primary financial support of this research through Section 

104(b)(3) Water Quality Cooperative Agreement #CP-966137-01. We appreciate the guidance 

of our EPA Project Managers Sharon Daugherty, Miriama Dover, and Sondra McDonald. 

Additional data collection for the field study, beyond what was outlined in the QAPP, was 

facilitated by additional support from a subcontract from Texas Commission on Environmental 

Quality to Ryan S. King at Baylor University through Texas AgriLife for “Refinement and 

Validation of Habitat Quality Indices (HQI) and Aquatic Life Use (ALU) Indices for Application 

to Assessment and Monitoring of Texas Surface Waters” Contract 582-6-80304 (K. O. 

Winemiller, PI, TAMU), and a contract from Texas Commission on Environmental Quality to 

Ryan S. King at Baylor University for “Development of biological indicators of nutrient 

enrichment for application in Texas streams,” Section106 Water Pollution Control Grant # 

98665304. The field portion of this study was coordinated intentionally so that the 26 field study 

sites for this project were also chosen as sites within the study designs implemented by these 

other two TCEQ sponsored projects. These other studies provided additional support for 

graduate students, which allowed us to collect data for a longer period of time than originally 

proposed. 

The field component of this study was designed, implemented, and managed by RSK and 

members of his laboratory. Several individuals contributed significantly to the collection of the 

data for the field component of the project. Jeffrey A. Back and Jason M. Taylor unequivocally 

contributed more time in the lab and field on the field study than any other investigator. Charles 

E. Stanley, David A. Lang, Justin Grimm, J. Thad Scott, Emily Hooser, Adolfo Flores, Success 

Sumpaoengoen, Brianna Kirchner, Rebecca Shaftel, and Julie Baldizar (in approximate order of 

hours served) also contributed significant time to the field portion of this study. 

The experimental stream component of the study was made possible from a unique partnership 

with the City of Waco, who provided the majority of the financial and logistical support for 

construction of the Baylor Experimental Aquatic Research (BEAR) facility. Tom Conry, Nora 

Schell, John McMillan, Buck Alambar, and David Kerr from the City of Waco deserve particular 

acknowledgement for making the BEAR facility a reality. Additional funds were contributed to 

Baylor for construction and personnel by an environmental stewardship grant from Altria Group, 

Inc. to Ryan S. King (PI), Bryan W. Brooks, and Robert D. Doyle. 

The experimental stream component of this study required numerous individuals and many hours 

of labor to accomplish. The experimental stream study was designed, implemented, and 

managed by RSK and BWB. We thank Barry Fulton, Theodore Valenti, Jason Berninger, and 

Richard Brain from Bryan Brooks’ lab for leading the design and implementation of the 

peristaltic pump dosing system, and for contributing significantly to the routine restocking of the 

P stock solutions. Jason Taylor, Rebecca Shaftel, Barry Fulton, Jeff Back, Justin Grimm, 

Charles Stanley, Julie Baldizar, Theodore Valenti, Jason Berninger, and Richard Brain each put 

in significant time in the set-up, sampling, lab processing, and data entry phases of the BEAR 

study. Jeffrey Back performed all of the nutrient chemistry analyses during this experiment. 

We especially thank Dr. Barbara Winsborough, of Winsborough Conulting, Leander, TX, for her 

hard work and rapid delivery of algae species identifications from both the field and 
R.S. King et al. 2009 4 

experimental studies. Dr. Winsborough conducted all algal species identifications reported in 

this document. 

Study designs, choice of data analyses, presentation and interpretation of results, and conclusions 

and recommendations are those of the Principal Investigator and not those of the U.S. EPA, and 

no official endorsement from the U.S. EPA should be inferred. 
R.S. King et al. 2009 5 

EXECUTIVE SUMMARY 

We employed a novel approach to developing a defensible, effects-based numerical target 

concentration of surface-water nutrients for wadeable streams in the Cross Timbers portion of 

Aggregate Nutrient Ecoregion IX of U. S. EPA Region 6. Our approach relied on an integration 

of collected field data from wadeable streams in Texas with experimental data generated from 

stream mesocosms at the Baylor Experimental Aquatic Research (BEAR) facility. 

Observational Field Study 

The observational field component of this study was conducted from August 2006 through June 

2008. Data were collected quarterly (8 sampling events) from 26 wadeable streams in the Brazos 

River basin within the Cross Timbers Level III Ecoregion. The selected streams spanned a steep 

gradient of phosphorus enrichment (< 10 ug/L to >2000 ug/L TP). Sites with low, moderate, and 

high levels of P enrichment were widely distributed throughout the study area. The study design 

captured the full range of natural variability in geology, drainage networks, stream size, and 

other physiographic factors, while still achieving a wide spatial distribution of P enrichment and 

its likely sources (wastewater effluent discharges, pasture). 

Field streams had similar hydrographs during the 2-year study. Differences in discharge were 

much greater among seasons and years rather than differences among the individual streams. 

August 2006-February 2007 was a drought; discharge was very low in all streams except those 

near effluent discharges. Heavy rains led to flooding in spring 2007, and streams maintained 

relatively high base-flow discharges through the remainder of the study. 

Surface-water nutrients, particularly PO4-P and TP, were more strongly influenced by 

differences among individual streams than by seasonal patterns of discharge. Streams with 

apparently few point or non-point sources of phosphorus exhibited consistently low TP. Streams 

with predominantly non-point sources of phosphorus (watersheds with pasture or other sources) 

showed sharp increases in TP during high discharge, reflecting transport of P from the land from 

surface-water runoff. Streams with point sources (effluent discharges) either had consistently 

high TP or had moderate declines in TP due to dilution by high surface-water runoff. 

Periphyton, Filamentous Algae, and Macrophyte Responses to TP 

We hypothesized excessive levels of enrichment of surface-water nutrients would be reflected in 

sharp changes in enrichment of the tissues of the periphyton. Indeed, sites with different levels 

of surface-water PO4-P or TP differed markedly in their periphyton C:P (carbon-to-phosphorus) 

and N:P (nitrogen-to-phosphorus) molar ratios. TP consistently predicted nonlinear, threshold 

declines in periphyton C:P ratios. Sites with elevated surface-water TP had virtually constant 

periphyton C:P or N:P ratios over time (typically less than 300 and 20, respectively). Threshold 

declines in periphyton C:P, N:P, and C:N ratios were highly likely between 15 and 25 ug/L TP, 

with 19-20 ug/L representing the most consistent concentration yielding a threshold response. 

Moreover, despite the high variability of DIN (dissolved inorganic nitrogen) in surface waters 
R.S. King et al. 2009 6 

among sites over time, the periphyton N:P ratio was essentially identical to the C:P ratio, 

illustrating that P, not N, was driving differences in these ratios. 

Significant threshold changes were detected in several of the periphyton, algal, and macrophyte 

biological response variables in response to TP. The thickness of microbial films (microalgae 

and other microbes; non-filamentous algae) growing on rocks showed consistently nonlinear 

declines in response to TP levels above 20 ug/L. This metric was computed as the percentage of 

observations in a reach (out of a maximum of 100 points along the reach-scale transect) in which 

biofilms exceeded 1 mm in thickness on rocks. Streams with TP < 20 typically had moderate to 

heavy growth of calcareous periphyton. 

Streams above the threshold value (20 ug/L) usually had a thinner, darker layer of biofilms on 

the rocks but also tended to have more filamentous green algae. There were 2 distinct 

changepoints in filamentous algal cover: a consistent increase in cover among sites >20 ug/L 

TP, and a less consistent but more dramatic increase in cover at very high levels of TP (>200-

1000 ug/L). Because filamentous algal cover depends highly on seasonal changes in light and 

water temperature, as well as sloughing from overgrowth and scouring from high flows, it is not 

surprising that many of the highly enriched sites had little filamentous algal cover on some dates. 

However, when high filamentous algal cover occurred, it was almost always associated with 

levels of P enrichment > 20 ug/L, and particularly at >200 ug/L. 

The ratio of periphyton chlorphyll to AFDM revealed a nonlinear shift toward a greater fraction 

of chlorophyll-bearing organisms in the periphyton in response to TP > 20-30 ug/L. A 

secondary threshold appeared around 200 ug/L, likely related to the 2-tiered response of 

filamentous algae and chlorophyll-a at 20 and 200 ug/L TP. 

One of the most important patterns that emerged from the field study was the sharp threshold 

declines in submersed macrophyte cover in response to TP. The two dominant submersed 

macrophyte taxa were Najas (a vascular plant) and Chara (a nonvascular charophyte); both 

virtually disappeared in streams with TP exceeding 25-54 ug/L TP. 

Diel Dissolved Oxygen Response to TP 

Interannual differences in stream discharge strongly influenced the daily (diel) variation in 

dissolved oxygen, water temperature, and pH. In 2006, a drought year, most of the field study 

streams had very low-to-no measureable discharge. This lack of flow limited the turbulent 

mixing of the water column. During the day, streams were supersaturated with dissolved oxygen 

(up 20 mg/L DO; > 250% saturation) which concomitantly caused pH to increase to relatively 

high levels (8.5-9.5). At night, DO was consumed and collapsed below 2 mg/L in some of the 

study streams. In September 2007, discharge was relatively high in all 26 study streams. Most 

of the streams were in flood stage for several of the preceding months and had recently subsided 

to wadeable flows. With higher flows and turblent mixing, minimum DO levels were rarely 

below 5 mg/L. 
R.S. King et al. 2009 7 

The harmful (0-2 mg/L) minimum DO values at several of the sites in 2006 were related to 

surface-water TP. All 6 streams with low flow and TP > 27.2 had minimum DO values < 2 

mg/L, and two others with TP near 20 ug/L dropped below 3.5 mg/L DO. The four streams 

located immediately downstream of effluent discharges maintained relatively high minimum DO 

concentrations despite very high TP. This was related to reaeration associated with turbulent 

mixing and artificially high flows during a drought when most other streams were not flowing. 

However, these artificially high flows quickly declined within a few kilometers from the 

upstream effluent discharges due to evaporation and infiltration below the stream channel. Here 

the lack of reaeration by turbulent mixing coupled with high nutrient levels from the effluent 

caused DO to be almost completely consumed at night. Thus, effluent discharges were having 

dramatic effects on DO, but farther downstream than typically considered in monitoring studies. 

Minimum DO in 2006 was also predicted significantly by mean biofilm thickness, filamentous 

algal cover, and the ratio of periphyton chlorophyll to AFDM. Thicker layers of calcareous 

periphyton were associated with low-P streams, and as TP increased, these biofilms succeeded to 

filamentous and colonial greens and other types of algae. Thus, these metrics appear to be 

sensitive to TP enrichment and may be linked quantitatively to aquatic life use standards that rely 

on dissolved oxygen as a measure of biological integrity. 

Algal Species Composition Response to TP 

Multivariate analyses of algal species composition during August 2006 and September 2007 

continued to support the earlier conclusions that TP and related metrics had a strong influence on 

stream biota. In both years, most of the variance in algal species composition was related to TP 

amd related measures. Sites with the lowest TP and highest periphyton C:P ratios were clearly 

grouped away from sites with the highest TP and lowest C:P ratios, which were also distinctly 

grouped. Although algal species changed between years, the relative grouping of sites from low 

P to high P remained intact regardless of year. This was compelling evidence that algal species 

composition was altered by nutrient enrichment, and the magnitude of its effect was similar 

regardless of interannual variation in stream discharge. 

Threshold Indicator Taxa Analysis (TITAN) continued to reveal stream biological thresholds at 

low levels of TP. Dozens of algal species essentially disappeared from streams between 12 and 

30 ug/L TP, with the threshold of greatest overall decline at 19.2 (2006) and 21.6 (2007) ug/L 

TP, respectively. Simultaneously, numerous algal taxa showed sharp increases, either replacing 

taxa as they declined, or driving the declines via competitive exclusion (e.g., shading by 

Cladophora, a significant increasing taxon in both years). 

Although most taxa declined or increased at relatively low levels of TP, a few taxa responded 

only to much higher levels of TP. These taxa were mostly associated with sites heavily 

influenced by effluent, and suggested a second tier of biological degradation at very high levels 

of TP (200-500 ug/L). 
R.S. King et al. 2009 8 

Macroinvertebrate Taxa Responses to TP 

Results of ordinations and Threshold Indicator Taxa Analysis (TITAN) on macroinvertebrate 

taxonomic composition during November 2006 showed that nutrients, specifically phosphorus, 

corresponded to shifts in community structure. Because composition differed substantially 

between sites with and without flow, and because of established relationship between flow and 

other water chemistry variables (e.g., DO), we separated sites into groups by flow status prior to 

analysis. 

Sites with no flow yielded more reliable relationships to TP than sites with flow, but this result 

should be interpreted with caution because there were only 9 sites in the group with flow. 

Ordinations clearly showed that in both sets of analyses, the sites with low TP and high 

periphyton C:P sorted together, whereas high TP and low C:P sites sorted away from the low TP 

sites. In both sets of TITAN analyses, more taxa were classified as negative threshold indicators 

(decline in response to TP) than as positive ones (increase). 

Experimental Stream Study

The experimental stream study was conducted from 31 January to 7 April 2008 at the Baylor 

Experimental Aquartic Research (BEAR) facility. Twelve streams were used in the study. The 

experimental streams are approximately 0.6 m wide and 20 m in length. The streams are 

stratified into riffle, glide, and pool sections and are designed mimic natural habitat in central 

Texas streams. Water is pumped from the adjacent Lake Waco Wetland and delivered to the 

streams via a series of 12 valves, each adjusted to regulate flow equivalently among all 12 

streams (180 L/min). Before entering the streams, water iss pumped into a mixing tank where 

chemicals representing experimental treatments can be dosed at a fixed rate using peristaltic 

pumps connected to a second, adjacent tank of chemical stock solution. Once dosed with the 

treatment levels of non-toxic chemicals, water is released into the streams and is discharged back 

into the Lake Waco Wetlands. 

Clean river cobble and gravel were placed in streams in January 2008 to simulate erosional 

habitat in natural stream in our field study. Stream flow was initiated on 31 January 2008 and 

calibrated to 182 L/min (+/- 5 L/min) for each stream. Streams were seeded with organic matter, 

periphyton, and macroinvertebrates collected from two of the intensive field sites. These sites 

were chosen because their P concentrations were representative of the background (control) and 

high P treatments to be employed in the experiment. Streams were seeded twice: 1 February and 

15 February 2008. Seeding was accomplished by transplanting organisms and organic matter 

from five 1-m2

 kick screen samples collected from into each of the 12 streams. 

The streams were allowed to run without any nutrient additions from 31 January to 10 March 

2008 in an effort to allow growth of periphyton at low nutrient concentrations. Established 

periphyton communities from riffle habitat from two intensive field sites, ROCK-01 (very low 

ambient phosphorus levels; 1-5 ug/L PO4-P, 4-12 ug/L TP) and NBOS-03 (moderately high P 

levels: 50-200 ug/L PO4-P, 75-250 ug/L TP) were also transplanted into the streams. Cobbles 

from ROCK-01 (300) and NBOS-03 (300) were placed in random cross sections throughout the 
R.S. King et al. 2009 9 

reach of each experimental stream on 7 and 8 March, respectively, a few days prior to initiation 

of the experiment. 

Experimental dosing of phosphorus began on 11 March 2008. Background concentration of 

PO4-P during the colonization phase of the study was 6 and increased to 8 ug/L during the 

experiment. Four streams were randomly assigned no additional phosphorus, and thus a target 

concentration of 8 ug/L (Control). Because our field study had identified consistent nonlinear 

changes in numerous biological response variables between 10 and 20 ug/L PO4-P (15-25 ug/L 

TP), four streams were assigned a treatment of +15 ug/L PO4-P to achieve a target PO4-P 

concentration of 20 ug/L (Low P treatment). The remaining four streams received +95 ug/L 

PO4-P to achieve a target concentration of 100 ug/L (High P treatment). 

Periphyton and Filamentous Algae Response to P Dosing 

Periphyton growing on the ceramic tiles responded rapidly to experimental P dosing. High P 

treatments had significantly more chlorophyll than Low-P and Controls on Day 14. By Day 28, 

Low P and High P treatments were not different, but had significantly more chlorophyll-a than 

Controls. The lack of difference between low and high P treatments is consistent with the 

threshold response observed in the field study. 

Periphyton (biofilm) AFDM and chlorophyll-a from the bare rocks and transplanted rocks was 

highly variable among P treatments and did not differ significantly. However, this was largely 

due to the very strong response of Cladophora (filamentous green algae) in the low and high P 

treatments. Cladophora biomass exploded near the end of the study, and was significantly 

higher in the Low and High P streams than the Controls on the bare rocks and transplant rocks 

(ROCK-01, NBOS-03) on Day 28. 

Periphyton C:P ratios from non-transplant bare rock samples differed significantly by treatment 

on Day 28. High P treated streams had the lowest C:P ratio (~150), Controls had the highest 

(~320) whereas Low-P streams were intermediately enriched (~230). Control C:P ratios were 

approaching levels deemed to be near or below a C:P threshold in the field study, suggesting that 

even the Control PO4-P (8 ug/L) and TP (19-20 ug/L) concentrations were high enough to cause 

sharp changes in periphyton nutrient content. 

Periphyton C:P ratios from the ROCK-01 transplants (a field site with PO4-P < 5 ug/L and 

TP<10 ug/L) responded very strongly to all 3 experimental treatments. On Day 0, ROCK-01 

periphyton had C:P ratios above 2,000. After transplanting these rocks into the BEAR streams 

for just 7 days, mean C:P ratios had dropped to 689, 346, and 215 among the Control, Low P and 

High P treatments, respectively. By Day 28, ROCK-01 Control C:P ratios had dropped to 250, 

whereas Low and High P values were near 150. This illustrated the remarkable affinity of 

stream periphyton for phosphorus, and provided additional evidence in support of for nonlinear 

uptake of P as the explanation for sharp declines in periphyton C:P with small increases in 

surface-water TP. 
R.S. King et al. 2009 10 

In contrast, the NBOS-03 transplants (a field site with PO4-P 50-100 ug/L and TP 75-200 ug/L) 

did not respond to any of the experimental P treatments on Day 7 or 28, supporting the 

hypothesis that recycling of P within the periphyton is an important mechanism for maintaining 

low C:P ratios over time in streams with highly variable surface-water TP. Moreover, it also 

showed that periphyton from NBOS-03 was already saturated with P and thus did not sequester 

more P per unit carbon in the low or high P treatments. 

Algae and Macroinvertebrate Taxonomic Response to P Dosing 

Transplanted algae from ROCK-01 were significantly different among P treatments on Day 28. 

ROCK-01 samples that were transplanted into Low and High P streams shifted significantly 

away from Control streams by day 28, and increasingly resembled algal communities from Penriched field sites. However, consistent with a nonlinear threshold response, algal species 

composition in Low and High P treatments did not differ. In contrast, transplanted algae species 

composition from NBOS-03 did not differ among treatments after 28 days of exposure. 

Indicator Species Analysis revealed that at least 5 diatom species were significantly less 

abundant (or absent) from ROCK-01 transplants in Low P and High P streams than Controls, 

whearas 2 species were significantly more abundant in the Low and High P treatments. Five of 

these 7 taxa also were identified as significant threshold indicators in response to TP gradients in 

the field study, validating the responses as caused by P. All five responded in the same direction 

(decline, increase) in the BEAR study as in the field study. 

Macroinvertebrate taxonomic composition did not differ among treatments on Day 28. The 

relatively short dosing period, in contrast to the relatively long life cycle of these taxa, 

necessarily limited the potential response of this diverse group of organisms to P treatments. 

Future experiments focused on the effect of low flow and P interactions, or longer dosing 

periods, may produce more meaningful results in the context of animal responses to experimental 

P enrichment. 

Conclusions and Recommendations 

Shifts from periphyton communities comprised of sensitive diatoms, calcareous cyanobacteria, 

and other non-chlorophyll bearing microbes to communities with higher chlorophyll content and 

more filamentous green algae was repeatably demonstrated at concentrations of surface-water TP 

above 20 ug/L. Streams with TP > 200-1000 ug/L likely represent a second tier of degradation, 

and appear at greater risk for nuisance algal growth based on our threshold analyses. However, 

results from the P dosing experiment suggest that concentrations as low as 20 ug/L PO4-P can 

lead to high levels of Cladophora biomass in as little as 28 days. Adding more PO4-P (100 ug/L) 

did not result in more Cladophora in a 4-week period, but did result in more Cladophora after 

only 2 weeks. Thus, because faster growth rates were observed in the 100 ug/L treatment, field 

streams with very high levels of TP will have a greater probability of heavy coverage of 

filamentous algae because biomass accumulation between scouring or sloughing events will be 

more rapid. 
R.S. King et al. 2009 11 

Aquatic macrophyte cover consistently declined in streams with TP > 25-50 ug/L. These 

submersed plants serve as important refugia for juvenile fishes and macroinvertebrates, and 

provide a source of dissolved oxygen during low flows. Their decline likely represents key 

structural and functional degradation to these stream ecosystems. 

Minimum dissolved oxygen levels are highly dependent upon an interaction between flow and 

nutrient enrichment. Our study suggests that TP levels > 20-30 ug/L, coupled with low flows, 

will cause detrimental declines in minimum dissolved oxygen levels. This is particularly 

important in the context of minimum flows, a contentious issue in the southwestern USA. It is 

unlikely that studies geared toward detecting effects of nutrients on DO will adequately 

characterize this relationship without sampling during periods of low flow when gas exchange 

with the atmosphere is very slow. Distance downstream and flow status are two very important 

considerations when evaluating the influence of WWTP discharges on wadeable streams in semiarid regions. Future research is needed to better quantify the interaction between minimum 

flows and biological integrity in streams. 

The weight of evidence from both the field stream study and experimental stream study 

demonstrates that streams of the study area are very sensitive to phosphorus enrichment. There 

is a very high probability that streams exposed to surface-water TP levels exceeding 20 ug/L, and 

possibly 15 ug/L, will experience a sharp decline in biological integrity, including loss of 

characteristic structure (periphyton and macrophytes), loss of numerous species (algae and 

macroinvertebrates), minimum dissolved oxygen levels unsuitable for supporting native fauna 

during low flows, and increase likelihood of nuisance algal growth that limits recreational use of 

strreams. Streams exceeding 200 ug/L may represent a second tier of degradation, with more 

consistent nuisance algal growth and additional losses of algal and macroinvertebrate species. 
R.S. King et al. 2009 12 

Table 1. Key to variable short names used throughout this document. 

Variable Description 

SITE_ID 

First 4 letters of site name followed by number (>1 for streams with more than one 

site) 

NH3-N Ammonia-nitrogen, surface water, ug/L 

NO2NO3-N Nitrite + nitrate-nitrogen, surface water, ug/L 

PO4-P Orthophosphate, surface water, ug/L 

TN Total nitrogen, surface water, ug/L 

TP Total phosphorus, surface water, ug/L 

TURB_NTU Turbidity, surface water, NTU 

CHLA_UGL Chlorophyll-a, surface water, ug/L 

C_ALG Total carbon, organic fraction of periphyton, % 

C_BULK Total carbon, bulk periphyton, % 

C_SED Total carbon, sediment fraction of periphyton, % 

N_ALG Total nitrogen, organic fraction of periphyton, % 

N_BULK Total nitrogen, bulk periphyton, % 

N_SED Total nitrogen, sediment fraction of periphyton, % 

P_ALG Total phosphorus, organic fraction of periphyton, % 

P_BULK Total phosphorus, bulk periphyton, % 

P_SED Total phosphorus, sediment fraction of periphyton, % 

CN_ALG Carbon:nitrogen ratio, OM fraction of periphyton 

CN_BULK Carbon:nitrogen ratio, bulk periphyton 

CN_SED Carbon:nitrogen ratio, sed fraction of periphyton 

CP_ALG Carbon:phosphorus ratio, OM fraction of periphyton 

CP_BULK Carbon:phosphorus ratio, bulk periphyton 

CP_SED Carbon:phosphorus ratio, sed fraction of periphyton 

NP_ALG Nitrogen:phosphorus ratio, OM fraction of periphyton 

NP_BULK Nitrogen:phosphorus ratio, bulk periphyton 

NP_SED Nitrogen:phosphorus ratio, sed fraction of periphyton 

CHLA_M2 Chlorophyll a, periphyton, mg/m2 (rock surface area) 

AFDM_M2 Ash-free dry mass, periphyton, g/m2 

AFDM_PCT Percent of dry mass as organic matter 

CHL_AFDM Chlorophyll-a:AFDM ratio, periphyton, mg/g 

DISCHARG Stream discharge, cubic meters/second 

DO_MGL Dissolved oxygen, mg/L 

DO_PCT Dissolved oxygen, % 

PH pH 

SALINITY Salinity, ppt 

SPCOND Specific conductance, uS/cm 

TEMP Water temperature 

CANOPY Tree canopy cover, mean % 

VELOCITY Stream velocity, mean m/s 

THAL_DEP Thalweg depth, mean cm 

WETWIDTH Stream wetted width, mean m 

DEPTH Water depth, mean (100 points) 

FLOW_N No flow velocity detected, % (100pts) 

FLOW_M+ Flow velocity>0.2 m/s, % (100pts) 

SED_CV Sediment index, mean (100pts) 

SED2+ Sediment index > 3, % (100pts) 
R.S. King et al. 2009 13 

SED3+ Sediment index > 2, % (100pts) 

WET_PCT % of reach with surface water (100pts) 

BED_PCT Bedrock, % (100pts) 

BO_PCT Boulder, % (100pts) 

CO_PCT Cobble, % (100pts) 

GR_PCT Gravel, % (100pts) 

GRCO_PCT Gravel+cobble, % (100pts) 

SA_PCT Sand, % (100pts) 

SL_PCT Silt-clay, % (100pts) 

FINE_PCT Silt-clay + sand, % (100pts) 

MACALG_C Filamentous algae cover score, mean (100pts) 

MACALG3+ Filamentous algae cover > 25%, % (100pts) 

MACALG4+ Filamentous algae cover > 50%, % (100pts) 

MACALG5+ Filamentous algae cover > 75%, % (100pts) 

MICALG_R Biofilm thickness, mean (100pts) 

MICALG2+ Biofilm thickness index > 0.5 mm, % (100pts) 

MICALG3+ Biofilm thickness index > 1 mm, % (100pts) 

PLNT_CV Submersed macrophyte cover index, mean (100pts) 

PLNT1+ Submersed macrophyte cover>0, % (100pts) 

PLNT3+ Submersed macrophyte cover>25%, % (100pts) 
R.S. King et al. 2009 14 

PROBLEM DEFINITION AND BACKGROUND

Nutrient pollution is the most common cause of degraded water quality in lakes, streams, 

wetlands, and estuaries in the USA. Excessive inputs of nutrients, specifically phosphorus (P) 

and nitrogen (N), have many negative effects on aquatic ecosystems. One of the most noticeable 

consequences of nutrient pollution is the accelerated growth of aquatic vegetation. Termed 

“eutrophication,” this increase in productivity caused by nutrient enrichment produces an 

undesirable disturbance to the balance of organisms present in the water. For instance, 

eutrophication is commonly associated with explosive growths of nuisance algae that can taint 

drinking water supplies, cause foul odors, and even harm humans. Eutrophication can also result 

in dissolved oxygen shortages that kill fish and other aquatic organisms and reduce aquatic 

biodiversity. Thus, nutrient pollution can significantly limit the ability of a water body to 

support its designated uses, such as fishing, swimming, or other recreational activities. 

Nutrient criteria and total maximum daily load (TMDL) implementation plans are developed by 

States to improve water quality in streams and reservoirs. A TMDL determines how much of a 

particular pollutant can be assimilated by an aquatic ecosystem and still maintain its beneficial 

uses and ecological integrity and is integrally related to a numerical nutrient criterion. In Texas, 

phosphorus has been identified by the Texas Commission on Environmental Quality as the 

nutrient that would have the most effect in limiting algal and plant growth in many central Texas 

streams. TMDL development requires that numerical criteria for the parameters of interest be 

established in order to calculate necessary load reductions. However, to date, numerical nutrient 

criteria have largely been developed subjectively and without knowledge of the biological 

consequences, despite the directive issued by the U.S. EPA in its National Strategy for the 

Development of Regional Nutrient Criteria (1998). In this document, the U.S. EPA detailed a 

comprehensive plan for the development of scientifically defensible, numerical water-quality 

criteria. The plan emphasized the need for the inclusion of endpoints in criteria development that 

reflect physical, chemical, and biological integrity of aquatic ecosystems. However, quantitative 

linkages between critical levels of nutrients and stream biota have not been well developed in 

Region 6, largely because historical sampling protocols do not include relevant ecological 

indicators for streams. 

Another major factor limiting the development of numerical nutrient criteria is the lack of 

experimental evidence to support field monitoring. Experimental studies allow for controlled 

nutrient treatments and, when coupled with field observations, substantially improve our ability 

to establish causation between water quality and biological integrity. Thus, coupling 

experimental evidence with observations from natural streams should allow States to more 

effectively develop defensible nutrient criteria for streams. 

In this study, we employed a novel approach to developing a defensible, effects-based numerical 

target concentration of surface-water phosphorus for wadeable streams in the Cross Timbers 

portion of Aggregate Nutrient Ecoregion IX of Region 6. Our approach relied on an integration 

of collected field data from wadeable streams in Texas with experimental data generated from 

model streams, or stream mesocosms. We used attributes of species assemblages (macrophytes, 

filamentous macroalgae, periphyton and macroinvertebrates) as indicators of biological condition 

in response to observed and experimental phosphorus gradients that spanned multiple aquatic life 
R.S. King et al. 2009 15 

use designations. We evaluated a wide range of biological attributes (e.g., periphyton nutrient 

content and biomass, cover or abundance of nuisance algae, community diversity, ecosystem 

process metrics) that are predicted by the US EPA Tiered Aquatic Life Use (TALU) conceptual 

model and Texas’ narrative aquatic life use categories to be degraded at different stressor 

concentrations along a human disturbance gradient (i.e., exhibit deflections from reference at 

different stressor levels corresponding to aquatic life use categories). Our approach explicitly 

associated numerical levels of phosphorus to threshold responses in ecological indicators of 

aquatic life uses as defined by narrative biological criteria in the TALU framework. 

The approach we employed is consistent with the mandates of the Clean Water Act and the U.S. 

EPA’s National Strategy for the Development of Regional Nutrient Criteria because we (a) 

explicitly address biological integrity and associated aquatic life uses, (b) developed cause-effect 

linkages between nutrient pollution and stream-ecosystem responses by reproducing field 

responses in an experimental setting, (c) identified critical levels of nutrient pollution that caused 

harm to biological integrity and aquatic life uses using a weight-of-evidence approach, and (d) 

estimated the probability of harmful effects associated with levels of nutrient pollution so that 

managers and stakeholders understand the likely ecological implications of selecting target 

nutrient concentrations for water quality standards and future TMDLs for streams. 

The purpose of this document is to report the results of this two-year study. This report is 

structured into two main sections: 

I. Field (observational) study. This section details the study area, methods, results and 

interpretation of 2 years of field data collected from 26 wadeable streams in the Cross 

Timbers Ecoregion of the Brazos River watershed in Texas. These data represent one of 

the most comprehensive evaluations of the effects of nutrient enrichment on wadeable 

streams in the USA. 

II. Experimental stream study. Part 2 of the report details the results of a short-term 

phosphorus dosing study conducted in the Baylor Experimental Aquatic Research 

(BEAR) stream facility. 

The Executive Summary and Conclusions and Recommendations provide a synthesis of findings, 

interpretation, conclusions, and recommendations. 
R.S. King et al. 2009 16 

SECTION I. FIELD STUDY 
R.S. King et al. 2009 17 

STUDY AREA AND STREAM SAMPLING 

Data were collected from 26 wadeable streams in the Brazos River basin within the Cross 

Timbers Level III Ecoregion (Figure 1; Griffin et al. 2004). The Cross Timbers ecoregion 

(Ecoregion 29) is a mosaic of forest, woodland, savanna, and prairie and is currently used mostly 

for rangeland and pastureland (Figure 2, Table 2). 

Watershed variables describing physical characteristics and topography, land use, and 

distribution of disturbance points (outfalls and dams) were calculated for each site (Table 2). 

Watershed boundaries for each sample site were automatically digitized in ArcGIS 9.2 with the 

ArcHYDRO 9 extension using a 1:24,000 scale digital elevation model (DEM) expressed as a 30 

m raster, available from the U. S. Geological Survey. Mean slope and elevation were calculated 

for each watershed using the digital elevation model. Mean annual precipitation was calculated 

for each watershed from a polygon coverage of average monthly and annual precipitation for the 

climatological period 1961-90. This dataset was obtained from USDA-NRCS. Number of 

wastewater outfalls and cumulative outfall (MGD) were calculated for each watershed based on 

the TCEQ municipal and industrial wastewater outfall shapefile available from 

http://www.tceq.state.tx.us/gis/sites.html. Landcover class percentages were calculated for each 

watershed using National Land Cover Database (NLCD 2001) available from 

http://www.mrlc.gov/nlcd_multizone_map.php. All watershed analyses were performed with 

ArcGIS 9.2 (ESRI, Redlands, CA.). 

We collected physical, chemical, and biological data from each of the selected 26 stream 

sampling locations on a quarterly basis from August 2006 through June 2008. Locations were 

permanently marked reaches, defined as 40x the mean stream width, or a minimum of 150-m 

reach (TCEQ 2005). Selected reaches were required to include erosional (riffle/glide) habitats. 

Surface-water nutrient samples (orthophosphate-P [SRP], total phosphorus-P [TP], ammonia-N 

[NH3-N], nitrate-nitrite nitrogen [NO2-NO3-N], total nitrogen [TN]), chlorophyll-a, and turbidity 

were collected from each stream on a quarterly basis. Sampling periods were adjusted slightly to 

ensure sampling was conducted at least 2 weeks following storm flows. SRP, NH3-N, and NO2-

NO3-N samples were collected in triplicate and filtered immediately, whereas triplicate unfiltered 

TP and TN samples were only acidified. Single 1-L chlorophyll-a grab samples were collected 

and filtered in the laboratory within 24 h. Single 50 mL turbidity grab samples were collected 

and analyzed in the laboratory within 24 h. All samples were immediately placed on ice, stored 

in the dark, and transported back to the laboratory and stored at 4°C and analyzed in accordance 

with the approved project plan. 
R.S. King et al. 2009 18 

Figure 1. Map of Cross Timbers ecoregion and field study watersheds in relation to Texas and the Brazos 

River watershed. Twenty-six streams were selected for the field study, which entailed eight quarterly 

sampling events from August 2006 through June 2008. Six of the 26 streams were sampled for algal 

species composition during each of the 8 sampling events; these are shown as intensive sites. The 

remaining streams were sampled twice for algal species composition. All 26 sites were sampled during 

all 8 events for most other predictor and response variables (see Table 1). 
R.S. King et al. 2009 19 

Figure 2. Spatial distribution of dominant land-cover classes among the 26 study watersheds (NLCD 

2001). See Table 1 for percent cover of each land-cover class by watershed. 
R.S. King et al. 2009 20 

Table 2. Physiographic and land cover characteristics among the 26 field-study watersheds. 

SITE_ID 

Precip. (in) 

Elev (m) 

Slope (%) 

Area (km2) 

Dams (n) 

Outfalls (MGD) 

Outfalls (n) 

Water (%) 

Dev. (%) 

Forest (%) 

Shrub (%) 

Grass (%) 

Pasture (%) 

C(%

BLUF-01 33.4 216 2.83 68 0 0.00 0 0.1 0.6 8.8 0.0 69.1 1.4 CORY-01 33.0 201 4.78 220 0 0.00 0 0.0 1.5 26.9 5.3 59.2 1.5 COWH-01 31.4 232 4.53 1180 2 0.06 1 0.1 0.7 18.8 43.0 33.8 1.6 DUFF-01 31.6 297 2.98 157 0 0.00 0 0.2 0.4 22.4 13.1 57.1 1.5 HARR-01 34.4 153 1.48 77 0 0.00 0 0.1 12.0 1.6 0.0 42.6 3.0 3LAMP-01 30.3 331 3.01 720 20 0.00 0 0.3 0.6 10.9 56.6 28.8 1.5 LAMP-02 31.1 271 3.08 1571 21 0.00 0 0.2 0.7 14.0 53.6 29.2 1.0 LEON-01 30.5 304 3.13 4705 141 3.00 10 0.8 3.1 12.2 33.1 33.3 8.4 LEON-02 32.2 226 3.31 6180 152 6.08 12 0.7 2.7 13.5 30.6 37.6 6.8 MBOS-01 33.9 161 2.92 478 0 0.09 1 0.1 1.6 10.5 0.0 63.2 2.0 MERI-01 32.6 198 5.14 480 4 0.04 1 0.4 0.3 32.3 0.4 63.7 0.5 NBOS-01 31.3 366 2.75 257 16 3.50 2 0.6 8.0 11.0 17.6 36.8 12.1 NBOS-02 31.0 324 2.94 489 30 3.50 2 0.6 6.5 10.7 20.1 43.9 8.5 NBOS-03 31.2 302 3.18 925 49 3.50 2 0.5 4.3 13.3 22.6 44.6 7.1 NBOS-04 32.1 220 3.60 1890 69 3.75 4 0.5 3.1 18.4 13.5 55.1 4.1 NBOS-05 33.8 154 4.20 3097 82 5.28 8 0.5 2.5 23.8 8.5 56.2 3.4 NEIL-01 32.9 177 5.20 357 1 0.00 0 0.2 0.2 35.6 1.4 57.7 0.7 NOLC-01 33.6 167 3.27 275 17 33.77 9 0.5 32.4 24.0 7.9 30.2 1.4 NOLR-01 33.6 200 2.35 451 2 6.73 5 1.6 12.3 3.2 0.1 65.7 8.8 PALU-01 32.0 214 6.06 933 43 0.00 0 0.4 1.1 32.9 11.0 49.8 3.1 PLUM-01 31.6 248 3.06 226 0 0.00 0 0.1 0.3 16.8 21.6 53.8 2.3 ROCK-01 31.4 258 2.75 221 0 0.00 0 0.1 1.3 24.3 39.9 34.0 0.1 SALA-01 33.0 198 1.94 215 0 0.25 1 0.1 2.1 27.9 5.2 59.7 0.6 SBOS-01 34.5 144 2.40 220 1 1.10 1 0.2 6.7 3.7 0.0 48.5 4.9 3SLEO-01 30.1 335 4.75 518 4 0.08 1 0.4 0.5 14.7 52.4 26.8 2.6 STEE-01 32.2 210 4.19 127 4 0.00 0 0.8 5.1 30.9 0.0 59.6 0.6 
R.S. King et al. 2009 21 

Diel dissolved oxygen (DO) concentrations were measured from each stream once during 

TCEQ’s (2005) critical index period in 2006 and 2007 using YSI 6600 or 600 XLM Datasondes. 

One datasonde was deployed in shallow glide habitats in each stream reach for at least 48 

consecutive hours in order to characterize diel variability in DO, particularly daily minimums, 

that could be indicative of important functional changes caused by nutrients. Diel dissolved 

oxygen sampling corresponded closely in space and time with surface-water nutrient and 

biological sampling. 

Periphyton, filamentous macroalgae, and aquatic macrophytes were sampled concurrently with 

quarterly nutrient sampling. Two approaches were utilized to assess aquatic vegetation growth. 

The first involved a direct sampling of periphyton occurring on rock (epilithic) substrate for the 

analysis and calculation quantitative measures of biomass per unit area (chlorophyll-a and 

AFDM) and algal species composition. Quantitative periphyton sampling followed the protocols 

of the US Geological Survey’s National Water Quality Assessment (NAWQA) program 

(Moulton et al. 2002) for epilithic substrates using the targeted sampling approach (riffle or glide 

habitat with gravel, cobble, or bedrock). In brief, 5 separate rock samples of attached periphyton 

were collected from each of 5 erosional habitats throughout the reach (n=25 rocks). Surfaces of 

rocks were quantitatively delineated and scraped to remove periphyton. Material from the rocks 

was composited and quantitatively subsampled (aliquots) to determine chlorophyll-a, AFDM, 

and number of cells per species per unit area. Additional subsamples were taken for elemental 

analysis to estimate percent carbon, nitrogen, and phosphorus and corresponding ratios of these 

three elements as indicators of nutrient availability and enrichment. Methods are detailed in 

Back et al. (2008) and Scott et al. (2008). 

The second approach utilized the modified version of Hawkins et al. (2001) vegetation cover 

protocols, which yielded a reach-wide assessment of aquatic macrophyte, macroalgae, and 

periphyton percentage cover and thickness of biofilm cover. The method entailed walking a zigzag transect from one end of the reach to the other and estimating vegetation cover at each of 100 

equally spaced points located along the transect. The protocol was modified to include an index 

of sediment film thickness on stream substrate (sediment cover index), dominant substrate 

(percent of different particle sizes in the reach), an estimate of flow velocity (flow index), and 

water depth. 

Periphyton taxonomic composition was measured at each of the 26 streams during August 2006 

and September 2007. Taxonomic data were obtained from 10-ml subsamples from the 

quantatitive periphyton composite sample. Samples were collected, homogenized, preserved, 

and identified in accordance with taxonomic methods for non-diatom and diatom algae described 

in TCEQ (2005). One non-diatom and one diatom taxonomic sample was identified per stream 

per year. At least 500 diatom and 300 non-diatom cells per respective sample were identified 

(TCEQ 2005). Dr. Barbara Winsborough, an expert periphyton taxonomist from central Texas, 

performed all of the species identifications in accordance with the approved project plan. 

Six of the 26 streams were sampled for algal species composition on each of the 8 quarterly 

sampling events (see Figure 1, “intensive” sites). These six sites were chose because they were 

widely separated throughout the study region and spanned a steep gradient of nutrient 

enrichment. 
R.S. King et al. 2009 22 

Benthic macroinvertebrate densities and taxonomic composition were measured in November 

2006. Macroinvertebrates were sampled quantitatively using a Hess sampler from the same 5 

erosional habitats sampled quantitatively for periphyton within the reach. Four Hess samples 

were collected from each of the 5 locations within the reach (n=20). Samples were composited, 

homogenized, and preserved in 5% (v/v) buffered formalin stained with rose bengal. 

A two-phase macroinvertebrate subsampling approach (King and Richardson 2002) was 

employed in the laboratory. The first phase was a comprehensive removal of all large-bodied 

taxa (organisms easily seen with the naked eye). The second phase was quantitative removal of 

a minimum of 500 individuals (fixed count method) using a gridded, numbered pan and random 

numbers table. All organisms will be removed from each randomly selected grid until enough 

grids have been sorted to achieve >500 individuals (Barbour et al. 1999), or the entire sample 

contents have been sorted, whichever came first. Total number of grids subsampled will allow 

estimation of densities (no/m2; King and Richardson 2002). 

Macroinvertebrates were identified in the laboratory using operational taxonomic units, usually 

genus or species for most taxa. A voucher collection of specimens is maintained in the Center 

for Reservoir and Aquatic Systems Research (CRASR). 

Physical habitat, flow, substrate, riparian attributes, and basic water chemistry of each reach 

were characterized according to methods outlined in TCEQ (2005). Cross-sectional sampling 

transects were established as described in TCEQ (2005). Discharge (cfs) was measured each 

quarter along a representative transect within each reach using a portable electromagnetic flow 

meter (Marsh-McBirney Flo-Mate Model 2000). 

DATA ANALYSES 

We estimated potential threshold responses in the measurement endpoints to numerical levels of 

nutrients using nonparametric changepoint analysis (nCPA), a technique explicitly designed for 

detecting threshold responses using ecological data (King and Richardson 2003, Qian et al. 

2003). This analysis is based on the fact that structural change in an ecosystem may result in a 

change in both the mean and the variance of an ecological response variable used to indicate a 

threshold. When observations are ordered along an environmental variable (gradient), a 

changepoint is a value that separates the data into the two groups that have the greatest difference 

in means and/or variances. This can also be thought of as the degree of within-group variance 

relative to the between group variance, or deviance (D). Analytically, the nCPA examines every 

point along the stressor gradient and seeks the point that maximizes the reduction in deviance. 

There is one particular value of the predictor y (e.g, TP) that maximizes the reduction in deviance 

in the response data (in this case, the selected biological responses); however, there is uncertainty 

associated with that value. It is unlikely that any one value of the predictor (e.g., TP) is the only 

value that could represent a changepoint. In reality, depending on the acuteness of the biological 

change in response to TP, several observations of TP could represent the changepoint, each with 

varying probabilities. Thus, to assess the risk associated with particular levels of TP, nCPA 
R.S. King et al. 2009 23 

incorporates estimates of uncertainty in the changepoint (King and Richardson 2003). These 

estimates are calculated using a bootstrap simulation. This simulation resamples (with 

replacement) the original dataset and recalculates the changepoint with each simulation. 

Bootstrap simulations are repeated 1,000 times. The result is a distribution of changepoints that 

summarizes the uncertainty among multiple possible changepoints. This uncertainty is expressed 

as a cumulative threshold frequency based on the relative frequency of each changepoint value in 

the distribution. 

Important gradients in algal and macroinvertebrate species compostion were identified using 

non-metric multidimensional scaling (nMDS). NMDS is a distance based procedure that 

ordinates study units based on rank dissimilarities (Minchin 1987, Clarke 1993, Legendre and 

Legendre 1998). We used Bray-Curtis dissimilarity (BCD) as the distance measure, a coefficient 

that has been repeatedly demonstrated to be robust for ecological community data (Faith and 

Norris 1989). A two-dimensional solution was used for all analyses as stress values (a measure 

of agreement between BCDs and the configuration of the ordination) were relatively low and did 

not substantially decrease when additional axes were included in ordinations. Before running 

ordinations on the data sets, algae or macroinvertebrate species occurring at only two sites within 

a data set were excluded, and abundances were log transformed. Variables from the watersheds 

and environmental measurements with high skewness (> 1) were also log transformed to 

improve linear relationships with the ordinations. Ordinations were performed in PC-Ord version 

5.20 (MjM Software, Gleneden Beach, OR, U.S.A.). 

We used rotational vector fitting to relate environmental and watershed variables to gradients in 

algal and macroinvertebrate community composition quantified by the NMS ordinations (Faith 

and Norris 1989). Vector fitting was used to find the direction of the maximum correlation for 

each environmental variable. Significance (P ≤ 0.05) of each environmental vectors was 

estimated using 1,000 random permutations of the data. Vector fitting was performed using the 

ECODIST package in R version 2.5.1 (© 2007, The R Foundation for Statistical Computing). 

For species abundance thresholds, we employed a new analytical approach, Threshold Indicator 

Taxa ANalysis (TITAN; Matthew E. Baker and Ryan S. King), with the goals of (1) exploring 

and identifying abrupt changes in both the occurence frequency and relative abundance of 

individual taxa along nutrient gradients, (2) quantifying uncertainty associated with both 

observed distributions of each taxon and the broader sample, and (3) estimating the relative 

synchrony of those changes as a non-parametric assessment of a community threshold. Current 

statistical methods used for grouping samples and detecting community ecological thresholds are 

not developed for distinguishing responses of individual taxa with low occurrence frequencies or 

highly variable abundances (Dufrêne and Legendre 1997, Brenden et al. 2008, Andersen et al. 

2008). Some methods assume a linear, univariate response along all or part of an environmental 

gradient (e.g., Toms and Lesperance 2003), whereas others focus solely on aggregate, 

community-level dissimilarity (e.g., De’Ath 2002, King et al. 2005) or species turnover between 

samples (i.e., beta-diversity). Noisy, non-linear, and poorly distributed occurrences are typical 

properties of the vast majority of taxa in multivariate community data matrices (McCune and 

Grace 2002). Multivariate or multi-metric analysis can obscure distinct responses of taxa subsets 

in a community data set, especially if both predominant and rare species do not respond in a 
R.S. King et al. 2009 24 

similar fashion or focal species do not respond as expected. TITAN circumvents most of these 

problems. 

TITAN represents a combination and extension of change-point and indicator species analysis. 

In TITAN, we use normalized indicator species taxa scores (z) to identify the value of a 

continuous variable, x, resulting in the optimal partitioning of sample units, such that the 

indicator score is maximized either for individual taxa or the additive response of all normalized 

indicator z -scores at the community level. Negatively responding taxa (z–) are distinguished 

from those responding positively (z+) to yield taxa-specific change-point distributions as well as 

cumulative responses of declining [sum(z–)] and increasing [sum(z+)] subsets of the community. 

Resampling procedures are used to measure both indicator reliability and purity, and to estimate 

uncertainty surrounding the existence of community change-points. 

TITAN analysis was performed on the same species data sets as in the ordinations using logtransformed abundance data. Predictors included important nutrient variables identified from the 

environmental vector fitting analysis. TITAN was conducted in R version 2.5.1 (© 2007, The R 

Foundation for Statistical Computing) using the custom package TITAN written by M. E. Baker 

and R. S. King (Baker and King, accepted with revision; King and Baker, in review). 
R.S. King et al. 2009 25 

RESULTS AND INTERPRETATION 

Temporal Patterns in Discharge and Nutrient Concentrations

The 26 selected streams spanned a steep gradient of phosphorus enrichment, which is critical for 

identifying numerical thresholds. Sites with low, moderate, and high levels of P enrichment 

were widely distributed throughout the study area. This latter point was important because our 

goal was to avoid spatial “clusters” of sites that had similar levels of enrichment. Spatial 

clustering in a predictor variable can lead to erroneous conclusions about the effect of the 

predictor (e.g., TP) on biological responses because other factors such as biogeography, geology, 

or other unmeasured variables may coincidently correspond to the predictor variable (e.g., King 

et al. 2005). Our study design avoided such spatial problems, capturing the full range of natural 

variability in geology, drainage networks, stream size, etc., while still achieving a heterogeneous 

arrangement of P enrichment and its likely sources (WWTP outfalls, pasture). 

Figure 3. Spatial distribution of surface-water total phosphorus (TP, ug/L) and periphyton carbon-tophosphorus (C:P, bulk) ratio during the first quarterly sampling event, August 2006. 
R.S. King et al. 2009 26 

Streams throughout the study area had similar discharge patterns during the study period (Figure 

4). Differences in discharge were much greater among seasons and years than among the 

individual streams (also see Figure 6). 

Stream discharge was very low in summer and fall 2006 and early winter 2007. This was a 

period of near-record drought, causing flow to cease or nearly so in most of the 26 streams 

during November 2006. This provided an excellent opportunity to evaluate the effects of P 

enrichment on biological responses under low-flow conditions. 

Heavy precipitation in spring and summer 2007 led to very high discharges and numerous 

scouring events. May 2007 (#4 in Figure 4) was completed during a brief period of stream-flow 

recession. During this event, 8 of the 26 streams were unwadeable so some variables were not 

sampled at those sites on that date. More rain and flooding continued through August. 

Streams finally began to recede in September 2007 (#6 in Figure 4), allowing us to reinstate our 

quarterly sampling schedule. However, baseflows remained much higher than 2006. 

February 2008 (#7 in Figure 4) corresponded to an extended period of normal to above-normal 

winter baseflows, without any major scouring events in the preceding few months. However, 

rains and flooding came back in March through May, thus the final sampling event (June 2008) 

followed heavy flows and scouring. 

Figure 4. Hydrographs of four of the 26 study streams spanning a period before, during, and after the 

field study (data acquired from www.usgs.gov). The gray-shaded bars represent the sampling window 

corresponding to each of the 8 quarterly sampling events: 1=August 2006; 2=November 2006; 

3=February 2007; 4=May 2007; 5=September 2007; 6=December 2007; 7=February 2008, and 8=June 

2008. 

 

1 2 3 4 567 8
R.S. King et al. 2009 27 

Figure 5. Photograph of Salado Creek (SALA-01) during August 2006, illustrating the typical channel 

and bank morphology of the region. 

All of the study streams had streambeds comprised predominantly of calcareous gravel, cobble, 

boulders, and bedrock (e.g, Figure 5). Streams had short reaches of erosional (riffle) habitat 

comprised mostly of larger substrate (gravel and cobble). Erosional areas were interspersed by 

typically longer sections of either shallow, low gradient glides underlain by bedrock, or deeper, 

depositional pools that often were associated with limestone bluffs on the erosional side of the 

stream channel. This particular photo reveals a moderate amount of calcareous periphyton and 

patches of bright green filamentous algae, a common pattern seen in sites with low nutrient 

levels during extended periods of low flow. Nutrient-enriched sites typically had a thinner films 

of dark-colored periphyton as well as dark-green filamentous algae (mostly Cladophora). 
R.S. King et al. 2009 28 

Instantaneous discharge measurements revealed that the hydrology of the 26 study streams was 

quite similar over time (Figure 6). All streams except for the 4 sites immediately downstream of 

significant effluent discharges (LEON-02, NOLC-01, NOLR-01, NBOS-01) exhibited a pattern 

of very low flow in August and November 2006, with a slight increase in February 2007 due to 

low evapotranspiration and increased groundwater discharge. However, the heavy rains and 

flooding of spring 2007 pushed discharge upward and maintained a relatively high baseflow 

through the end of the study in June 2008. 

Figure 6. Temporal patterns of stream discharge (cfs) and surface water total phosphorus (TP, ug/L) 

among the 26 field sites, August 2006-June 2008. Both variables are plotted on a log-scale. 

Surface-water TP, however, varied more by streams than by date of measurement. Streams with 

apparently few point or non-point sources of phosphorus (e.g., ROCK-01, NEIL-01, SALA-01, 

COWH-01, etc) exhibited consistently low TP, seen as relatively flat lines over time. Streams 

with predominantly non-point sources of phosphorus (watersheds with pasture or other sources) 

showed sharp increases in TP during high discharge, reflecting transport of P from the land from 

surface-water runoff. Streams with point sources (effluent discharges) either had consistently 

high TP (NOLC-01, NBOS-01) or were diluted by high surface-water runoff (LEON-02, NOLR01, SLEO-01). 
R.S. King et al. 2009 29 

Figure 7. Temporal patterns of surface water orthophosphate (PO4-P, ug/L) and dissolved inorganic 

nitrogen (DIN) among the 26 field sites, August 2006-June 2008. Both variables are plotted on a logscale. 

Patterns in PO4-P among sites over time (Figure 7) were similar to that of TP, particularly for 

sites strongly influenced by point-source effluent discharges (NOLC-01, NOLR-01, NBOS-01, 

LEON-01, SLEO-01), as most of the TP at these sites was in the form of PO4-P. 

Orthophosphate comprised a smaller fraction (30-70%) of the TP at sites influence mostly by 

nonpoint sources (CORY-01, DUFF-01, PALU-01, LEON-01, etc). Orthophosphate represented 

a very small fraction of the TP at the sites with the fewest sources of phosphorus, and was often 

at or near the minimum detectable limit of 1-2 ug/L. 

Dissolved inorganic nitrogen behaved much differently than PO4-P over time. During winter 

and spring of 2007, sites with rowcrop in their watersheds exhibited very high fluxes of DIN 

(mostly NO3-N). These sites tended to have consistently high DIN all year, but high 

concentrations were particularly evident as dissolved forms of N were transported from fields 

(mostly corn) via ground and surface water runoff to the streams (e.g., BLUF-01, CORY-01, 

HARR-01, MBOS-01, SBOS-01 NBOS-05). These high-DIN sites were important benchmarks 

because a few of them had low levels of TP (e.g., MBOS-01), revealing that biological variables 

did not respond to elevated DIN without a coincident subsidy of phosphorus (i.e., stream 
R.S. King et al. 2009 30 

ecosystem processes were strongly limited by P, and additions of N did not have a detectable 

effect on the streams unless additional P was available). 

Figure 8. Temporal patterns of periphyton C:P ratio (bulk) and periphyton N:P ratio (bulk) among the 26 

field sites, August 2006-June 2008. 

We hypothesized that levels of enrichment of surface-water nutrients that were important would 

be reflected in enrichment of the tissues of the periphyton. Sites with elevated P, N or both 

should have more enriched periphyton P and/or N if either element was limiting its growth. 

Figure 8 illustrates that indeed sites differed markedly in their periphyton C:P and N:P ratios. 

Contrary to surface-water concentrations, low values of C:P or N:P reflect greater enrichment

(i.e., more P per unit carbon or nitrogen in the periphyton = lower C:P or N:P ratio). 

What is particularly compelling about these results is that sites with elevated surface-water TP 

had virtually constant C:P or N:P ratios in the periphyton. Moreover, despite the high variability 

of DIN in surface waters among sites over time, the periphyton N:P ratio was essentially 

identical to the C:P ratio, illustrating that P, not N, was driving differences in these ratios. 
R.S. King et al. 2009 31 

Figure 9. Scatterplot of surface-water TP (ug/L) versus periphyton C:P ratio (bulk) for each of the 8 

sampling events, 2006-2008. Each point represents one of the eight dates of sampling. Surface-water TP 

is plotted on a log scale. 
R.S. King et al. 2009 32 

Figure 9 illustrates more directly the strong influence of surface-water TP on periphyton P 

content. There appear to be 3 types of relationships in this graph: 

1. Sites with consistently low concentrations of surface-water TP (5-15 ug/L; e.g., NEIL-01, 

ROCK-01, SALA-01, STEE-01, etc) had highly variable periphyton C:P ratios. Importantly, 

these ratios almost always remained above 500. This showed that the periphyton was 

strongly limited by P, and variability in its P content was likely due to subtle seasonal 

differences in availability (either via recycling or pulses of low enrichment), taxonomic 

structure, and/or accumulation of biomass. 

2. Sites with consistently moderate levels of TP (15-25 ug/L) had much less variability in the 

C:P ratios of the periphyton (e.g., BLUF-01, CORY-01, LAMP-01, HARR-01, SBOS-01) 

than sites with TP consistently < 15 ug/L. Moreover, these sites consisently had C:P ratios 

below 500, often below 200. This suggested that subtle increases in P availability resulted in 

a sharp, nonlinear response in the periphyton, with rapid uptake and storage of P. 

3. Sites with consistently high levels of TP (e.g., effluent discharge sites such as NBOS-01, 

NOLC-01, NOLR-01), or with a wide range of levels of TP over time (e.g., SLEO-01, 

LEON-02, NBOS-02, NBOS-03) had consistently low (<200) periphyton C:P ratios, but 

these C:P ratios were not markedly lower than sites with consistently moderate levels of TP. 

This suggested that the periphyton was P-saturated (or nearly so) when exposed to low level 

P enrichment, and that temporal variability in surface-water TP did not correspond to 

variability in periphyton C:P because of its ability to store P during periods of high 

availability, and recycle P when it is less available. 

In summary, these results imply that (1) periphyton was strongly limited by P, (2) it was very 

efficient at sequestering and recycling P, and (3) its C:P ratios were low and similar among sites 

ranging from subtle but consistent TP enrichment to consistently high levels of TP enrichment. 

The next section of this report deals directly with quantification of levels of surface-water TP 

that result in thresholds, or nonlinear changes, in periphyton nutrient content and the implications 

of this enrichment on other biological response variables. 
R.S. King et al. 2009 33 

RESULTS AND INTERPRETATION, CONTINUED 

Biological Responses to TP Gradients 

The series of subfigures in Figure 10 effectively reveals the strikingly strong nonlinear 

relationship between surface-water TP and periphyton nutrient content. These figures use all of 

the data combined to show the remarkable consistency of the response, regardless of season or 

site. Threshold declines in periphyton C:P, N:P, and C:N ratios were highly likely between 15 

and 25 ug/L TP, with 19-20 ug/L representing the most consistent concentration yielding a 

threshold response. 

Note that C:N also declines with TP at the same threshold; this is important because it shows that 

as the periphyton is relaxed from its P limitation, it can also utilize the subsidy of nitrogen 

available in the streams. This pattern is not evident when C:P, N:P, and C:N are plotted against 

DIN or TN (see Figure 16). 

Significant threshold changes were detected in biofilm thickness, filamentous macroalgal cover, 

and submersed macrophyte cover in response to TP, even when combining all of data from each 

site across the 8 sampling dates (Figure 11). 

The thickness of microbial films (microalgae and other microbes; non-filamentous algae) 

growing on gravel, cobble, boulders, or bedrock showed consistently nonlinear declines in 

response to TP levels above 20 ug/L. This metric was computed as the percentage of 

observations in a reach (out of a maximum of 100 points along the reach-scale transect; see 

Methods) in which biofilms exceeded 1 mm in thickness on rocks. If no rocks were present at a 

point, or if the channel was too deep at a particular point to collect a rock, no measurement was 

recorded. Heavy accumulation of calcareous periphyton was a consistent trait of low-P streams 

(see Figure 12a). Streams above the threshold value of 20 ug/L usually had a thinner, darker 

layer of biofilms on the rocks (Figure 12b) but also tended to have more filamentous green algae 

(see next). 

The percentage of points along the reach-scale transect that had filamentous macroalgae cover 

exceeding 75% of the substrate was also responsive to surface-water TP, but contrary to biofilm 

thickness, filamentous algae increased in response to TP (Figure 11). There were actually 2 

distinct changepoints in filamentous algal cover: a consistent increase in cover among sites at 

about 20 ug/L TP, and a more diffuse but dramatic increase in cover at very high levels of TP 

(200-1000 ug/L). Because filamentous algal cover depends highly on seasonal changes in light 

and water temperature, as well as sloughing from overgrowth and scouring from high flows, it is 

not surprising that many of the highly enriched sites had little filamentous algal cover on some 

dates. However, when high filamentous algal cover occurred, it was almost always associated 

with levels of P enrichment > 20 ug/L, and particularly at >200 ug/L. 
R.S. King et al. 2009 34 

Figure 10. Results from nonparametric changepoint analysis using surface-water TP as a predictor of 

threshold changes in periphyton nutrient ratios across all 26 sites over the full study period. Bulk 

represents ratios of carbon, nitrogen, and phosphorus that were estimated using homogenized periphyton 

that was not subjected to the centrifugation separation. The OM (organic matter) ratio represents the 

fraction of homogenized periphyton that was separated from inorganic sediment using the centrifugation 

method described in Scott et al. (2008). Each blue dot represents one of the 26 sites, and each site is 

represented 8 times in the analysis, once for each sampling date. The gray vertical line is the observed TP 

threshold (the level of TP resulting in the greatest difference in the response variable to the left and right 

of that value). The dotted red line is the cumulative threshold frequency, an estimate of uncertainty based 

on 1,000 bootstrap samples of the data (see King and Richardson 2003). The cumulative threshold 

frequency illustrates the range of possible threshold values; different quantiles of this distribution can be 

interpreted as confidence intervals around the observed threshold. See Table 3 for summary of the 

corresponding statistical results.

C:P (OM)

C:P (bulk) C:N (bulk)

N:P (bulk)

Surface‐water Total Phosphorus vs.

Periphyton NutrientContent(2006‐2008)
R.S. King et al. 2009 35 

One of the most important patterns that emerged from the field study was the sharp threshold 

declines in submersed macrophyte cover in response to TP >25-50 ug/L. The percentage of 

points along the reach-scale transect that had either some cover of macrophytes (top right panel, 

Figure 11) or cover exceeding 25% of the substrate (bottom right panel) both strongly declined 

in response to TP. The two dominant submersed macrophyte taxa were Najas (a vascular plant) 

and Chara (a nonvascular macroalga that is functionally equivalent to a vascular macrophyte). 

Other taxa less frequently encountered included Potamogeton and Nitella. 

The mechanism for decline of these species may be related to filamentous algal growth on the 

plants themselves, which is a common problem in other systems experiencing eutrophication 

(e.g., Chesapeake Bay). Chara has also been shown to decline in response to TP levels above 30 

ug/L in the Everglades (King et al. 2004, Richardson 2008), which is possibly related to changes 

in speciation of dissolved inorganic carbon with diel swings in pH associated with P-stimulated 

photosynthesis by algae. Regardless of mechanism, it is clear that P enrichment is associated 

with a significant negative change in an important structural component of these streams. 

Figure 11. Results from nonparametric changepoint analysis using surface-water TP as a predictor of 

threshold changes in biofilm (non-filamentous periphyton) thickness on rocks, filamentous algae cover, 

and submersed macrophyte cover across all 26 sites over the full study period. See Figure 10 for other 

graphical details and Table 3 for statistical summaries corresponding to the figure.

Filamentous

algae cover

>75%

Macrophyte

cover > 0 Biofilm

thickness

Macrophyte

cover > 25%

Surface‐water Total Phosphorus vs.

Biofilm,Macroalgae, and Macrophyte Cover(2006‐2008)
R.S. King et al. 2009 36 

 

Figure 12. Photographs of typical periphyton (biofilm) color and thickness from sites below the TP 

threshold of <15-20 ug/L (panel A) and above the threshold (panel B). In panel A, the periphyton is 

mostly diatoms, calcareous cyanobacteria, and other microbes (bacteria and fungi), with little obvious 

filamentous green algae. In panel B, the periphyton is comprised of a thinner veneer of mostly diatoms 

and bacteria, but the overall biomass does not change markedly because of the increase in colonial and 

filamentous green algae. This shift in structure becomes evident in the next series of figures. 

A 

B 
R.S. King et al. 2009 37 

The preceding pair of photographs in Figure 12 places the results presented in Figure 13 into 

context and aids in their interpretation. Periphyton ash-free dry mass was highly variable in 

response to TP, and showed a weak decline in response to TP levels ranging from as low as 10 

ug/L to about 50 ug/L. Because total ash-free dry mass combines all types of attached 

periphyton (diatoms, cyanobacteria, other microbes and filamentous/colonial green algae), it is 

intuitive AFDM might not respond as sharply as biofilm thickness (decline) or filamentous algal 

cover (increase), because AFDM combines both. Nevertheless, the pattern of decreasing biofilm 

thickness does result in a significant decline in AFDM, although the threshold level of TP is 

relatively uncertain. 

Periphyton chlorophyll-a tended to track the pattern in % filamentous algal cover shown in 

Figure 11, with possibly 2 changepoints (20 and 200 ug/L), each leading to higher levels of 

chlorophyll-a. However, this result is also relatively uncertain and noisy because diatoms and 

cyanobacteria characteristic of low-P sites also contain chlorophyll. 

Figure 13. Results from nonparametric changepoint analysis using surface-water TP as a predictor of 

threshold changes in periphyton ash-free dry mass (AFDM, g/m2

), periphyton chlorophyll-a (ChlA (peri); 

mg/m2

), surface-water chlorophyll-a (ChlA (water); ug/L), and the ratio of periphyton ChlA to periphyton 

AFDM (ChlA:AFDM; mg/g) across all 26 sites over the full study period. See Figure 10 for other 

graphical details and Table 3 for statistical summaries corresponding to the figure. 

ChlA:

AFDM

ChlA

(water)

AFDM

ChlA

(peri)

Surface‐water Total Phosphorus vs.

Periphyton and Phytoplankton Biomass(2006‐2008)
R.S. King et al. 2009 38 

The ratio of periphyton chlorphyll to AFDM did a much better job of revealing the shift in 

structure of the periphyton in response to P. This relationship (bottom left panel) reveals a clear, 

nonlinear shift toward a greater fraction of chlorophyll-bearing organisms in the periphyton in 

response to TP > 20-30 ug/L. A secondary threshold appears to be near 200 ug/L, coincident 

with the 2-tiered response of filamentous algae and chlorophyll-a. 

Most primary production in wadeable streams is a result of periphyton and macroalgae rather 

than phytoplankton. However, wadeable streams in Texas typically have lower flows and thus 

longer residence times than wadeable streams in more mesic parts of North America. Longer 

residence times allow for some potential for phytoplankton blooms in response to TP. We 

observed a consistent nonlinear increase in surface-water chlorophyll-a at 20 ug/L TP. Streams 

with high TP did not consistently have high chlorophyll-a, but when high levels of chlorophyll 

were observed, TP was also elevated above 20 ug/L. This is somewhat circular, because TP is an 

unfiltered, digested sample that includes particulates such as phytoplankton. Thus, TP will 

necessarily increase if high chlorophyll is observed. It is also likely that much of the chlorophyll 

observed in the water column was from attached algal cells that had sloughed off of rocks. 

Fine sediment runoff and deposition into streams may be an important contributor of non-point 

source phosphorus (Figure 14): C:P ratios in sediment that was separated from organic matter in 

the homogenized periphyton showed a strong nonlinear relationship to TP. It is likely that this 

relationship was at least partially caused by P-enriched organic matter (periphyton) that remained 

attached to sediment particles, but it also suggests that phosphorus bound to clay particles may 

be enriching the periphyton. 

Streams with >20% of the channel dominated by silt or clay substrate were consistently 

associated with TP > 20 ug/L. Sediment-impacted streams often had high bank erosion, 

relatively high percentages of pasture in their watersheds, and usually had evidence of cattle 

activity in the stream channel and banks. However, many of the streams with TP>20 ug/L did 

not have obvious sedimentation problems, thus sedimentation alone was not the driving factor in 

these relationships. Rather, this suggests that sediment-bound P may have been an alternative 

source of enrichment that was driving the strong nonlinear biological responses. 

The frequency of sediment cover classified as sufficiently heavy to obscure the color of 

periphyton (Sed>3) corresponded closely to the percentage of the reach dominated by fine siltclay substrate. It also showed a sharp increase at 20 ug/L TP. This particular index was a direct 

measure of the potential effect of sediment on periphyton because it was a qualitative index of 

sediment film thickness on rock substrates. Again, not all streams with high TP had high 

sediment-index scores, suggesting that sediment alone was not the driver of threshold responses 

in biological endpoints. 

Turbidity response to TP paralleled the responses of silt cover and the sediment index, sharply 

increasing at 20 ug/L TP. TP is partially dependent upon turbidity, however, because greater 

particulates in the water will necessarily lead to higher TP, assuming the particulates have P 

bound to them (clay) or the particulates are organic (detritus, phytoplankton). 

 
R.S. King et al. 2009 39 

Figure 14. Results from nonparametric changepoint analysis using surface-water TP as a predictor of 

threshold changes in (1) periphyton C:P ratio (sediment fraction); (2) the percentage of points in the reach 

dominated by silt-clay substrate; (3) the percentage of points in the reach in which substrate had a heavy 

film of sediment (sediment index>3, where 3=sufficiently heavy to obscure color of periphyton); and (4) 

surface-water turbidity (NTU) across all 26 sites over the full study period. See Figure 10 for other 

graphical details and Table 3 for statistical summaries corresponding to the figure. 

% silt‐

clay

Sed

>3

C:P (sed)

Turbidity

Surface‐water Total Phosphorus vs.

Fine Sediment P, Cover, and Turbidity (2006‐2008)
R.S. King et al. 2009 40 

Numerical threshold responses of most of the measured water quality and biological endpoints 

from the the field study for all dates combined, 2006-2008 are presented in Table 3. The 

observed threshold value (shown as the gray vertical bar in Figures 10-14) is highlighted here in 

bold. The P-values represent the probabilities of Type I error (i.e., the probability of concluding 

that there is a threshold when in fact there is not) for each response to TP. Importantly, the 

cumulative threshold quantiles are narrow for most of the responses, with the 10% and 50% 

values corresponding closely to the observed value. The 90% quantile occasionally spiked up to 

near 200 ug/L for variables that were strongly influenced by filamentous algae, a response 

variable which likely had changepoints at ~20 and ~200 ug/L TP. 

Table 3. Results of nonparametric changepoint analysis using surface-water TP as a predictor of 

threshold responses in selected biological and water quality response variables across 26 sites and 8 

quarterly sampling events during 2006-2008. See Table 1 for full names and descriptions corresponding 

to variable IDs, and figures 10 through 14 for graphical display of most of these results. 

Cumulative Threshold 

Quantiles 

Mean variable 

value 

Variable ID 

TP 

(ug/L) Response P value 10% 50% 90% Below Above 

AFDM_M2 18.3 Decline 0.0027 9.4 10.2 163.9 5.30 3.30

CHLA_UGL 19.2 Increase <0.0001 15.5 19.2 28.3 0.81 3.86

CHL:AFDM 27.6 Increase <0.0001 27.5 40.7 239.8 2.05 3.57

CHLA_M2 186.0 Increase 0.0042 12.0 57.6 239.8 7.53 19.88

CN_ALG 20.1 Decline 0.0001 12.0 20.1 46.5 14.98 12.18

CN_BULK 26.2 Decline <0.0001 15.6 25.4 46.5 20.43 12.91

CP_ALG 24.5 Decline <0.0001 12.1 24.5 32.9 564.95 259.96

CP_BULK 19.8 Decline <0.0001 12.2 19.8 26.8 768.59 260.21

NP_ALG 28.3 Decline <0.0001 22.7 32.9 174.5 34.59 19.60

NP_BULK 27.5 Decline <0.0001 19.8 27.5 40.7 31.49 14.01

MACALG5+ 554.7 Increase 0.0361 15.7 250.1 554.7 2.93 5.75

MICALG3+ 21.1 Decline 0.0005 18.2 21.0 174.7 6.24 2.48

PLNT1+ 46.5 Decline 0.0004 10.2 36.1 46.8 4.12 1.82

PLNT3+ 46.5 Decline 0.0014 25.9 46.5 46.8 2.39 1.31

SED3+ 12.8 Increase <0.0001 10.8 12.8 18.4 5.78 20.85

TURB_NTU 18.0 Increase <0.0001 15.5 17.8 19.8 1.08 4.24
R.S. King et al. 2009 41 

Orthophosphate (PO4-P) was also a strong predictor of periphyton nutrient content. The 

relationships depicted in Figure 15 are very similar to the TP thresholds in Figure 10, but perhaps 

slightly more nonlinear. Virtually no sites with >18 ug/L PO4-P had bulk C:P ratios above 300 

(upper left panel). This is further evidence for nonlinear uptake of P by periphyton, reaching a 

saturation point at the observed PO4-P threshold. 

One disadvantage of PO4-P relative to TP as a predictor, however, is that it can be rapidly 

depleted during daylight when photosynthesis is active. Thus, some sites with low PO4-P are 

nevertheless highly enriched with P, shown by the low C:P ratios. TP is less variable and thus is 

a better indicator of P enrichment, but is still influenced by diel variability in photosynthesis and 

uptake. A major conclusion from this PO4-P result is that when there is a subsidy of 

bioavailable phosphorus (dissolved orthophosphate), it often indicates that the periphyton is near 

or beyond its point of saturation, thus excess dissolved P remains in the water column. 

Figure 15. Results from nonparametric changepoint analysis using surface-water orthophosphate as a 

predictor of threshold changes in periphyton nutrient ratios across all 26 sites over the full study period. 

Bulk represents ratios of carbon, nitrogen, and phosphorus that were estimated using homogenized 

periphyton that was not subjected to the centrifugation separation. The OM (organic matter) ratio 

represents the fraction of homogenized periphyton that was separated from inorganic sediment using the 

previously described centrifugation method. 

C:P (OM) C:N (bulk)

C:P (bulk) N:P (bulk)

Surface‐waterOrthophosphate (PO4‐P) vs.

Periphyton NutrientContent(2006‐2008)
R.S. King et al. 2009 42 

There were significant changes in periphyton C:P and N:P ratios in response to dissolved and 

total nitrogen, but the relationships were weak and noisy (Figure 16). Most of the signal in these 

relationships were driven by the covariation between P and N in effluent discharges (i.e., high P 

and N, but the periphyton was responding to the P), and the noise in the relationship is attributed 

to sites with high amounts of rowcrop in their watersheds but little pasture or effluent discharges 

(low P but high N, thus no response of the periphyton to the N because it was limited by P). 

Figure 16. Results from nonparametric changepoint analysis using surface-water dissolved inorganic 

nitrogen (DIN, ug/L) and total N (TN, ug/L) as a predictor of threshold changes in periphyton nutrient 

ratios across all 26 sites over the full study period. 

C:P (bulk)

N:P (bulk)

C:P (bulk)

N:P (bulk)

Surface‐waterDissolved InorganicN (DIN) and

TotalN (TN) vs. Periphyton Nutrient Content (2006‐2008)
R.S. King et al. 2009 43 

Table 4. Results of nonparametric changepoint analysis using surface-water PO4-P, DIN, TN, and 

periphyton C:P ratio (bulk) as a predictors of threshold responses in biological and water quality 

response variables from all 26 sites and 8 quarterly sampling events combined during 2006-2008. 

Significant thresholds are highlighted in bold. See Table 1 for full names and descriptions corresponding 

to variable IDs. 

Cumulative Threshold 

Quantiles 

Predictor Variable Response Obs. P value 10% 50% 90%

PO4-P (ug/L) AFDM_M2 Decline 6.8 <0.0001 6.8 7.4 7.9

CHLA_UGL Increase 28.9 0.0001 10.9 17.0 32.5

CHL:AFDM Increase 104.4 <0.0001 19.4 113.5 139.3

CHLA_M2 Increase 84.3 <0.0001 6.8 7.7 145.2

CN_ALG Decline 7.0 <0.0001 5.5 8.7 13.8

CN_BULK Decline 19.4 <0.0001 17.3 19.2 20.7

CP_ALG Decline 17.9 <0.0001 17.3 17.9 20.4

CP_BULK Decline 17.9 <0.0001 16.5 17.9 19.9

MACALG5+ Increase 509.0 0.0010 7.8 264.3 509.0

MICALG3+ Decline 9.7 0.0015 5.7 10.0 235.1

NP_ALG Decline 17.9 <0.0001 19.2 49.8 141.9

NP_BULK Decline 17.9 <0.0001 17.3 17.9 21.0

PLNT1+ Decline 6.8 <0.0001 5.5 8.1 9.4

PLNT3+ Decline 8.2 <0.0001 5.1 7.7 11.7

SED3+ Increase 9.8 <0.0001 5.6 9.6 145.2

TURB_NTU Increase 13.7 <0.0001 11.5 13.7 14.2

Periphyton 

C:P (bulk) AFDM_M2 Increase 762.1 0.0159 431.6 736.2 963.6

CHLA_UGL Decline 496.1 <0.0001 181.1 416.4 739.4

CHL:AFDM Decline 717.3 <0.0001 141.6 442.3 869.1

CHLA_M2 Decline 762.1 <0.0001 454.4 731.6 834.5

CN_ALG Increase 317.5 <0.0001 226.0 317.5 521.9

CN_BULK Increase 540.8 <0.0001 337.6 459.6 554.9

MACALG5+ Decline 1050.7 <0.0001 129.3 272.6 1050.7

MICALG3+ Increase 338.3 <0.0001 301.3 338.3 491.3

NP_ALG Increase 623.9 <0.0001 363.3 537.0 627.4

NP_BULK Increase 349.3 <0.0001 301.3 349.3 454.9

PLNT1+ Increase 195.6 <0.0001 176.6 226.4 244.5

PLNT3+ Increase 176.6 <0.0001 175.1 184.0 234.7

SED3+ Decline 482.9 <0.0001 290.6 486.1 773.8

TURB_NTU Decline 403.5 <0.0001 301.2 403.5 736.3

TN (ug/L) AFDM_M2 Decline 2181.7 0.0234 212.6 2181.7 3973.3

CHLA_UGL Increase 351.8 <0.0001 243.0 350.3 368.5
R.S. King et al. 2009 44 

CHL:AFDM Increase 1060.7 <0.0001 987.6 1071.3 3965.0

CHLA_M2 Increase 2181.7 0.0002 2075.0 2445.0 5083.4

CN_ALG Decline 285.4 <0.0001 261.4 285.4 580.8

CN_BULK Decline 548.9 <0.0001 524.9 574.4 1490.0

CP_ALG Decline 593.5 <0.0001 179.8 593.5 2445.0

CP_BULK Decline 548.9 <0.0001 511.2 548.9 771.7

MACALG5+ Increase 3335.0 <0.0001 428.0 3071.3 7571.0

MICALG3+ Decline 243.0 0.0013 204.4 240.5 587.4

NP_ALG Decline 2116.7 0.0001 1533.2 2116.7 2525.4

NP_BULK Decline 2445.0 0.0003 382.7 2145.0 3501.7

PLNT1+ Decline 786.4 0.0001 258.1 786.4 939.0

PLNT3+ Decline 922.2 0.0001 761.6 884.8 968.1

SED3+ Increase 279.9 <0.0001 240.0 309.4 458.6

TURB_NTU Increase 160.2 <0.0001 160.2 208.2 587.4

DIN (ug/L) AFDM_M2 Decline 67.6 0.0001 59.4 71.5 2092.4

CHLA_UGL Increase 9249.7 0.0754 64.0 69.1 3826.9

CHL:AFDM Increase 609.4 <0.0001 398.4 609.4 1939.6

CHLA_M2 Increase 3263.8 <0.0001 1893.9 2912.5 3732.9

CN_BULK Decline 1372.4 <0.0001 323.6 1186.0 3525.3

CP_ALG Decline 1627.1 <0.0001 523.8 1660.1 3343.7

CP_BULK Decline 1627.1 <0.0001 436.2 1372.4 3343.7

MACALG5+ Increase 2147.6 0.0591 61.6 1007.5 3900.0

MICALG3+ Decline 21.4 0.0147 21.4 82.9 2060.6

NP_ALG Decline 1783.9 0.1290 72.6 1783.9 3358.2

NP_BULK Decline 1783.9 <0.0001 1609.3 1783.9 6138.7

PLNT1+ Decline 71.5 0.0514 59.5 111.8 375.5

PLNT3+ Decline 71.5 0.0002 61.6 101.4 492.4

SED3+ Increase 523.8 0.0008 40.1 523.8 3202.8

TURB_NTU Increase 73.2 <0.0001 40.5 72.6 1372.4
R.S. King et al. 2009 45 

TP and periphyton C:P were again used as predictors of threshold changes in each of the 

biological responses listed in Table 3 and 4, but for each of the 8 dates separately (Figures 17, 

18; Tables 5, 6). This was done to evaluate seasonal changes and identify variables that 

responded consistently over the 2 year study. 

The results of these date-specific analyses revealed several important conclusions: 

• TP consistently predicted nonlinear declines in periphyton C:P ratios (Figure 17), and 

periphyton C:P ratios consistently predicted the same threshold responses in other 

variables that were predicted by TP, and even some that TP did not detect. 

• The most consistent biological responses, aside from C:N:P ratios in the periphyton, were 

CHL_AFDM (ratio of periphyton chlorophyll to periphyton AFDM) and PLNT1 and/or 

PLNT3+ (percentage of the reach with > 0 or > 25% macrophyte cover). Both of these 

responses reflect fundamental changes to the structure and functioning of wadeable 

streams. 

• Several biological variables were inconsistent responders to P enrichment, but usually 

this could be explained by seasonal patterns in light and/or discharge (scouring). The 

consistency of the direction of response (e.g., MACALG5+ always increasing, etc) 

supports these as reliable indices of excessive P enrichment, but also suggests that sites 

with TP levels above the reported thresholds will not always exhibit these responses (due 

to seasonal or hydrological limiting factors). 

• Some metrics responded consistently among years within a particular season. In 

particular, MICALG3+ (% of reach with biofilm thickness > 1 mm) significantly 

declined only in the summer months (Aug 2006, Sep 2007, and June 2008), but this is 

perhaps the most critical time of the year in terms of primary production and other 

ecosystem processes. The consistent increase of CHLA:AFDM ratio also implies that 

biofilm structure changed with TP enrichment even when there was no effect on biofilm 

thickness (MICALG3+). 
R.S. King et al. 2009 46 

Figure 17. Results from nonparametric changepoint analysis using surface-water TP (ug/L) as a 

predictor of threshold changes in periphyton C:P ratios (bulk) across all 26 sites for each of the 8 

quarterly sampling events separately, illustrating the consistency of the threshold decline of C:P (bulk), 

regardless of year, season, or discharge. All TP thresholds were significant, and ranged from a minimum 

of 11.7 ug/L (May 2007) and a maximum of 24.6 ug/L TP (September 2007). See Table 5 for detailed 

summaries of the statistical results. 

Aug 2006

Nov 2006

Feb 2007

May 2007

Sep 2007

Dec 2007

Feb 2008

Jun 2008
R.S. King et al. 2009 47 

\ 

Figure 18. Results from nonparametric changepoint analysis using surface-water TP (ug/L) as a 

predictor of threshold changes in macrophyte cover across all 26 sites for 6 of the 8 quarterly sampling 

events separately, illustrating the consistency of the threshold decline of macrophyte cover regardless of 

year, season, or discharge. All TP thresholds were significant, and ranged from a minimum of 14.6 ug/L 

to a maximum of 54.2 ug/L TP (September 2007). See Table 5 for detailed summaries of the statistical 

results. 

Aug 2006

Feb 2007

Nov 2006

Sep 2007

Dec 2007

Feb 2008
R.S. King et al. 2009 48 

Table 5. Results from nonparamateric changepoint analysis using surface-water TP (ug/L) as a 

predictor of threshold responses in biological and water quality variables among each of the 8 quarterly 

sampling events from the 26 field study streams during 2006-2008. TP thresholds (Obs) and bootstrap 

quantiles are shown for each response variable and date combination. Direction (increase or decrease) of 

the response with increasing TP is shown for thresholds deemed significant (bold). 

Cumulative Threshold 

Quantiles 

Date Variable Response Obs. 10% 50% 90% P value 

Aug-06 AFDM_M2 Decline 30.5 14.4 27.8 48.3 0.0357

Nov-06 AFDM_M2 120.1 12.4 31.1 120.1 0.1596

Feb-07 AFDM_M2 26.4 11.5 24.4 28.0 0.1046

May-07 AFDM_M2 14.3 11.9 14.3 18.1 0.1593

Sep-07 AFDM_M2 115.8 10.9 18.4 54.7 0.3967

Dec-07 AFDM_M2 15.5 13.4 15.5 22.0 0.1587

Feb-08 AFDM_M2 45.3 19.1 20.9 39.5 0.1532

Jun-08 AFDM_M2 29.4 10.6 26.6 54.1 0.3157

Aug-06 CHLA_UGL 653.7 14.4 32.8 220.7 0.1326

Nov-06 CHLA_UGL 14.0 12.8 14.5 47.2 0.1156

Feb-07 CHLA_UGL Increase 15.5 14.6 15.5 17.3 0.0016

May-07 CHLA_UGL Increase 82.2 11.4 15.6 30.7 0.0503

Sep-07 CHLA_UGL Increase 21.1 18.4 20.2 26.5 0.0050

Dec-07 CHLA_UGL Increase 22.0 15.0 22.0 40.8 0.0105

Feb-08 CHLA_UGL Increase 45.3 20.8 36.8 65.9 0.0037

Jun-08 CHLA_UGL Increase 18.2 13.4 18.2 21.0 0.0087

Aug-06 CHL_AFDM Increase 437.2 27.2 45.6 233.0 0.0028

Nov-06 CHL_AFDM Increase 801.0 23.7 76.2 236.3 0.0074

Feb-07 CHL_AFDM Increase 37.6 21.8 32.2 65.7 0.0044

May-07 CHL_AFDM 16.0 12.9 15.6 18.1 0.1849

Sep-07 CHL_AFDM 10.2 10.2 12.8 36.4 0.3732

Dec-07 CHL_AFDM Increase 40.8 18.9 24.7 40.8 0.0170

Feb-08 CHL_AFDM Increase 32.2 21.0 52.5 108.0 0.0082

Jun-08 CHL_AFDM 131.2 14.2 32.7 102.7 0.1321

Aug-06 CHLA_M2 54.2 20.5 35.0 141.7 0.1179

Nov-06 CHLA_M2 801.0 14.0 27.4 236.3 0.3263

Feb-07 CHLA_M2 Increase 26.4 16.5 26.4 37.6 0.0188

May-07 CHLA_M2 Increase 14.3 12.9 14.3 16.5 0.0438

Sep-07 CHLA_M2 10.2 10.2 13.0 42.9 0.4436

Dec-07 CHLA_M2 Increase 22.0 14.2 22.0 24.7 0.0375

Feb-08 CHLA_M2 Increase 32.2 19.7 23.4 39.5 0.0178

Jun-08 CHLA_M2 29.4 13.4 48.0 82.2 0.1926
R.S. King et al. 2009 49 

Aug-06 CP_ALG Decline 18.5 17.6 18.5 20.5 0.0034

Nov-06 CP_ALG Decline 9.6 9.6 16.3 97.6 0.0211

Feb-07 CP_ALG Decline 37.6 8.5 26.4 37.6 0.0075

May-07 CP_ALG Decline 11.7 10.7 12.5 17.0 0.0435

Sep-07 CP_ALG Decline 12.3 12.3 13.0 31.1 0.0202

Dec-07 CP_ALG Decline 30.6 12.2 22.0 35.1 0.0061

Feb-08 CP_ALG Decline 17.5 16.9 17.5 23.6 0.0058

Jun-08 CP_ALG Decline 13.3 12.5 13.3 45.7 0.0051

Aug-06 CP_BULK Decline 18.5 17.6 19.3 30.5 0.0058

Nov-06 CP_BULK Decline 16.3 9.4 16.6 53.7 0.0317

Feb-07 CP_BULK Decline 18.7 11.3 17.1 20.7 0.0036

May-07 CP_BULK Decline 11.7 11.7 14.3 23.5 0.0331

Sep-07 CP_BULK Decline 24.6 11.6 13.0 24.6 0.0141

Dec-07 CP_BULK Decline 12.2 12.0 12.3 24.9 0.0125

Feb-08 CP_BULK Decline 23.5 18.2 20.6 39.5 0.0052

Jun-08 CP_BULK Decline 15.4 13.3 15.4 15.5 0.0011

Aug-06 MACALG5+ Increase 141.7 18.5 29.2 141.7 0.0388

Nov-06 MACALG5+ 8.5 11.0 19.1 120.1 0.5798

Feb-07 MACALG5+ Increase 8.3 8.3 8.7 26.4 0.0420

May-07 MACALG5+ 15.7 11.7 15.6 17.2 0.4341

Sep-07 MACALG5+ 11.6 11.6 13.9 42.9 0.6199

Dec-07 MACALG5+ 30.6 13.6 22.9 35.1 0.3143

Feb-08 MACALG5+ 17.3 17.3 19.1 36.7 0.2957

Jun-08 MACALG5+ 10.8 10.8 15.9 75.5 0.6003

Aug-06 MICALG3+ Decline 20.6 12.1 20.6 22.3 0.0252

Nov-06 MICALG3+ 200.2 14.0 26.8 200.2 0.2573

Feb-07 MICALG3+ 9.3 9.3 13.5 72.6 0.5553

May-07 MICALG3+ 11.7 11.7 13.8 22.8 0.4841

Sep-07 MICALG3+ Decline 10.2 10.2 10.3 13.9 0.0013

Dec-07 MICALG3+ 11.9 11.9 15.4 58.4 0.1905

Feb-08 MICALG3+ 32.2 17.5 20.3 39.5 0.1097

Jun-08 MICALG3+ Decline 13.4 13.4 14.2 14.8 0.0368

Aug-06 PLNT1+ Decline 54.2 15.8 30.1 54.2 0.0484

Nov-06 PLNT1+ Decline 20.2 16.3 20.2 29.3 0.0153

Feb-07 PLNT1+ Decline 14.6 14.3 14.6 17.7 0.0267

May-07 PLNT1+ Decline 15.3 13.2 15.3 15.4 0.0860

Sep-07 PLNT1+ 13.9 10.3 13.9 31.9 0.3884

Dec-07 PLNT1+ Decline 17.1 14.3 17.1 22.0 0.0400
R.S. King et al. 2009 50 

Feb-08 PLNT1+ Decline 25.1 19.6 23.8 38.2 0.0269

Jun-08 NA 

Aug-06 PLNT3+ Decline 54.2 12.1 45.6 115.4 0.0643

Nov-06 PLNT3+ Decline 20.2 19.2 20.2 29.3 0.0227

Feb-07 PLNT3+ 72.6 9.3 14.9 42.9 0.1456

May-07 PLNT3+ NA 

Sep-07 PLNT3+ 33.0 11.4 14.9 31.9 0.5600

Dec-07 PLNT3+ Decline 17.1 12.0 17.1 19.0 0.0528

Feb-08 PLNT3+ Decline 22.1 18.2 23.8 38.2 0.0460

Jun-08 PLNT3+ NA 

Aug-06 SED3+ 22.8 14.4 22.2 41.0 0.1015

Nov-06 SED3+ Increase 9.6 9.4 11.4 17.1 0.0407

Feb-07 SED3+ Increase 8.0 7.6 9.3 42.9 0.0472

May-07 SED3+ 20.4 11.8 14.7 23.2 0.0699

Sep-07 SED3+ Increase 12.8 10.2 12.8 13.0 0.0128

Dec-07 SED3+ Increase 12.0 12.0 14.2 22.0 0.0429

Feb-08 SED3+ Increase 22.1 17.5 20.7 39.5 0.0279

Jun-08 SED3+ Increase 15.9 9.9 15.9 54.1 0.0183

Aug-06 TURB_NTU Increase 17.4 15.3 17.4 19.9 0.0267

Nov-06 TURB_NTU 9.6 9.5 16.3 86.9 0.1315

Feb-07 TURB_NTU Increase 9.3 8.9 9.3 13.5 0.0040

May-07 TURB_NTU Increase 17.2 12.5 15.9 24.4 0.0360

Sep-07 TURB_NTU Increase 17.6 13.9 20.2 31.9 0.0077

Dec-07 TURB_NTU Increase 15.9 15.8 16.8 24.9 0.0066

Feb-08 TURB_NTU Increase 45.3 19.4 22.4 39.5 0.0054

Jun-08 TURB_NTU Increase 18.2 13.2 18.1 21.0 0.0027
R.S. King et al. 2009 51 

Table 6. Results from nonparamateric changepoint analysis using periphyton C:P ratio (bulk) as a 

predictor of threshold responses in biological and water quality variables among each of the 8 quarterly 

sampling events from the 26 field study streams during 2006-2008. Periphyton C:P thresholds (Obs.) 

and bootstrap quantiles are shown for each response variable and date combination. Direction (increase 

or decrease) of the response with increasing periphyton C:P is shown for thresholds deemed significant 

(bold). *Note that an increasing value of C:P corresponds to a decline in P enrichment in the 

periphyton. 

Cumulative Threshold 

Quantiles 

Date Variable Response* Obs. 10% 50% Date Variable

Aug-06 AFDM_M2 Increase 275.1 274.7 306.4 597.9 0.0386

Nov-06 AFDM_M2 373.6 122.5 195.4 368.0 0.2509

Feb-07 AFDM_M2 373.6 135.7 373.6 461.2 0.1407

May-07 AFDM_M2 182.5 182.7 368.3 726.0 0.1484

Sep-07 AFDM_M2 155.0 155.0 534.4 886.0 0.6122

Dec-07 AFDM_M2 328.9 212.9 334.6 624.7 0.3682

Feb-08 AFDM_M2 297.5 175.8 297.5 499.0 0.2741

Jun-08 AFDM_M2 278.7 160.6 278.7 709.1 0.4895

Aug-06 CHLA_UGL 618.3 275.1 387.7 706.6 0.4483

Nov-06 CHLA_UGL 368.0 149.2 213.9 364.1 0.4072

Feb-07 CHLA_UGL Decline 394.4 205.6 347.4 457.9 0.0191

May-07 CHLA_UGL 731.0 204.0 477.2 731.0 0.0520

Sep-07 CHLA_UGL Decline 814.8 204.9 814.8 859.0 0.0086

Dec-07 CHLA_UGL Decline 328.9 169.9 408.5 659.8 0.0158

Feb-08 CHLA_UGL Decline 297.5 266.2 297.5 369.3 0.0068

Jun-08 CHLA_UGL Decline 762.4 180.5 743.1 803.1 0.0108

Aug-06 CHL_AFDM Decline 306.4 212.3 306.4 344.2 0.0052

Nov-06 CHL_AFDM 205.1 140.5 205.1 236.6 0.0514

Feb-07 CHL_AFDM Decline 123.2 116.9 164.7 429.3 0.0280

May-07 CHL_AFDM 226.4 226.4 391.5 639.2 0.1566

Sep-07 CHL_AFDM Decline 1207.6 501.2 867.0 1130.4 0.0220

Dec-07 CHL_AFDM Decline 659.8 169.9 354.9 659.8 0.0225

Feb-08 CHL_AFDM Decline 171.2 175.2 225.3 271.0 0.0086

Jun-08 CHL_AFDM Decline 523.3 236.7 523.3 863.2 0.0294

Aug-06 CHLA_M2 212.3 212.3 317.5 740.0 0.1763

Nov-06 CHLA_M2 205.1 109.4 204.0 212.1 0.1650

Feb-07 CHLA_M2 Decline 123.2 123.2 300.3 429.3 0.0480

May-07 CHLA_M2 226.4 204.0 345.9 616.1 0.0958

Sep-07 CHLA_M2 Decline 1207.6 510.8 851.5 993.1 0.0342

Dec-07 CHLA_M2 Decline 328.9 194.6 328.9 658.9 0.0460
R.S. King et al. 2009 52 

Feb-08 CHLA_M2 Decline 297.5 269.2 297.5 385.4 0.0178

Jun-08 CHLA_M2 278.7 170.5 345.9 877.6 0.1525

Aug-06 MACALG5+ 306.4 299.4 306.4 658.6 0.1331

Nov-06 MACALG5+ 173.5 109.4 173.5 242.2 0.6289

Feb-07 MACALG5+ 537.6 123.2 262.2 457.9 0.7244

May-07 MACALG5+ 470.2 204.0 455.6 726.0 0.5601

Sep-07 MACALG5+ 143.5 143.5 352.5 867.0 0.1605

Dec-07 MACALG5+ 213.3 162.3 213.3 606.6 0.2077

Feb-08 MACALG5+ Decline 642.1 252.0 642.1 705.8 0.0481

Jun-08 MACALG5+ 158.4 158.4 241.6 709.1 0.3257

Aug-06 MICALG3+ Increase 306.4 299.4 317.5 724.5 0.0497

Nov-06 MICALG3+ 324.0 122.6 259.1 333.2 0.2077

Feb-07 MICALG3+ 354.6 114.4 277.7 441.6 0.6632

May-07 MICALG3+ 470.2 204.0 474.1 726.0 0.8096

Sep-07 MICALG3+ Increase 993.1 758.6 840.7 993.1 0.0208

Dec-07 MICALG3+ 328.9 127.5 328.9 528.8 0.2828

Feb-08 MICALG3+ 540.2 297.5 526.2 686.3 0.1434

Jun-08 MICALG3+ Increase 762.4 335.8 679.7 877.6 0.0264

Aug-06 PLNT1+ Increase 212.3 212.3 363.4 711.2 0.0273

Nov-06 PLNT1+ Increase 195.6 169.9 195.6 221.0 0.0234

Feb-07 PLNT1+ Increase 139.8 136.1 262.2 427.0 0.0563

May-07 PLNT1+ 486.6 368.3 486.6 727.9 0.2028

Sep-07 PLNT1+ 1207.6 233.5 843.5 1109.9 0.2403

Dec-07 PLNT1+ Increase 470.7 169.9 470.7 524.4 0.0591

Feb-08 PLNT1+ Increase 676.6 269.2 596.2 713.4 0.0331

Jun-08 PLNT1+ NA 

Aug-06 PLNT3+ 212.3 229.3 524.4 598.9 0.1627

Nov-06 PLNT3+ Increase 173.5 169.9 188.0 230.1 0.0546

Feb-07 PLNT3+ Increase 154.0 153.7 154.0 416.4 0.0436

May-07 PLNT3+ 

Sep-07 PLNT3+ 843.5 217.0 821.7 1013.6 0.3490

Dec-07 PLNT3+ 659.8 169.9 418.7 659.8 0.2043

Feb-08 PLNT3+ Increase 297.5 269.2 456.7 725.8 0.0572

Jun-08 PLNT3+ 

Aug-06 SED3+ Decline 779.8 484.6 749.6 908.4 0.0558

Nov-06 SED3+ Decline 292.3 177.2 292.3 313.4 0.0691

Feb-07 SED3+ 93.6 93.6 160.2 444.9 0.3262

May-07 SED3+ 486.6 205.1 486.6 726.0 0.1023
R.S. King et al. 2009 53 

Sep-07 SED3+ Decline 793.0 767.2 806.2 993.1 0.0337

Dec-07 SED3+ 701.3 137.9 308.5 635.6 0.3543

Feb-08 SED3+ Decline 297.5 269.2 386.0 676.6 0.0692

Jun-08 SED3+ Decline 523.3 270.7 523.3 803.1 0.0187

Aug-06 TURB_NTU Decline 779.8 724.5 770.2 829.0 0.0181

Nov-06 TURB_NTU 259.1 122.6 207.7 278.9 0.3126

Feb-07 TURB_NTU Decline 373.6 352.2 394.4 457.9 0.0326

May-07 TURB_NTU Decline 486.6 368.3 486.6 731.0 0.0307

Sep-07 TURB_NTU 814.8 188.3 754.5 830.3 0.0822

Dec-07 TURB_NTU Decline 169.9 166.0 173.9 401.4 0.0197

Feb-08 TURB_NTU Decline 385.4 297.5 385.4 642.1 0.0081

Jun-08 TURB_NTU Decline 626.4 278.7 626.4 660.4 0.0054
R.S. King et al. 2009 54 

RESULTS AND INTERPRETATION, CONTINUED 

Effects of TP on Diel Dissolved Oxygen 

Interannual differences in stream discharge strongly influenced the daily (diel) variation in 

dissolved oxygen, water temperature, and pH (Figure 19). 

Figure 19. Patterns of dissolved oxygen, water temperature, and pH across 2-consecutive 24-hour periods 

at each of the 26 field study sites during a period of very low discharge (September 2006) and during a 

period of high flows following significant flooding (September 2007). 
R.S. King et al. 2009 55 

Figure 19 clearly illustrates the important influence of stream flow on diel water chemistry. In 

2006, a drought year, most of the field-study streams had very low-to-no measureable discharge. 

This lack of flow limited the turbulent mixing of the water column. Without turbulence, gas 

exchange between air and water was limited to passive diffusion. During the day, this caused 

many of the streams to become supersaturated with dissolved oxygen (up to 20 mg/L DO; > 

250% saturation) which concomitantly caused pH to increase to relatively high levels (8.5-9.5). 

At night, DO was rapidly consumed. Several streams had minimum DO levels near 0 mg/L, 

often for several hours (typically between 00:00-09:00, but in some cases until 12:00). Water 

temperature also varied widely at different hours of the day, reaching temperatures up to 30-35 C 

during late afternoon (Figure 19). 

In September 2007, discharge was relatively high in all 26 study streams. Most of the streams 

were in flood stage for several of the preceding months, and had just recently subsided to 

wadeable flows. Even under these higher flows and turbulent mixing conditions, dissolved 

oxygen showed clear diel variation, peaking around 16:00 at concentrations up to 12 mg/L (120-

150% saturation), and declining during the night to typically 6-8 mg/L DO. However, minimum 

DO levels were rarely below 5 mg/L, and no stream had minimum DO levels that would be 

considered detrimental to native fishes or macroinvertebrates in this region, especially in moving 

water. 

Figure 20. Results of nonparametric changepoint analysis using surface water TP (ug/L) as a predictor of 

threshold declines in minimum dissolved oxygen (DO, mg/L) during September 2006. The four points in 

the upper right corner of the plot correspond to streams immediately downstream of WWTP discharges; 

these were the only flowing streams during this sampling event. 

Minimumdissolved oxygen, 48 hr (mg/L), September 2006

Symbolsscaled to stream discharge (large=WWTP outfalls)
R.S. King et al. 2009 56 

The very low minimum DO values at several of the sites in 2006 were strongly related to 

surface-water TP, but also stream discharge (Figure 20). All 6 streams with no detectable flow 

and TP > 27.2 had minimum DO values < 2 mg/L, and two others with TP near 20 ug/L dropped 

below 3.5 mg/L DO. The four streams located immediately downstream of effluent discharges 

maintained relatively high minimum DO concentrations despite very high TP. This was related 

to reaeration associated with turbulent mixing. Despite these outliers, TP was nevertheless a 

significant predictor of threshold declines in minimum DO (Table 7). 

The diel pattern of DO at NBOS-02 (North Bosque River - 02) is further evidence that WWTP 

discharge sites maintained relatively high minimum DO because of turbulent mixing associated 

with flow. NBOS-02 is located a few kilometers downstream of NBOS-01, one of the 4 WWTP 

discharge sites depicted in Figure 20. NBOS-02 had DO levels fall to near 0 mg/L the same 

night that NBOS-01 maintained DO above 5 mg/L. Because of the drought, the water table had 

dropped below the stream channel and discharge from NBOS-01 subsided to a slow trickle by 

the time it reached NBOS-02. Here, P enrichment stimulated photosynthesis during the day 

whereas microbial respiration, also fueled by P-rich organic matter, was even higher at night. 

Thus, the stream was essentially anoxic for several hours at night and early morning. 

Minimum DO also corresponded to mean biofilm thickness, filamentous algal cover, and the 

ratio of periphyton chlorophyll to AFDM (Figure 21). As previously shown, thicker layers of 

calcareous periphyton comprised mostly of diatoms, cyanobacteria, and other microbes were 

associated with low-P streams, and as TP increased, these biofilms succeeded to filamentous and 

colonial greens and other types of algae. The results in Figure 21 explicitly link TP to sharp 

nonlinear responses of primary producers, which, in turn, cause sharp nonlinear declines in 

minimum DO. Thus, these metrics appear to not only be sensitive to TP enrichment, but also can 

be linked quantitatively to aquatic life use standards that rely on dissolved oxygen as a measure 

of biological integrity. 

These flow-dependent results imply that studies on the effects of nutrients on DO will not 

adequately characterize risk to biota and associated aquatic life use designations without 

sampling during periods of low flow. 
R.S. King et al. 2009 57 

Figure 21. Results of nonparametric changepoint analysis using surface water TP (ug/L), biofilm 

thickness, filamentous algal cover, and periphyton chlorophyll-to-AFDM ratio as predictors of threshold 

declines in minimum dissolved oxygen (DO, mg/L) during September 2006. The four streams 

immediately downstream of WWTP discharges were excluded from the analysis because these were the 

only flowing streams during this sampling event. 

Minimumdissolved oxygen, 48 hr (mg/L), September 2006

vs. surface‐water TP, filamentousalgae, and periphyton
R.S. King et al. 2009 58 

Table 7. Results from nonparametric changepoint analysis using surface-water TP, periphyton C:P bulk, 

filamentous macroalgal cover > 75% (MACALG5+) and mean biofilm thickness (MICALG_R) as 

threshold predictors of minimum dissolved oxygen during 48 h among 26 stream sites in September 

2006 and 2007. Significant thresholds in DO are highlighted in bold. *All 26 sites. **Four WWTP sites 

with high discharge and turbulent mixing removed from analysis. See Figures 20 and 21 for graphical 

display of results. 

Cumulative Threshold 

Quantiles 

Mean DO 48 h 

min (mg/L) 

Predictor Obs. 10% 50% 90%

P

value Below Above 

September 2006 

(drought, low 

flow) 

TP* 27.2 17.6 27.2 29.2 0.0499 5.09 3.06

TP** 27.2 19.4 24.5 27.2 0.0068 5.09 1.64

CP_BULK** 299.4 299.4 321.9 698.8 0.0237 1.14 4.63

MACALG5+** 11.5 2.5 4.0 11.0 0.0170 4.96 1.92

MICALG_R** 0.31 0.31 0.51 0.775 0.0237 1.14 4.63

September 2007 

(post-flood, high 

flow) 

TP 12.9 11.6 12.9 47.3 0.5489 5.59 6.13

CP_BULK 143.4 143.4 517.8 885.9 0.1694 5.25 6.05
R.S. King et al. 2009 59 

RESULTS AND INTERPRETATION, CONTINUED 

Periphyton Taxonomic Responses to TP: Ordinations 

Figure 22. Nonmetric multidimensional scaling ordination of algal species composition among the 26 

field study sites in August 2006. Abundance data (no. of cells/cm2

) was log10(x+1) transformed prior to 

analysis. Bray-Curtis distance was used as the dissimilarity metric. Distances between sites in the 

ordination space are proportional to taxonomic dissimilarity (near=similar, far=dissimilar). In each 

figure, the red arrows (vectors) represent the direction and magnitude of significant (p<0.05) correlations 

between environmental variables and algal species composition. The upper panel shows the significant 

environmental predictors of algal species: nutrients (e.g., TP, periphyton C:P), sediment, biofilm 

thickness, and filamentous algae. The lower panel shows the significant watershed predictors of algae 

species: pasture, outfalls, and urban development in watersheds. See Table 1 and 2 for full variable 

names. 

BLUF163

CORY163

COWH163

DUFF163

HARR163

LAMP163

LAMP263

LEON163

LEON263

MBOS163

MERI163

NBOS163

NBOS263

NBOS363

NBOS463

NBOS563

NEIL163

NOLC163

NOLR163

PALU163

PLUM163

ROCK163

SALA163

SBOS163

SLEO163

STEE163

PO4-P

TN

TP

C_BULK

N_ALG

N_BULK

P_ALG

P_BULK P_SED

CN_ALG

CN_BULK

CN_SED

CP_ALG

CP_BULK

CP_SED

NP_ALG

NP_BULK

AFDM_PCT

CHL_AFDM

DISCHARG

SPCOND

DEPTH

SED_CV

SED0

SED2+

SL_PCT FINE_PCT

MACALG4

MACALG6

MICALG_R

MICALG0

MICALG2+

-2.0

-1.0

-1.0 0.0 1.0

-0.5

0.0

0.5

1.0

nMDS Axis 1 (75%)

nMDS Axis 2 (11%)

Year

2006

Increasing P enrichment,sedimentation, chlorophyll,filamentousmacroalgae

Decreasing periphyton C:P, C:N ratios, biofilm thickness

August 2006, environmental vectors

August 2006, watershed vectors

Increasing WWTP outfall discharge, pasture,reservoirs, dams, and urban land
R.S. King et al. 2009 60 

Figure 23. Nonmetric multidimensional scaling ordination of algal species composition among the 26 

field study sites in September 2007. Most of the environmental and watershed variables that were strong 

predictors of algal species composition in 2006 were also significant predictors in these ordinations. See 

Figure 22 for details. 

Increasing P enrichment,sedimentation,periphyton chlorophyll

Decreasing periphyton C:P, C:N ratios, biofilm thickness

Decreasing periphyton chlorophyll, increasingsed OM

September 2007, environmental vectors

BLUF-01 CORY-01

COWH-01

DUFF-01

HARR-01

LAMP-01

LAMP-02

LEON-01

LEON-02

MBOS-01

MERI-01

NBOS-01

NBOS-02

NBOS-03

NBOS-04

NBOS-05

NEIL-01 NOLC-01

NOLR-01

PALU-01

PLUM-01

ROCK-01

SALA-01

SBOS-01

SLEO-01

STEE-01

OUT_MGD

WATER

PASTURE

-1.5

-1.5

-0.5 0.5 1.5

-0.5

0.5

nMDS Axis 1 (57%)

nMDS Axis 2 (25%)

September 2007, watershed vectors

Increasing WWTP outfall discharge, pasture, and reservoir surface area

Increasing pasture, and reservoir surface area
R.S. King et al. 2009 61 

Figure 24. Nonmetric multidimensional scaling ordination of algal species composition among the 26 

field study sites in August 2006 and September 2007. The arrows indicate directions and magnitude of 

change in algal species composition between years at each of the 26 sites. 

The multivariate analyses of algal species composition during August 2006 and September 2007 

continued to support the earlier conclusions that TP and related metrics had a strong influence on 

stream biota (Figures 22-24). In both years, most of the variance in algal species composition 

was related to TP. Sites with the lowest TP and highest periphyton C:P ratios were placed at the 

low end of axis 1 whereas sites with the highest TP and lowest C:P ratios were at the opposite 

end of axis 1. Figure 24 is particularly interesting because it showed that although algal species 

changed between years (illustrated by the shift in sites from 2006 to 2007 along axis 2), the 

relative order of sites from low P to high P remained intact on axis 1. This is compelling 

evidence that algal species composition was altered by nutrient enrichment, and the magnitude of 

its effect was similar regardless of interannual variation in stream discharge. 

 
R.S. King et al. 2009 62 

Figure 25. Nonmetric multidimensional scaling ordination of algal species composition among the 26 

field study sites in August 2006 and September 2007. The ordination diagram is identical to Figure 24, 

except that site symbols are scaled in proportion to surface-water TP (upper panel) and the proportion of 

the reach with velocity > 0.2 m/s (lower panel). 

BLUF163

CORY163

COWH163

DUFF163

HARR163

LAMP163

LAMP263

LEON163

LEON263

MBOS163

MERI163

NBOS163

NBOS263

NBOS463 NBOS363 NBOS563

NEIL163

NOLC163

NOLR163

PALU163 PLUM163

ROCK163

SALA163

SBOS163

SLEO163

STEE163

BLUF173

CORY173

COWH173

DUFF173

HARR173 LAMP173

LAMP273

LEON173

LEON273

MBOS173

MERI173

NBOS273NBOS173

NBOS373

NBOS473

NBOS573

NEIL173

NOLC173

NOLR173

PALU173

PLUM173

ROCK173

SALA173

SBOS173

SLEO173

STEE173

-1.5

-1.5

-0.5 0.5 1.5

-0.5

0.5

1.5

nMDS Axis 1 (41%)

nMDS Axis 2 (40%)

Year

2006

2007 % VELOCITY EXCEEDING 0.2 m/s
R.S. King et al. 2009 63 

Figure 29. Nonmetric multidimensional scaling ordination of algal species composition over the 8 

quarterly sampling events at the 6 intensive sampling sites (see Figure 1). Lines connect dates within 

each site in chronological order, starting with August 2006 (1) and ending with June 2008 (8). For 

clarity, patterns of community succession are shown for each site separately in the lower 6 panels. 

 
R.S. King et al. 2009 64 

As shown previously in the ordination of sites between years (2006 vs. 2007), algal species 

composition varied significantly over time (Figure 29). The 6 sites selected for species analysis 

on all 8 quarterly sampling events (“intensive sites”) followed the same general trajectory of 

change from along nMDS Axis 2, shifting down axis 2 during the high flow periods of May and 

September 2007, but turning around and moving back up axis 2 toward their original positions in 

August 2006. 

Importantly, each site stayed in the same approximate position along axis 1 (the nutrient axis) 

over time. ROCK-01, COWH-01, and LAMP-02 overlapped somewhat in their trajectories, but 

each of these sites was consistently below the observed TP thresholds reported in the preceding 

results. Thus, their similarity is to be expected. 

PALU-01 remained consistently intermediate along Axis 1, distinctly separated from the low-P 

sites during the first few sampling events, but overlapping with COWH-01 near the end of the 

study. NBOS-03 and LEON-02, both highly enriched sites, consistently stayed on the right end 

of axis 1. NBOS-03 had high TP levels for most of the study (~100-200 ug/L). In 2006, LEON02 had very high levels of TP (> 2 mg/L), but TP declined to ~100 ug/L in the latter half of the 

study (Figure 6). The decline in TP was also coincident with a slight shift to the left in algal 

species composition, possibly indicating a decline in the few taxa that were shown to proliferate 

mostly at the levels of TP > 200 ug/L (Tables 10-13). 
R.S. King et al. 2009 65 

RESULTS AND INTERPRETATION, CONTINUED 

Periphyton Taxonomic Responses to TP: Threshold Indicator Taxa Analysis (TITAN)

TITAN analyses on algal species composition in 2006 and 2007 continued to add evidence to an 

already substantial case for a threshold at low levels of TP. Dozens of algal species essentially 

disappeared from streams between 12 and 30 ug/L TP, with the threshold of greatest overall 

decline (sum(z-)) at 19.2 and 21.6 ug/L in 2006 and 2007, respectively (Figure 26, Table 8). 

Simultaneously, numerous algal taxa showed sharp increases, either replacing taxa as they 

declined, or driving the declines via competitive exclusion (e.g., shading by 

Cladophora=CLAglome, a significant increasing taxon in both years; Table 10). 

Although most taxa declined or increased at relatively low levels of TP, a few taxa responded 

only to much higher levels of TP (Figure 26, Table 10). These taxa were mostly associated with 

sites heavily influenced by effluent. 

Fewer taxa declined in response to TP in 2007 than 2006, but as many or more increased in 

response to TP. Because of the protracted period of flooding prior to September 2007, many of 

the streams were in a period of rapid recolonization (community succession). Scouring of rocks 

reduced periphyton biomass substantially compared to 2006. It is likely that part of the 

explanation was that fast-responding “weedy” algal taxa proliferated with elevated TP, whereas 

slower responding, low-P indicator taxa had not become as consistently established across all 

streams as in 2006. 

Another explanation for differences between 2006 and 2007 was that surface-water TP was not 

as strong of an indicator of P-enrichment status during higher and more variable flows. There is 

some support for this hypothesis given that more threshold indicator taxa were deemed 

significant in 2007 when using periphyton C:P ratios (bulk or OM) than TP (Table 9, 11; Figure 

27, 28). This implies that exposure and availability (via recycling, sediment-bound P, etc) at 

these sites was better indicated by the P in the periphyton than in an instantaneous measurement 

from the water. 

 
R.S. King et al. 2009 66 

Figure 26. Results of Threshold Indicator Taxa ANalysis (TITAN) using surface-water TP a a predictor 

of threshold changes in individual algal species during August 2006 and September 2007. Taxa are 

classified as either negative (z-) or positive (z+) threshold indicators based on the direction of response to 

TP. The upper panels show the observed TP threshold value (colored symbols) for each taxon deemed to 

change significantly. Taxon IDs (see Appendix 1) are shown on the left (negative indicators) and right 

(positive indicators) y-axes, in rank order of their TP thresholds. Line segments around each symbol are 

90% confidence intervals around the TP threshold. The lower two panels show the aggregate response of 

negative (sum(z-)) and positive (sum(z+)) threshold indicator taxa. The TP value resulting in the highest 

sum(z) value is the point in which the greatest cumulative negative (z-) or positive (z+) occurs. 

Bootstrapping is used to estimate the cumulative threshold frequency for negative (green) and positive 

(red) responses, respectively. See Tables 8 and 9 for community level (sum(z)) thresholds, and Tables 10 

and 12 for taxa-specific thresholds. 

August 2006 September 2007
R.S. King et al. 2009 67 

Table 8. Community-level results from Threshold Indicator Taxa Analysis (TITAN) on algal species 

composition in response to water and periphyton nutrient concentrations during August 2006 (drought, 

n=26 sites). Thresholds (Obs.) are based on the value of the predictor resulting in the greatest aggregate 

decrease (sum(z-)) or increase (sum(z+)) in the frequency and abundance of taxa in the community. Taxa 

responses associated with lower nutrient conditions are shown in bold. The lower (5%, 10%), middle 

(50%), and upper (90%, 95%) quantiles of 1,000 bootstraps represent measures of uncertainty around the 

observed threshold.*Note that lower C:P values = higher P enrichment relative to organic carbon in the 

periphyton, thus taxa that “decrease” sharply in response to increasing C:P are associated with higher 

levels of P-enrichment,, whereas “increaser” taxa are associated with lower levels of P enrichment..

Figures 26-28 for details. 

August 2006 (n=26 streams) 

Bootstrap Cumulative Distribution of 

Thresholds 

Predictor Obs. 

Response > Obs 

Threshold Value 5% 10% 50% 90% 95%

Surface-water TP (ug/L) 19.2 Decline (z-) 11.7 17.1 20.6 30.4 32.4

32.4 Increase (z+) 22.8 22.8 30.4 437.2 1250.2

Surface-water PO4-P (ug/L) 4.4 Decline (z-) 4.4 4.4 5.1 14.3 16.2

30.8 Increase (z+) 9.6 9.6 14.3 1181.8 1808.3

Periphyton C:P ratio (OM) 369 Decline *(z-) 313 338 461 500 515

628 Increase* (z+) 445 461 563 833 1017

Periphyton C:P ratio (bulk) 465 Decline* (z-) 284 306 546 711 725

780 Increase* (z+) 659 659 729 925 926

Surface-water DIN (ug/L) 56.9 Decline (z-) 28.1 28.1 54.4 64.9 85.3

85.3 Increase (z+) 56.9 56.9 834.3 7411.5 7411.5

Surface-water TN (ug/L) 280.6 Decline (z-) 222.6 222.6 280.6 663.0 1162.5

1162.5 Increase (z+) 613.0 663.0 1852.5 8510.0 8510.0
R.S. King et al. 2009 68 

Table 9. Community-level results from Threshold Indicator Taxa Analysis (TITAN) on algal species 

composition in response to water and periphyton nutrient concentrations during September 2007 (postflood, n=26 sites). Thresholds (Obs.) are based on the value of the predictor resulting in the greatest 

aggregate decrease (sum(z-)) or increase (sum(z+)) in the frequency and abundance of taxa in the 

community. Taxa responses associated with lower nutrient conditions are shown in bold. See Table 8 

and Figures 26-28 for details. 

September 2007 (n=26 

streams) 

Bootstrap Cumulative Distribution of 

Thresholds 

Predictor Obs. 

Response > Obs. 

Threshold Value 5% 10% 50% 90% 95%

Surface-water TP (ug/L) 21.6 Decline (z-) 10.3 11.6 15.6 26.5 33.0

24.5 Increase (z+) 13.1 13.8 21.1 42.8 71.9

Surface-water PO4-P (ug/L) 13.5 Decline (z-) 10.0 10.9 13.5 14.8 17.3

13.5 Increase (z+) 12.1 13.2 14.3 28.4 45.1

Periphyton C:P ratio (OM) 421 Decline *(z-) 279 283 379 457 485

737 Increase* (z+) 356 379 612 973 1187

Periphyton C:P ratio (bulk) 775 Decline* (z-) 182 235 749 815 844

775 Increase* (z+) 362 749 844 1328 1328

Surface-water DIN (ug/L) 189.4 Decline (z-) 159.3 159.3 189.4 446.5 446.5

1827.1 Increase (z+) 446.5 446.5 1827.1 3981.5 3981.5

Surface-water TN (ug/L) 435.5 Decline (z-) 362.0 362.0 423.0 753.0 753.0

1720.0 Increase (z+) 753.0 753.0 2080.0 4080.0 4080.0
R.S. King et al. 2009 69 

Figure 27. Results of Threshold Indicator Taxa ANalysis (TITAN) using periphyton C:P ratio (bulk) as a 

predictor of threshold changes in individual algal species during August 2006 and September 2007. Taxa 

are classified as either negative (z-) or positive (z+) threshold indicators based on the direction of 

response to TP. Note that lower C:P values = higher P enrichment relative to organic carbon in 

the periphyton, thus taxa that are negative indicators (z-) in these results are associated with 

higher levels of P-enrichment,, whereas positive threshold taxa are associated with lower levels 

of P enrichment (high C:P ratios). See Figure 26 for other graphical details. See Tables 8 and 9 for 

community level (sum(z)) thresholds, and Tables 11 and 13 for taxa-specific thresholds. 

August 2006 September 2007

Increasing P enrichment Increasing P enrichment
R.S. King et al. 2009 70 

Figure 28. Results of Threshold Indicator Taxa ANalysis (TITAN) using periphyton C:P ratio (organic 

fraction, sediment removed) as a predictor of threshold changes in individual algal species during August 

2006 and September 2007. Taxa are classified as either negative (z-) or positive (z+) threshold indicators 

based on the direction of response to TP. Note that lower C:P values = higher P enrichment 

relative to organic carbon in the periphyton, thus taxa that are negative indicators (z-) in these 

results are associated with higher levels of P-enrichment,, whereas positive threshold taxa are 

associated with lower levels of P enrichment (high C:P ratios). See Figure 26 for other graphical 

details. See Tables 8 and 9 for community level (sum(z)) thresholds, and Tables 11 and 13 for taxaspecific thresholds. 

Increasing P enrichment Increasing P enrichment

August 2006 September 2007
R.S. King et al. 2009 71 

Table 10. Taxa-specific results from Threshold Indicator Taxa Analysis (TITAN) on algal species 

composition in response to surface-water total phosphorus (TP, ug/L) in August 2006 (n=26 sites). 

Only species that showed significant threshold declines or increases to surface-water (TP) are included 

in this table. The observed (Obs) threshold value of TP for each taxon is shown in bold, whereas lower 

(10%), middle (50%), and upper (90%) quantiles of 1,000 bootstraps represent measures of uncertainty 

around the observed threshold. Z represents the standardized indicator score from TITAN (larger 

numbers = stronger threshold response), IndVal is the unstandardized indicator score (scaled from 0-

100%, with 100=perfect indicator). Purity is the relative consistency of the response direction among the 

1,000 bootstraps (purity > 0.95 is significant). P-value is the likelihood of getting an equal or larger 

IndVal if the score were computed with random shuffling of the observed data (P<0.05 is significant). 

See Appendix 1 for full species names corresponding to Taxon IDs. 

Cumulative Threshold 

Quantiles 

Taxon ID Obs. 

Response 

> obs. 

value z IndVal Purity 

P

value 10% 50% 90% 

AHminuti 39.7 Decline 5.97 87.76 0.994 <0.004 27.2 39.7 437.2

BRvitrea 19.9 Decline 5.37 70.39 0.998 <0.004 18.5 22.8 39.7

CAsilicu 19.9 Decline 2.93 39.70 0.960 0.044 14.4 20.6 32.4

CMdelcat 18.5 Decline 5.41 55.56 0.998 0.008 11.7 17.4 19.9

DEkuetzi 19.2 Decline 3.95 68.49 0.998 0.012 17.4 20.6 141.7

ECcarina 18.5 Decline 3.01 33.33 0.958 0.040 12.1 18.5 22.8

ECsilesi 30.4 Decline 3.40 60.53 0.982 0.012 17.4 27.2 54.2

EYevergl 19.9 Decline 5.94 75.95 1.000 <0.004 17.4 20.6 27.2

EYmicroc 19.9 Decline 6.22 77.91 1.000 <0.004 17.4 20.6 27.2

MSellipt 14.4 Decline 5.02 57.14 0.982 <0.004 10.8 14.4 19.2

MSsmithi 20.6 Decline 5.09 50.00 0.998 <0.004 11.7 19.9 24.4

NAcrypto 30.4 Decline 2.69 43.75 0.982 0.028 14.4 22.8 39.7

NAcryten 19.9 Decline 4.35 65.68 1.000 0.008 19.2 27.2 54.2

NAexilis 19.2 Decline 3.05 30.00 0.962 0.024 10.8 14.4 20.6

NAradios 30.4 Decline 4.54 67.68 0.984 <0.004 19.2 27.2 54.2

NArhynch 24.4 Decline 2.92 42.86 0.962 0.012 19.2 24.4 32.4

NIampoid 22.8 Decline 3.42 48.77 0.990 0.016 17.4 22.8 39.7

RPgibba 24.4 Decline 4.97 71.87 1.000 <0.004 17.4 27.2 54.2

SRconstr 27.2 Decline 2.87 47.38 0.994 0.028 10.8 19.9 39.7

SYacus 20.6 Decline 5.60 75.66 1.000 <0.004 17.4 20.6 30.4

ANBsp 18.5 Decline 3.13 57.14 0.984 0.012 12.1 19.2 141.7

BULsp 14.4 Decline 4.30 62.60 0.978 <0.004 11.7 17.4 20.6

CLOsp2 19.9 Decline 3.03 36.36 0.974 0.040 14.4 19.9 24.4

COSbotry 20.6 Decline 5.73 66.67 1.000 <0.004 12.1 19.2 27.2

COSgaler 18.5 Decline 4.01 65.53 0.988 0.008 14.4 19.2 54.2

GLHsp 22.8 Decline 2.38 57.28 0.964 0.024 14.4 22.8 437.2

SCYsp 17.4 Decline 3.24 37.50 0.954 0.036 10.8 17.4 20.6

SCZsp 1853.3 Decline 3.71 53.86 0.996 <0.004 19.9 32.4 1250.2
R.S. King et al. 2009 72 

SPOpulch 27.2 Decline 2.77 40.00 0.956 0.020 17.4 24.4 39.7

STAsp 18.5 Decline 3.30 33.33 0.962 0.024 10.8 17.4 20.6

AHexigum 141.7 Increase 3.40 72.59 0.994 0.012 18.5 32.4 437.2

AMgranul 1853.3 Increase 8.83 92.88 0.964 <0.004 32.4 1250.2 1853.3

AMveneta 437.2 Increase 8.45 80.00 0.992 <0.004 39.7 437.2 1853.3

BApardxa 32.4 Increase 4.08 58.27 0.998 0.012 19.2 27.2 54.2

CCpedcls 437.2 Increase 8.34 60.00 0.954 <0.004 27.2 437.2 1853.3

CCplacen 22.8 Increase 7.53 83.85 1.000 <0.004 18.5 22.8 30.4

CYmenegh 39.7 Increase 4.80 73.39 1.000 <0.004 19.2 30.4 141.7

DIconfer 30.4 Increase 6.32 82.53 1.000 <0.004 22.8 32.4 437.2

FAtener2 32.4 Increase 4.01 49.79 0.992 <0.004 19.9 32.4 141.7

GMlinfor 32.4 Increase 5.21 62.98 0.974 <0.004 24.4 39.7 1250.2

HIhunga 39.7 Increase 4.83 71.10 0.982 <0.004 20.6 30.4 141.7

NAsancru 24.4 Increase 4.32 50.00 1.000 <0.004 20.6 32.4 1853.3

NAsavana 1853.3 Increase 5.80 62.68 0.958 0.008 22.8 1250.2 1853.3

NAtexana 39.7 Increase 4.23 44.83 0.998 0.008 22.8 36.1 1853.3

NIamphib 27.2 Increase 4.05 54.51 1.000 <0.004 11.7 19.2 54.2

NIfrustu 32.4 Increase 7.08 87.20 1.000 <0.004 19.2 30.4 141.7

NIincons 19.2 Increase 3.55 68.12 0.998 <0.004 18.5 24.4 54.2

PRlaevis 54.2 Increase 6.75 71.43 0.998 <0.004 32.4 141.7 1853.3

REsinuta 27.2 Increase 4.63 66.23 0.966 <0.004 19.2 27.2 41.2

ROabbre 32.4 Increase 3.73 40.42 0.990 0.008 20.6 32.4 437.2

SFseminu 22.8 Increase 3.72 46.15 0.994 0.020 19.9 27.2 54.2

TBfascic 39.7 Increase 7.63 82.70 0.992 <0.004 27.2 39.7 437.2

TEmusica 54.2 Increase 6.98 71.43 0.994 <0.004 32.4 141.7 1853.3

CHOsp 1250.2 Increase 2.57 53.57 0.976 0.016 19.2 32.4 1853.3

CLAglome 32.4 Increase 4.34 67.04 0.986 <0.004 19.2 30.4 54.2
R.S. King et al. 2009 73 

Table 11. Taxa-specific results from Threshold Indicator Taxa Analysis (TITAN) on algal species 

composition in response to C:P ratio in the periphyton (bulk, no separation from sediment) in August 

2006 (drought, n=26 sites). Only species that showed significant threshold declines or increases to 

periphyton C:P (bulk) are included in this table. The observed (Obs) threshold value of C:P for each 

taxon is shown in bold. *Note that lower C:P values = higher P enrichment relative to organic carbon in 

the periphyton, thus taxa that “decrease” sharply in response to increasing C:P are associated with 

higher levels of P-enrichment,, whereas “increaser” taxa are associated with lower levels of P 

enrichment. 

Cumulative Threshold 

Quantiles 

Taxon ID Obs. 

Response 

> obs. 

value z IndVal Purity P value 10% 50% 90% 

ACbiasso 859.2 Increase* 3.94 66.74 0.972 <0.004 398.8 724.5 950.8

AHminuti 658.6 Increase* 5.35 76.65 1.000 <0.004 275.1 398.8 724.5

BRvitrea 724.5 Increase* 7.25 85.66 1.000 <0.004 546.4 729.1 859.2

BULsp 779.8 Increase* 3.90 53.41 0.992 0.008 546.4 740.0 950.8

CAsilicu 465.1 Increase* 3.18 40.00 0.974 0.028 333.1 658.6 779.8

CLOsp2 1030.0 Increase* 6.01 70.87 0.990 <0.004 729.1 950.8 1218.3

CMdelcat 859.2 Increase* 6.73 71.43 1.000 <0.004 740.0 925.2 1218.3

COSbotry 1218.3 Increase* 3.98 83.30 0.998 <0.004 398.8 779.8 1218.3

COSgaler 740.0 Increase* 5.86 79.48 1.000 <0.004 465.1 729.1 859.2

DEkuetzi 729.1 Increase* 3.95 68.49 0.990 0.008 658.6 740.0 950.8

ECcarina 859.2 Increase* 4.07 42.86 0.966 0.028 711.2 925.2 1218.3

ECsilesi 333.1 Increase* 3.92 66.93 0.996 <0.004 283.8 546.4 925.2

EYevergl 724.5 Increase* 8.11 90.91 1.000 <0.004 546.4 729.1 859.2

EYmicroc 724.5 Increase* 8.18 90.91 1.000 <0.004 546.4 729.1 859.2

FRcapuci 779.8 Increase* 4.40 64.66 0.978 <0.004 658.6 740.0 925.2

FRnanana 1030.0 Increase* 6.70 70.64 0.994 <0.004 740.0 950.8 1218.3

GOgracil 859.2 Increase* 4.96 74.19 0.992 <0.004 711.2 859.2 1030.0

GOvibdes 925.2 Increase* 6.33 50.00 0.960 <0.004 729.1 925.2 1030.0

MERglauc 658.6 Increase* 4.79 71.00 0.980 <0.004 333.1 658.6 729.1

MICsp 740.0 Increase* 1.69 57.43 0.960 0.044 255.8 658.6 925.2

MSellipt 950.8 Increase* 4.58 55.96 0.986 0.008 724.5 925.2 1218.3

MSsmithi 724.5 Increase* 4.86 54.55 0.998 0.012 546.4 729.1 1218.3

NAcrypto 398.8 Increase* 3.07 43.75 0.974 0.016 330.4 658.6 859.2

NAcryten 724.5 Increase* 4.86 68.32 1.000 <0.004 398.8 724.5 859.2

NAexilis 729.1 Increase* 2.75 30.00 0.950 0.048 546.4 740.0 1030.0

NAradios 711.2 Increase* 3.37 61.58 0.994 0.008 273.0 465.1 740.0

NIampoid 658.6 Increase* 4.84 61.54 0.994 0.008 333.1 658.6 740.0

PERsp 724.5 Increase* 5.52 54.55 0.998 <0.004 546.4 729.1 925.2

RPgibba 333.1 Increase* 3.88 66.94 0.994 <0.004 275.1 465.1 740.0

SCYsp 950.8 Increase* 6.80 60.00 0.956 <0.004 740.0 990.4 1218.3

SCZsp 255.8 Increase* 2.80 52.84 0.978 0.012 255.8 398.8 859.2
R.S. King et al. 2009 74 

SPIsp 740.0 Increase* 3.85 60.21 0.980 <0.004 465.1 779.8 950.8

SPLsp 711.2 Increase* 3.91 56.36 0.956 0.008 465.1 724.5 859.2

SRconstr 333.1 Increase* 3.30 52.94 0.958 0.016 283.8 398.8 711.2

STAsp 859.2 Increase* 4.68 42.86 0.958 0.012 724.5 950.8 1218.3

SYacus 740.0 Increase* 8.28 92.42 1.000 <0.004 658.6 729.1 859.2

AHexigum 859.2 Decrease* 4.62 78.95 1.000 <0.004 398.8 779.8 950.8

AMinarie 724.5 Decrease* 5.18 75.36 0.980 <0.004 465.1 724.5 859.2

AMpedcls 465.1 Decrease* 4.23 60.72 0.988 <0.004 306.4 546.4 729.1

AMveneta 255.8 Decrease* 5.86 70.99 0.984 0.008 255.8 273.0 306.4

BApardxa 465.1 Decrease* 4.45 57.57 0.998 <0.004 306.4 546.4 729.1

CCplacen 306.4 Decrease* 5.22 81.46 0.998 <0.004 275.1 333.1 724.5

CHOsp 398.8 Decrease* 3.05 52.60 0.976 <0.004 275.1 398.8 729.1

CLAglome 398.8 Decrease* 4.31 67.96 1.000 <0.004 398.8 740.0 1030.0

CRYsp 398.8 Decrease* 3.30 49.39 0.958 0.016 274.9 398.8 740.0

CYmenegh 465.1 Decrease* 5.55 73.04 0.998 <0.004 275.1 398.8 711.2

DIconfer 306.4 Decrease* 7.31 91.22 1.000 <0.004 275.1 333.1 546.4

FAlenzii 275.1 Decrease* 3.40 71.86 0.986 0.008 273.0 333.1 950.8

FAtener2 333.1 Decrease* 3.29 49.79 0.992 0.008 275.1 398.8 729.1

GMlinfor 333.1 Decrease* 3.86 48.26 0.986 <0.004 255.8 333.1 729.1

GOparvul 1030.0 Decrease* 3.65 77.92 0.950 <0.004 465.1 950.8 1218.3

HIhunga 333.1 Decrease* 5.60 74.27 0.998 <0.004 283.8 398.8 724.5

MEvarian 255.8 Decrease* 4.69 46.93 0.952 0.012 255.8 273.0 546.4

NAsancru 546.4 Decrease* 4.42 50.00 0.998 <0.004 275.1 398.8 711.2

NAtexana 546.4 Decrease* 3.57 41.67 0.992 <0.004 275.1 465.1 724.5

NIamphib 950.8 Decrease* 3.01 54.67 0.996 0.012 275.1 724.5 1030.0

NIangust 779.8 Decrease* 3.97 66.67 0.962 0.008 658.6 779.8 925.2

NIfrustu 465.1 Decrease* 6.62 83.23 1.000 <0.004 306.4 546.4 729.1

NIincons 950.8 Decrease* 5.95 90.48 0.996 <0.004 724.5 925.2 1218.3

PIgibba 658.6 Decrease* 3.73 38.46 0.954 0.028 333.1 658.6 729.1

PRlaevis 306.4 Decrease* 5.83 62.50 0.994 <0.004 255.8 306.4 465.1

PTlanceo 779.8 Decrease* 4.47 66.67 0.998 0.008 304.1 658.6 740.0

REsinuta 398.8 Decrease* 3.90 63.69 0.996 0.008 275.1 465.1 859.2

ROabbre 283.8 Decrease* 4.62 53.44 0.996 0.008 273.0 306.4 658.6

SFseminu 465.1 Decrease* 4.43 54.55 0.998 0.008 275.1 398.8 711.2

TBfascic 333.1 Decrease* 6.56 72.75 0.998 <0.004 275.1 333.1 546.4

TEmusica 306.4 Decrease* 5.23 62.50 0.990 <0.004 255.8 283.8 465.1
R.S. King et al. 2009 75 

Table 12. Taxa-specific results from Threshold Indicator Taxa Analysis (TITAN) on algal species 

composition in response to surface-water total phosphorus (TP, ug/L) in September 2007 (post-flood, 

n=26 sites). Only species that showed significant threshold declines or increases to surface-water (TP)

are included in this table. The observed (Obs) threshold value of TP for each taxon is shown in bold. 

Cumulative 

Threshold Quantiles 

Taxon ID Obs. 

Response 

> obs. 

value Z IndVal Purity 

Pr. 

(Type I) 10% 50% 90%

ACbiasso 12.9 Decrease 4.45 63.21 1.000 <0.004 12.3 15.6 26.5

AHminuti 32.9 Decrease 4.56 76.14 1.000 <0.004 13.8 26.5 115.8

CMexcisa 12.9 Decrease 3.57 55.34 0.982 0.008 10.9 12.9 24.5

CMkolbei 32.9 Decrease 6.15 88.52 0.998 <0.004 21.0 32.9 115.8

DEkuetzi 24.5 Decrease 4.08 71.96 1.000 <0.004 12.9 21.0 42.8

EYmicroc 12.3 Decrease 3.12 56.18 0.976 0.020 10.9 12.9 26.5

NAexilis 12.9 Decrease 5.02 72.15 1.000 <0.004 12.8 13.8 32.9

NAstroem 17.6 Decrease 4.12 64.13 0.988 0.008 12.8 15.6 32.9

SYulna 149.3 Decrease 4.17 66.36 0.978 <0.004 17.6 42.8 529.3

COSsp 13.1 Decrease 2.60 61.08 0.984 0.016 12.3 13.1 32.9

MERglauc 26.5 Decrease 3.83 58.82 0.968 0.016 13.1 21.0 42.8

AHexigum 24.5 Increase 5.56 60.00 0.996 <0.004 15.6 24.5 42.8

AMbullat 24.5 Increase 3.08 30.00 0.956 0.028 15.6 26.5 45.7

AMlibyca 12.9 Increase 4.00 58.82 0.984 0.012 12.3 13.1 21.0

AMpedcls 24.5 Increase 3.63 54.59 0.998 0.008 12.8 13.8 26.5

AMveneta 21.0 Increase 2.73 27.27 0.960 <0.004 15.6 24.5 42.8

CAbacill 12.9 Increase 6.03 82.35 1.000 <0.004 11.6 12.9 13.8

CCplacen 24.5 Increase 3.23 64.12 0.992 0.012 12.3 13.8 26.5

DIconfer 13.1 Increase 4.14 56.25 0.998 0.008 12.9 15.6 32.9

GOparvul 24.5 Increase 4.05 68.06 0.994 0.012 10.9 17.6 32.9

GOpumilu 42.8 Increase 4.74 74.47 0.978 <0.004 13.8 24.5 71.9

GOrhombi 149.3 Increase 4.34 45.34 0.954 0.048 15.6 42.8 529.3

GYnodfrm 26.5 Increase 3.46 33.33 0.964 0.020 17.6 26.5 71.9

NAantoni 26.5 Increase 3.08 33.33 0.952 0.032 15.6 26.5 529.3

NAminima 13.8 Increase 2.87 35.71 0.966 0.008 13.1 17.6 149.3

NArecens 17.6 Increase 6.05 80.36 0.992 <0.004 13.1 17.6 26.5

NAsancru 26.5 Increase 4.35 44.44 0.992 0.012 17.6 26.5 71.9

NAsubmin 26.5 Increase 5.50 55.56 0.998 <0.004 17.6 32.9 149.3

NAvirdla 24.5 Increase 4.19 50.00 0.994 0.008 15.6 24.5 76.3

NIincons 24.5 Increase 5.62 76.31 1.000 <0.004 12.9 17.6 32.9

NIpalea 21.0 Increase 3.72 45.45 0.994 0.016 13.8 24.5 115.8

PIgibba 13.1 Increase 3.67 53.33 0.988 <0.004 12.8 13.1 24.5

PRlaevis 26.5 Increase 6.96 85.44 1.000 <0.004 15.6 24.5 42.8
R.S. King et al. 2009 76 

REsinuta 32.9 Increase 4.76 72.10 0.998 <0.004 11.6 13.8 42.8

SUbreb 26.5 Increase 4.61 60.28 1.000 <0.004 13.8 24.5 115.8

TEmusica 24.5 Increase 5.66 60.00 1.000 <0.004 17.6 26.5 115.8

THweiss 32.9 Increase 4.19 37.50 0.966 0.012 17.6 32.9 115.8

TYlevid 24.5 Increase 4.03 40.00 0.984 0.012 15.6 24.5 71.9

ANKfalca 24.5 Increase 3.71 59.42 0.980 0.016 12.9 17.6 32.9

CLAglome 17.6 Increase 3.79 64.41 0.980 <0.004 12.9 17.6 42.8

XBGcoc 115.8 Increase 5.64 76.12 0.978 <0.004 32.9 115.8 529.3

Table 13. Taxa-specific results from Threshold Indicator Taxa Analysis (TITAN) on algal species 

composition in response to C:P ratio in the periphyton (bulk, no separation from sediment) in 

September 2007 (drought, n=26 sites). Only species that showed significant threshold declines or 

increases to periphyton C:P (bulk) are included in this table. The observed (Obs) threshold value of 

C:P for each taxon is shown in bold. Note that lower C:P values = higher P enrichment relative to organic 

carbon in the periphyton, thus taxa that “decrease” sharply in response to increasing C:P are associated 

with higher levels of P-enrichment,, whereas “increaser” taxa are associated with lower levels of P 

enrichment. 

Cumulative Threshold 

Quantiles 

Taxon ID Obs. 

Response 

> obs. 

value Z IndVal Purity P value 10% 50% 90% 

ACbiasso 793.0 Increase* 3.70 54.99 0.994 0.016 234.9 775.2 886.0

AHminuti 204.9 Increase* 3.51 69.35 0.972 0.004 165.7 234.9 817.7

CMdelcat 993.1 Increase* 3.47 65.32 0.972 0.012 362.3 843.5 1207.6

CMexcisa 775.2 Increase* 4.36 57.94 0.990 0.004 362.3 793.0 1303.4

CMkolbei 204.9 Increase* 4.94 81.92 1.000 0.004 165.7 204.9 775.2

DEkuetzi 775.2 Increase* 4.26 71.73 1.000 0.008 204.9 749.4 843.5

EYcesati 793.0 Increase* 2.96 33.33 0.984 0.020 772.6 886.0 1303.4

EYevergl 775.2 Increase* 3.05 50.34 0.952 0.016 263.5 793.0 1207.6

EYmicroc 775.2 Increase* 4.92 58.10 0.996 0.004 362.3 793.0 1109.9

GOintvib 1303.4 Increase* 4.16 45.86 0.964 0.024 843.5 1303.4 1327.6

GOmaclau 1303.4 Increase* 3.36 61.24 0.956 0.024 263.5 1109.9 1327.6

NAexilis 775.2 Increase* 4.35 64.18 1.000 0.004 204.9 592.0 843.5

NAstroem 775.2 Increase* 3.66 59.11 0.950 0.012 234.9 775.2 993.1

NIsinde 1303.4 Increase* 4.26 67.35 0.984 0.008 775.2 1109.9 1327.6

SYacus 1303.4 Increase* 2.54 60.60 0.950 0.020 362.3 886.0 1327.6

SYulna 204.9 Increase* 3.58 61.58 0.954 0.008 155.0 182.3 362.3

COEsp 793.0 Increase* 3.75 41.67 0.994 0.004 749.4 843.5 1327.6
R.S. King et al. 2009 77 

HOMsp 1327.6 Increase* 4.98 63.39 0.958 0.012 814.8 1255.5 1327.6

MERglauc 263.5 Increase* 3.82 58.82 1.000 0.016 204.9 775.2 896.7

OEDsp 775.2 Increase* 3.08 38.46 0.980 0.048 362.3 814.8 1109.9

OSCsp 1109.9 Increase* 3.24 68.82 0.990 0.004 204.9 886.0 1207.6

AHexigum 793.0 Decrease* 4.06 42.86 0.996 0.008 182.3 592.0 814.8

AMmontan 775.2 Decrease* 3.05 38.46 0.974 0.040 155.0 592.0 814.8

AMpedcls 592.0 Decrease* 5.57 70.16 1.000 0.004 263.5 749.4 843.5

AMveneta 362.3 Decrease* 2.78 30.00 0.958 0.048 182.3 362.3 775.2

CCplacen 793.0 Decrease* 5.14 72.90 0.998 0.004 592.0 814.8 1303.4

CYmenegh 775.2 Decrease* 4.04 65.60 0.992 0.008 362.3 814.8 1109.9

DIconfer 775.2 Decrease* 6.82 69.23 1.000 0.004 234.9 749.4 843.5

GOparvul 592.0 Decrease* 5.11 72.24 1.000 0.004 263.5 793.0 1207.6

GOpumilu 204.9 Decrease* 5.12 74.47 0.990 0.004 165.7 234.9 775.2

GYnodfrm 263.5 Decrease* 3.11 33.33 0.972 0.036 165.7 234.9 749.4

MEvarian 592.0 Decrease* 5.33 58.63 0.990 0.004 234.9 749.4 843.5

NAantoni 263.5 Decrease* 3.00 33.33 0.962 0.036 155.0 234.9 775.2

NAkotsch 793.0 Decrease* 4.08 62.24 0.956 0.004 569.0 793.0 993.1

NAminima 793.0 Decrease* 3.00 35.71 0.978 0.008 165.7 592.0 843.5

NArecens 182.3 Decrease* 3.38 69.06 0.982 0.012 165.7 263.5 843.5

NAsubmin 263.5 Decrease* 4.84 55.56 0.998 0.004 155.0 234.9 592.0

NAvirdla 775.2 Decrease* 3.17 38.46 0.994 0.032 165.7 263.5 793.0

NIincons 592.0 Decrease* 8.00 86.56 1.000 0.004 234.9 749.4 843.5

PRlaevis 204.9 Decrease* 4.98 72.21 0.998 0.004 165.7 263.5 775.2

REsinuta 592.0 Decrease* 5.25 71.84 0.998 0.004 204.9 362.3 867.0

SUangust 592.0 Decrease* 3.57 36.36 0.986 0.020 165.7 362.3 793.0

SUbreb 263.5 Decrease* 5.20 64.36 1.000 0.004 165.7 263.5 793.0

TEmusica 263.5 Decrease* 5.66 66.67 1.000 0.004 165.7 263.5 749.4

CLAglome 182.3 Decrease* 2.94 68.42 0.988 0.016 165.7 592.0 993.1

XBGcoc 155.0 Decrease* 4.55 67.72 0.958 0.004 155.0 182.3 362.3
R.S. King et al. 2009 78 

RESULTS AND INTERPRETATION, CONTINUED 

Macroinvertebrate Taxonomic Responses to TP

Results of ordinations and Threshold Indicator Taxa Analysis (TITAN) on macroinvertebrate 

taxonomic composition during November 2006 showed that nutrients, specifically phosphorus, 

corresponded to shifts in community structure (Figure 30, 31). Because composition differed 

substantially between sites with and without flow, and because of established relationship 

between flow and other water chemistry variables (e.g., DO), we separated sites into groups by 

flow status prior to analysis. 

Sites with no flow yielded more reliable relationships to TP than sites with flow, but this result 

should be interpreted with the caveat that there were only 9 sites in the group with flow. 

Ordinations clearly showed that in both sets of analyses, the sites with low TP and high 

periphyton C:P sorted on the left side of axis 1, whereas high TP and low C:P sites fell out on the 

right side of axis 1 (Figure 30). 

In both sets of TITAN analyses, more taxa were classified as negative threshold indicators 

(decline in response to TP) than as positive ones (increase). A few of the negative indicators of 

note included the stoneflies Zealeuctra and Perlesta, the caddisflies Chimarra, and Hydroptila, 

the mayfly Neochoroterpes nanita, and the dryopid beetle Postelichus. Notable increasers 

included the filter-feeding caddisfly Cheumatopsyche, the amphipod Hyallela azteca, the 

fingernail clam Sphaeriidae, and aquatic worms (Oligochaeta). 

Because macroinvertebrates are so highly tied to flow, these results likely represent a weak 

representation of the potential influence of nutrient enrichment on macroinvertebrates. Many of 

the sites had gone dry or nearly so between August and October 2006, so some of the sites with 

flow during this event had been reduced to tiny disconnected pools in weeks prior to sampling. 

We anticipate producing a larger macroinvertebrate data set (J. A. Back, Ph. D dissertation) 

spanning more flow conditions, and therein more completely evaluate macroinvertebrate 

responses to TP enrichment. 
R.S. King et al. 2009 79 

Figure 30. Nonmetric multidimensional scaling ordination of macroinvertebrate species composition 

during a period of drought in November 2006. Dissimilarity (Bray-Curtis) was computed using logtransformed densities (no./m2) per taxon. Ordinations were conducted separately for sites with detectable 

flow (n=9) and those without flow (n=16; one site had insufficient surface water for macroinvertebrate 

sampling and was removed). Red arrows (vectors) correspond to significant predictors of taxonomic 

composition. 

Flowing(n=9) Non‐flowing(n=16)

Macroinvertebrate species composition,November 2006 (severe drought)  
R.S. King et al. 2009 80 

Table 14. Community-level results from Threshold Indicator Taxa Analysis (TITAN) on 

macroinvertebrate species composition in response to surface-water TP and periphyton C:P 

ratio (bulk) during November 2006 (severe drought, n=26 sites). Analyses were conducted 

separately for sites that had measureable flow preceding and during the sampling (flow, n=9), 

and those that were not flowing (no flow, n=16). Thresholds (Obs.) are based on the value of the 

predictor resulting in the greatest aggregate decrease (sum(z-)) or increase (sum(z+)) in the 

frequency and abundance of taxa in the community. See Figure 31 for details. 

Bootstrap Cumulative Distribution of 

Thresholds 

Data set Predictor Method Obs. 5% 10% 50% 90% 95%

No flow 

(n=16) 

Surface-water 

TP (ug/L) 

Decline 

(sumz-) 16.3 16.3 16.3 18.3 20.2 20.2

Increase 

(sumz+) 17.5 18.3 19.3 27.3 120.1 120.1

No flow 

(n=16) 

Periphyton C:P 

ratio (bulk) 

Decline 

(sumz-) 230.1 152.1 152.1 195.4 230.1 259.1

Increase 

(sumz+) 373.6 173.5 195.4 230.1 373.6 373.6

Flow (n=9) 

Surface-water 

TP (ug/L) 

Decline 

(sumz-) 10.7 10.7 10.7 14.8 850.4 1636.5

Increase 

(sumz+) 409.2 10.7 14.8 850.4 1636.5 1636.5

Flow (n=9) 

Periphyton C:P 

ratio (bulk) 

Decline 

(sumz-) 195.6 151.8 151.8 195.6 290.9 359.2

Increase 

(sumz+) 359.2 195.6 195.6 290.9 359.2 359.2
R.S. King et al. 2009 81 

Figure 31. Results of TITAN using surface-water TP as a predictor of threshold changes in 

macroinvertebrate taxa abundances from sites with flow (n=9) and those without flow (n=16) in 

November 2006. 

Flowing(n=9) Non‐flowing (n=16)
R.S. King et al. 2009 82 

Table 15. Taxa-specific results from Threshold Indicator Taxa Analysis (TITAN) on 

macroinvertebrate species composition in response to surface-water TP during November 

2006 (severe drought, n=26 sites). Analyses were conducted separately for sites that had 

measureable flow preceding and during the sampling (flow, n=9), and those that were not 

flowing (no flow, n=16). Only species that showed significant threshold declines or increases to 

surface-water (TP) are included in this table. The observed (Obs) threshold value of TP for 

each taxon is shown in bold. See Table 14 and Figure 31 for details. 

Cumulative Threshold 

Quantiles 

Taxon ID Obs. 

Response 

> Obs. 

Value z IndVal Purity 

Pvalue 10% 50% 90%

Non-flowing sites (n=16) 

AMBRpulc 20.2 Decline 3.82 75.00 0.990 0.020 16.3 20.2 27.3

Argia 23.7 Decline 5.63 95.20 1.000 <0.004 18.3 23.7 29.3

Atrichop 16.3 Decline 4.60 50.00 0.872 0.048 16.3 16.3 17.5

Chimarra 16.3 Decline 7.29 50.00 0.878 0.016 16.3 16.3 20.2

CORBflun 16.3 Decline 3.19 80.09 0.966 0.012 16.3 19.3 53.7

Culicoid 23.7 Decline 2.64 66.48 0.942 0.024 17.5 23.7 53.7

Drepanot 16.3 Decline 5.09 50.00 0.880 0.040 16.3 16.3 18.3

Enochrus 16.3 Decline 5.16 50.00 0.872 0.036 16.3 16.3 17.5

Glossiph 16.3 Decline 3.40 81.85 0.980 <0.004 16.3 19.3 27.3

Hydracri 20.2 Decline 2.63 63.98 0.872 0.024 17.5 20.2 53.7

Hydropti 16.3 Decline 3.17 70.72 0.950 0.012 16.3 17.5 23.7

Limnophi 16.3 Decline 4.98 50.00 0.880 0.040 16.3 16.3 18.3

NEOCnani 23.7 Decline 2.61 55.56 0.984 0.044 16.3 19.3 27.3

Perlesta 16.3 Decline 5.29 50.00 0.872 0.036 16.3 16.3 20.2

Planorbe 16.3 Decline 2.75 74.33 0.938 0.032 16.3 19.3 53.7

POSTimms 16.3 Decline 4.83 89.02 0.988 <0.004 16.3 17.5 23.7

Tanypodi 29.3 Decline 3.13 62.64 0.982 0.008 17.5 23.7 53.7

Copepoda 16.3 Increase 4.05 56.40 0.884 <0.004 16.3 17.5 27.3

Elodes 53.7 Increase 4.98 50.00 0.884 0.040 16.3 53.7 120.1

Oligocha 18.3 Increase 2.96 53.86 0.982 0.024 16.3 19.3 29.3

Sphaerii 27.3 Increase 2.60 67.64 0.876 0.024 19.3 27.3 53.7

Flowing sites (n=9) 

Berosus 409.2 Decline 1.82 75.40 0.904 0.032 10.7 409.2 1636.5

Bezzia 409.2 Decline 3.14 91.83 0.994 0.020 10.7 409.2 1636.5

CAENlati 409.2 Decline 2.57 82.76 0.982 0.040 10.7 409.2 1636.5

Chimarra 850.4 Decline 2.76 76.34 0.894 <0.004 14.8 850.4 1636.5

Culicoid 409.2 Decline 2.62 86.52 0.988 0.020 10.7 409.2 1636.5

Hydracri 10.7 Decline 3.48 100.00 0.964 0.024 10.7 10.7 409.2
R.S. King et al. 2009 83 

Physella 409.2 Decline 1.97 75.05 0.930 0.028 10.7 409.2 1636.5

Tabanus 409.2 Decline 2.26 82.85 0.972 0.040 10.7 409.2 1636.5

Zealuctr 10.7 Decline 3.03 66.67 0.880 0.084 10.7 10.7 409.2

BRECmend 1636.5 Increase 4.82 100.00 0.894 <0.004 10.7 1636.5 1636.5

Cheumato 10.7 Increase 3.08 100.00 0.960 <0.004 10.7 10.7 409.2

HYALazte 409.2 Increase 1.71 72.75 0.894 0.100 10.7 409.2 1636.5

PSEPtexa 1636.5 Increase 2.05 82.98 0.910 0.100 10.7 850.4 1636.5
R.S. King et al. 2009 84 

SECTION 2: EXPERIMENTAL STREAM STUDY 
R.S. King et al. 2009 85 

SITE DESCRIPTION 

Experimental evidence derived from controlled studies is important for the development of 

scientifically defensible water quality standards, yet most studies are limited only to field 

observations from natural lakes, wetlands, and streams because these habitats are too large and 

complex to manipulate experimentally. The intention of the Baylor Experimental Aquatic 

Research (BEAR) facility in Waco, TX, is to bridge the gap between field observations, which 

represent the habitat of interest but may be influenced by many interacting chemicals or other 

aquatic stressors simultaneously, and laboratory or small field experiments, which allow for 

control of environmental variables yet are too small and unrepresentative of natural conditions to 

be realistic. Because of its size (over 30,000 square feet), outdoor location, and close proximity 

to natural aquatic habitats the BEAR facility is a unique, state-of-the-art resource for conducting 

controlled yet realistic water research studies (Figure 32). 

The BEAR facility supports 12 experimental stream mesocosms. The facility is located at the 

Lake Waco Wetlands, an 80-hectare constructed wetland. Water is pumped from the outflow of 

cell 2 of the 5-cell wetland to the BEAR facility. Here, the water has relatively low levels of 

dissolved N and P and thus provides high-quality source water for a nutrient addition 

experiment. 

Water from the wetland is delivered to the BEAR facility via a 2500 L/min pump. Water is 

pumped into a 160,000 L water tower and delivered in controlled volumes to the streams via a 

series of 12 valves, each adjusted to regulate flow equivalently among all 12 streams (180 

L/min). Before entering the streams, water is pumped into a mixing tank where chemicals 

representing experimental treatments can be dosed at a fixed rate using peristaltic pumps 

connected to a second, adjacent tank of chemical stock solution. Once dosed with the treatment 

levels of non-toxic chemicals, water is released into the streams and is discharged back into the 

Lake Waco Wetlands. 

The experimental streams are approximately 0.6 m wide and 20 m in length. The streams are 

stratified into riffle, glide, and pool sections and are designed mimic natural habitat in central 

Texas streams. A riffle is a high-gradient habitat that is usually fast-flowing, quite shallow, and 

usually composed of gravel or cobble substrate. Glides are also shallow but usually much slower 

in water velocity. Pools are deep, slow areas in streams. Each of these habitats tends to support 

different species with specific adaptations to those particular environments. 

The BEAR stream facility was covered by a canopy that excluded 60% of incoming solar 

radiation to approximate riparian canopies of a typical stream in the field study (800-1,000 μE, 

RSK, unpublished data), and reduce variability of sunlight across experimental units. 
R.S. King et al. 2009 86 

Figure 32. Aerial view of the Lake Waco Wetland and the location of the Baylor Experimental Aquatic 

Research (BEAR) stream facility (red arrow, upper panel). The lower panel is an aerial view the BEAR 

experimental stream facility (80’ x 100’, covered in 60% shade cloth), adjacent pond mesocosms, and a 

40K gallon water tower used to store water pumped from the Lake Waco Wetlands en route to the 

experimental streams. See http://www.baylor.edu/aquaticlab/index.php?id=45868 for a video tour of the 

BEAR streams. 
R.S. King et al. 2009 87 

EXPERIMENTAL DESIGN 

Clean river cobble and gravel were placed in streams in January 2008 to simulate erosional 

habitats found in natural streams in our field study. Stream flow was initiated on 31 January 

2008 and calibrated to 182 L/min (+/- 2 L/min) for each stream. 

Streams were seeded with organic matter, periphyton, and macroinvertebrates collected from 

ROCK-01 and NBOS-03, two of the intensive field sites. These sites were chosen because their 

P concentrations were representative of the background (control) and high P treatments to be 

employed in the experiment. Inoculating the streams with organisms only from the low-P 

streams would bias the study because of the reduced probability of introducing taxa that 

proliferate under high P conditions. 

Streams were seeded twice: 1 February and 15 February 2008. The first event was to primarily 

introduce algae, bacteria, fungi, and detritus, as we anticipated high mortality of 

macroinvertebrates due to the lack of food in the streams at the beginning of the colonization 

period. The second seeding was done to facilitate establishment of macroinvertebrates found in 

both low and high P streams in the region. 

Seeding was accomplished by collecting five 1-m2

 kick screen samples from ROCK-01 and 

NBOS-03 for each of the 12 experimental streams. Samples were taken from 5 different 

locations in the field study sites, with one sample collected for each of the 12 streams at each of 

the 5 different locations. A composite of the five samples was placed in one 20-L bucket per 

experimental stream. Each bucket was aerated during transport from the field back to the BEAR 

facility. One bucket per stream was mixed and gently dumped into the top of the riffle section to 

permit drifting organisms to colonize downstream areas in the reach. 

The streams were allowed to run without any nutrient additions from 31 January to 10 March 

2008 in an effort to allow growth of periphyton at low nutrient concentrations. However, we 

were also interested the potential effect of nutrients on established periphyton communities from 

real streams. To this end, we transplanted 600 cobbles with established periphyton communities 

from riffle habitat in ROCK-01 and NBOS-03. Cobbles from ROCK-01 and NBOS-03 were 

placed in random cross sections throughout the reach of each experimental stream on 7 and 8 

March 2008, respectively, a few days prior to initiation of the experiment. 

Experimental dosing of phosphorus began on 11 March 2008. Background concentration of 

PO4-P during the colonization phase of the study was approximately 6 ug/L, slightly higher than 

our lowest field sites, but nevertheless relatively low. Four streams were randomly assigned no 

additional phosphorus, and thus a target concentration of 6 ug/L (Control). Because our field 

study had identified consistent nonlinear changes in numerous biological response variables 

between 10 and 20 ug/L PO4-P (usually corresponding to 15-25 ug/L TP), we selected a target 

PO4-P concentration of 20 ug/L for our lower nominal P treatment. Four streams were assigned 

a treatment of +15 ug/L PO4-P to achieve a target PO4-P concentration of 20 ug/L (Low P 

treatment). The remaining four streams received +95 ug/L PO4-P to achieve a target 

concentration of 100 ug/L (High P treatment). The higher P concentration was selected 
R.S. King et al. 2009 88 

because it was clearly above the level of P enrichment that was correlated with significant 

biological changes in the field study. 

Streams were dosed with NaH2PO4 using peristaltic pumps. Pumps were calibrated to deliver 

phosphorus stock solutions from 50-L carboys to mixing tanks located at the head of each 

experimental stream. 

Figure 33. Collage of BEAR stream photographs illustrating the layout of the stream facility 

(top left), a view from within a glide section of a stream channel (top right), the substrate of a 

typical riffle section during the colonization phase of the study (bottom left), and a lateral view 

of the streams from the far left side of the facility, looking toward the adjacent Lake Waco 

Wetlands. 
R.S. King et al. 2009 89 

SAMPLING AND DATA ANALYSES 

Dissolved nutrients (PO4-P, NH3-N, and NO2NO3-N) were sampled in triplicate biweekly from 

the mixing tank and pool (terminal outlet) of the streams. Because of the relatively short length 

of the streams and insufficient amount of time for phytoplankton growth before water is emptied 

from the streams, turbidity and chlorophyll-a in surface water were not monitored. Because of 

the effect of turbulent mixing on dissolved oxygen in streams and lack of response of DO to TP 

in field streams during periods of high flow, diel dissolved oxygen was not monitored in this 

study. 

Biotic sampling was stratified into riffle, and glide habitats. Each section was partitioned into 

approximately forty 20-cm wide cross-sections. Each cross section was assigned a number for 

random sampling location for biota sampling. 

Quantitative periphyton samples from rocks that were exclusively colonized in the experimental 

streams (“bare rock”, non-transplant periphyton) were collected on Day 0 (day before dosing 

commenced) and Day 28 (4 weeks of dosing) of the study. On each date, four random crosssections from the riffle sections were sampled. Within each of the four cross sections, 5 cobbles 

were removed quantitatively sampled in accordance with the field study protocol. The 

composite of 20 rocks was subsampled for chlorophyll-a, AFDM, and CNP chemical analysis 

following the QAPP (Appendix B). On Day 28, filamentous algae (Cladophora) was gently 

removed from the rocks (most of it was loosely attached) and quantified separately for AFDM, 

chlorophyll-a and CNP chemical analysis. The remaining attached periphyton was processed as 

previously described. 

Quantitative periphyton samples from transplanted cobbles (ROCK-01 and NBOS-03) were 

processed on Day -2 and -1 (two and one days prior to dosing, the same day rocks were collected 

from the field streams) to characterize the baseline AFDM, chlorphyll a, and CNP content prior 

to transplanting. Four separate composite samples were collected from each site (four sets of 5 

rocks). Five transplanted cobbles per field stream were collected from each experimental stream 

on Day 7 (17 March 2008) and 28 (7 April 2008). Each set of five rocks represented a single 

composite sample (following Day -1 and -2) from each of the 2 field streams. AFDM, 

chlorophyll-a, and CNP content of the periphyton was quantified for each composite sample. 

Additionally, algal species composition was measured from samples from Day -2/-1 (hereafter, 

Day 0) and Day 28. 

An additional set of quantitative periphyton samples was collected from 5 x 5 cm ceramic tiles. 

Clean tiles were placed in the glide section of streams on Day -14. A set of 10 tiles was 

randomly removed from each stream on Day 0, 14, and 28 for analysis of chlorophyll-a and 

AFDM. 

Quantitative macroinvertebrate sampling was conducted on Day 0 and 28 and was coordinated 

with periphyton sampling so that the same cross-sections were sampled on the same dates. 

Samples were collected with a custom-made quantitative sampler developed specifically for the 

BEAR facility. The sampler, called a BEAR Trap, is functionally similar in design and function 

to a Hess sampler, but has dimensions that fit within the 20-cm cross-sections of the BEAR 
R.S. King et al. 2009 90 

streams (52 x 20 cm; area=0.104 m2

). Four quantitative samples were collected per stream per 

riffle and run sections per date and were processed as described in the QAPP (Appendix B). 

Depending upon the response variable, most data were analyzed as a one-way ANOVA with P 

treatment as a 3-level main effect (Control, Low P, High P). Individual streams were replicates 

(n=4 per treatment). Appropriate transformations were applied when necessary. Multiple 

comparison tests were done when ANOVA deemed the main effect significant (p<0.05). 

Algal and macroinvertebrate species composition were analyzed using nonmetric 

multidimensional scaling ordination (see Section I) and analysis of successional vectors. Tests 

for diffrerences in treatments were done using Multi-Response Permutation Procedure (MRPP). 

Indicator Species Analysis was conducted to identify taxa that changed significantly in response 

to P treatments if the overall MRPP test of community differences was deemed significant. 

Multivariate analyses were conducted in PC-ORD v.5.20. 
R.S. King et al. 2009 91 

RESULTS AND INTERPRETATION 

Nutrient Concentrations among the Experimental Streams 

Experimental PO4-P additions yielded concentrations among treatments that were very close to 

target nominal concentrations of 6, 20, and 100 ug/L PO4-P (Figure 34, Table 16). The 4-week 

PO4-P average from the control streams ranged between 7.7 and 8.1 ug/L PO4-P, slightly higher 

than anticipated due to slight increases in nutrients in the wetland during the study period. The 

average PO4-P for the Low P treatment ranged from 17.7 to 19.3 among streams, slightly lower 

than the target of 20 ug/L. High P treatments ranged from 102 to 112 ug/L PO4-P, slightly 

higher than the 100 ug/L target concentration. 

DIN and total N were very similar among all 12 study streams (Table 16). DIN and TN were 

within the range of the least impacted field study streams. TP was higher than expected, largely 

due to wind re-suspension of fine sediment in the wetland. Mean TP ranged from 19.9-20.5 

among the Control streams during the 4 week dosing period. This TP level was close to the 

threshold observed from the field study. 

Figure 34. Time series multi-panel plot showing concentrations of PO4-P among the 12 experimental 

streams during the colonization (pre-dosing; 1 February – 10 March 2008) and dosing (11 March – 7 

April 2008) phases of the experiment. The magenta points correspond to the samples collected from the 

outlet of each stream, whereas the blue points are samples from the mixing tank. No mixing tank samples 

were collected prior to dosing. Control = no P addition (6 ug/L), Low P = 20 ug/L and high P = 100 ug/L. 

REP = stream 1 -4 per treatment. 
R.S. King et al. 2009 92 

Table 16. Mean dissolved and total P and N concentrations during the 28-days of PO4-P dosing in the 

experimental streams. Dissolved nutrients (PO4-P, DIN) were sampled in triplicate per location per 

stream, twice weekly during the study period (11 March – 7 April 2008; n=9 sampling events). Total N 

and P were sampled once per week during the dosing period (n=4 dates). See figure 34 for temporal 

patterns in PO4-P prior to and during dosing. 

Treatment 

Stream 

rep. Location PO4-P DIN NH3-N 

NO2NO3-

N TN TP 

Control 1 Mixing tank 8.1 264.9 15.7 249.1 419.3 19.9

Control 2 Mixing tank 7.9 272.1 15.6 256.6 468.5 20.2

Control 3 Mixing tank 7.8 288.1 15.7 272.4 472.5 20.5

Control 4 Mixing tank 7.7 267.2 15.0 252.3 501.3 20.5

Low 1 Mixing tank 18.3 258.5 15.4 243.1 452.0 37.8

Low 2 Mixing tank 19.3 273.2 14.9 258.2 449.3 39.9

Low 3 Mixing tank 19.3 273.2 14.7 258.6 389.8 40.3

Low 4 Mixing tank 17.7 250.9 14.6 236.4 432.5 37.8

High 1 Mixing tank 109.8 267.2 15.6 251.6 469.5 136.2

High 2 Mixing tank 112.7 273.3 16.2 257.2 464.3 137.2

High 3 Mixing tank 102.7 265.4 14.7 250.8 447.0 127.4

High 4 Mixing tank 109.5 277.6 14.2 263.4 421.9 136.0

Control 1 Outlet 8.5 259.2 18.0 241.2 463.1 19.4

Control 2 Outlet 8.8 259.7 16.2 243.5 459.3 19.1

Control 3 Outlet 9.1 272.3 16.6 255.6 521.0 19.5

Control 4 Outlet 8.9 251.5 14.6 236.9 488.3 19.2

Low 1 Outlet 17.8 226.8 15.5 211.3 411.0 37.7

Low 2 Outlet 19.5 262.8 15.0 247.8 396.5 39.3

Low 3 Outlet 18.4 245.4 15.5 229.9 426.5 40.3

Low 4 Outlet 17.4 224.2 15.5 208.7 457.3 38.5

High 1 Outlet 96.6 216.6 16.1 200.5 431.4 128.7

High 2 Outlet 98.7 214.9 15.6 199.3 441.8 134.4

High 3 Outlet 102.8 250.9 15.7 235.2 466.8 127.2

High 4 Outlet 103.1 225.9 18.0 207.9 420.8 134.5
R.S. King et al. 2009 93 

RESULTS AND INTERPRETATION, CONTINUED 

Periphyton and Filamentous Algal Biomass Response to P Dosing 

Periphyton growing on the ceramic tiles responded rapidly to experimental P dosing (Figure 35). 

There were no differences in treatments on Day 0 (Figure 36). High P treatments had 

significantly more chlorophyll than Low-P and Controls on Day 14, although Low-P 

chlorophyll-a was trending higher than the Control. By Day 28, Low P and High P treatments 

were not different, but had significantly more chlorophyll-a than Controls (Figure 35, 36). The 

lack of difference between low and high P treatments is consistent with the threshold response 

observed in the field study. 

Figure 35. Comparison of periphyton growing on ceramic tiles among the 3 P treatments on Day 14 (left) 

and Day 28 (right). Note the obvious difference in color between Conrol and Low/High P treatments, 

even on Day 14. By Day 28, Low/High P tiles were covered in green algae. 

 
R.S. King et al. 2009 94 

Figure 36. Results of ANOVA comparing mean chlorophyll-a (ug/cm2) among P treatments on Day 0, 

14, and 28. Means significantly differed on Day 14 and 28, but not Day 0. Means with the same letter 

are not different (p>0.05). 

Phosphorus treatment

Control Low High

Chlorophyll-a (μg cm-2) 0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

Phosphorus treatment

Control Low P High P

Chlorophyll-a (μg cm-2)

0

2

4

6

8

10

12

14

Day 0

Day 14

Day 28

A

A

B

A

B

B

Phosphorus treatment

Control Low High

Chlorophyll-a (μg cm-2) 0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

1.2

Periphyton biomass

Ceramic tiles(glide habitat)
R.S. King et al. 2009 95 

Periphyton AFDM and chlorophyll-a from the bare rocks and transplanted rocks was highly 

variable among P treatments and did not differ significantly. However, this was largely due to 

the very strong response of Cladophora (filamentous green algae) in the low and high P 

treatments. Cladophora biomass exploded near the end of the study, and was significantly 

higher in the Low and High P streams than the Controls on the bare rocks and transplant rocks 

(ROCK-01, NBOS-03) on Day 28 (Figures 37-40). 

Figure 37. Photographs of riffle sections of Control, Low P, and High P treated experimental streams on 

Day 14 (left) and Day 28 (right). On Day 14, patches of Cladophora had begun to appear in Low and 

High P streams, but was more visually abundant in the High P streams. By Day 28, Low and High P 

streams were virtually indistinguishable, both supporting high biomass of Cladophora. Controls 

maintained a golden-brown coating of diatoms on rocks thorughout the study, although some Cladophora

had begun to appear by Day 28. 
R.S. King et al. 2009 96 

Figure 38. A longitudinal view of Control, Low-P, and High-P streams on Day 28. 

Figure 39. Results of ANOVA comparing mean Cladophora chlorophyll-a (ug/cm2) among P treatments 

on Day 28. Means with the same letter are not different (p>0.05). 

 

Phosphorus treatment

Control P Low P High P

Log chlorophyll-a (μg cm-2) 0.0

0.5

1.0

1.5

2.0

2.5

3.0

Cladophora (filamentous green algae) biomass

Bare rocks(non‐transplant)

A

B B Day 28
R.S. King et al. 2009 97 

Figure 40. Results of ANOVA comparing mean Cladophora chlorophyll-a (ug/cm2) on 

transplanted rocks (ROCK-01=very low nutrient site, NBOS-03=high nutrient site) among P 

treatments on Day 28. Means with the same letter are not different (p>0.05). 

Phosphorus treatment

Control P Low P High P

Log Chlorophyll-a (μg cm-2) 0.0

0.5

1.0

1.5

2.0

2.5

Phosphorus treatment

Control P Low P High P

Log Chlorophyll-a (μg cm-2) 0.0

0.5

1.0

1.5

2.0

2.5

NBOS‐03 (High P) Transplants ROCK‐01 (Control P) Transplants

Cladophora (filamentous green algae) biomass

A

B

B

A

B

B Day 28 Day 28
R.S. King et al. 2009 98 

RESULTS AND INTERPRETATION, CONTINUED 

Periphyton Nutrient Content Response to Experimental P Dosing 

Periphyton C:P, C:N, and N:P ratios from non-transplant bare rock samples did not differ among 

streams on Day 0, but differed significantly by treatment on Day 28 (Figure 41). High-P treated 

streams had the lowest C:P ratio (~150), Control streams had the highest (~320), whereas Low-P 

streams were intermediate (~230). Even the control C:P ratios were approaching levels deemed 

to be near or below a C:P threshold in the field study, suggesting that the background PO4-P 

concentrations were high enough to allow periphyton to sequester much higher amounts of P 

than typical of the lowest nutrient field sites. 

Periphyton C:P ratios from the ROCK-01 transplant rocks responded very strongly to all 3 

experimental treatments (Figure 42). On Day 0, ROCK-01 periphyton had C:P ratios above 

2,000. After transplanting these rocks into the BEAR streams for just 7 days, mean C:P ratios 

had dropped to 689, 346, and 215 among the Control, Low P and High P treatments, 

respectively. By Day 28, ROCK-01-Control C:P ratios had dropped to 250, whereas Low and 

High P values were near 150 (Figure 43). This illustrated the remarkable affinity of stream 

periphyton for phosphorus, and provided additional evidence in support of nonlinear uptake of P 

as the explanation for sharp declines in periphyton C:P with small increases in surface-water TP. 

In contrast to the ROCK-01 transplants, the NBOS-03 transplants did not respond to any of the 

experimental P treatments on Day 7 or 28 (Figure 42, 43), supporting the hypothesis that 

recycling of P within the periphyton is an important mechanism for maintaining low C:P ratios 

over time in streams with highly variable surface-water TP. Moreover, it also showed that 

periphyton from NBOS-03 was already saturated with P and thus did not sequester more P per 

unit carbon in the low or high P treatments. 
R.S. King et al. 2009 99 

Figure 41. Results of ANOVA comparing mean periphyton C:P ratio (upper panel) and 

Cladophora C:P ratio (lower panel) among P treatments. Means with the same letter are not 

different (P>0.05). 

0

50

100

150

200

250

300

350

400

Control Low P High P

Periphyton C:P ratio (bulk)

A

B

C

ANOVA, F=25.6, p=0.0001

0

50

100

150

200

250

300

350

Control Low P High P

Cladophora C:P ratio

A

B

B

ANOVA, F=28.4, p=0.0001

Bare rock (Non‐transplant)

Periphyton C:P ratio, Day 28

Cladophora C:P ratio, Day 28
R.S. King et al. 2009 100 

Figure 42. Results from ANOVA comparing periphyton C:P ratios from transplant rocks from 

ROCK-01 (top panel) and NBOS-03 (lower panel) after 7 days of exposure in the experimental 

streams. The ROCK-01 (top panel) and NBOS-03 (bottom panel) column corresponds to the 

mean from 4 composite samples collected on Day 0 of the study (prior to transplanting into the 

streams). Means with the same letter are not significantly different. 

0

500

1000

1500

2000

2500

3000

ROCK‐01 Control Low P High P

Periphyton C:P ratio (bulk)

A

B

C

C

ANOVA, F=36.0, p<0.0001

Periphyton C:P ratio, Day 7

ROCK‐01 Transplant

0

50

100

150

200

250

300

350

NBOS‐03 Control Low P High P

Periphyton C:P ratio (bulk)

NBOS‐03 Transplant

ANOVA, F=2.88, p=0.07
R.S. King et al. 2009 101 

Figure 43. Results from ANOVA comparing periphyton C:P ratios from transplant rocks from 

ROCK-01 (top panel) and NBOS-03 (lower panel) after 28 days of exposure in the experimental 

streams. Means with the same letter are not significantly different. 

0

50

100

150

200

250

300

Control Low P High P

Periphyton C:P ratio (bulk)

A

B B

ANOVA, F=6.88 p=0.015

Periphyton C:P ratio, Day 28

ROCK‐01 Transplant

0

50

100

150

200

250

Control Low P High P

Periphyton C:P ratio (bulk)

NBOS‐03 Transplant

ANOVA, F=1.44, p=0.28
R.S. King et al. 2009 102 

RESULTS AND INTERPRETATION, CONTINUED 

Algae Species Responses to Experimental P Dosing 

Nonmetric multidimensional scaling ordination of algal species composition from the 6 intensive 

field sites in February 2008 and June 2008, and the ROCK-01 and NBOS-03 transplant rocks on 

Day 0 and Day 28 revealed several interesting responses (Figure 44). ROCK-01 samples from 

Day 0 were significantly different than the Control, Low P or High P samples on Day 28 (MRPP, 

p<0.0001). Algal species composition at ROCK-01 in February 2008 (prior to the BEAR study) 

and in June 2008 (after the BEAR study) remained clearly separated from any of the ROCK-01 

samples that were transplanted in the BEAR streams. 

ROCK-01 Control species composition shifted significantly to the right along nMDS Axis 1 by 

Day 28, moving in the direction of the NBOS-03 samples and toward field sites with species 

indicative of moderate to high TP (PALU-01, LEON-02). However, ROCK-01 Low and High P 

samples shifted significantly farther along this axis (MRPP, P<0.0032), indicating changes in 

species composition in Low and High P treatments were significantly greater than Controls. 

Consistent with a threshold response, algal species composition in Low and High P treatments 

did not differ (Figure 44). 

In contrast, NBOS-03 rocks shifted in the same direction as the June 2008 NBOS-03 sample, 

remaining on the high-P end of Axis 1, regardless of P treatment. Algal species composition did 

not differ among treatments after 28 days of exposure (Figure 44). 

Indicator Species Analysis of ROCK-01 transplants from Day 28 revealed that at least 5 diatom 

species were significantly less abundant (or absent) from Low P and High P samples than 

Controls, and 2 diatoms were significant indicators of the Low/High P treatments (Table 17). 

Five of these 7 taxa were identified as significant threshold indicators in response to TP gradients 

in the field study. All five responded in the same direction (decline, increase) in the BEAR study 

as in the field study. 
R.S. King et al. 2009 103 

Figure 44. Nonmetric multidimensional scaling ordination of algal species composition from ROCK-01 and NBrocks on Day 0 and Day 28. Algal species composition from the 6 intensive field sites during February (before) are also ordinated with the BEAR samples for a frame of reference. Sites sorted from low P sites (left end of Ax(right end of Axis 1). All ROCK-01 samples shifted toward NBOS-03 samples after 28 days of exposure in the B

Low and High P treated rocks shifted farther away from the natural stream and were significantly different than C

NBOS-03 rocks shifted in the same direction as the June 2008 NBOS-03 sample, and algal species composition d

treatments after 28 days of exposure. 
R.S. King et al. 2009 104 

Table 17. Results of Indicator Species Analysis (IndVal) using Control vs. Low/High P

treatments as a predictor of algal species composition growing on ROCK-01 transplanted 

cobbles at Day 28 of the BEAR experimental stream study. Only taxa with significant IndVal 

scores are shown below. Taxa that showed significant declines or increases in response to P in 

both the experimental dosing study and in the field are highlighted in bold.

Taxon ID Indicator group 

Response to 

experimental 

PO4-P 

enrichment 

Response to field 

study TP 

gradients IndVal p 

ACbiasso Control Decline Decline 51.8 0.0408 

AHminuti Control Decline Decline 77.6 0.0034 

CMaffins Control Decline 88.2 0.0022 

EYevergl Control Decline Decline 100 0.0022 

MDcircul Control Decline 100 0.0022 

CCpedcls Low/High P Increase Increase 58.9 0.0022 

REsinuta Low/High P Increase Increase 87.5 0.0102 
R.S. King et al. 2009 105 

RESULTS AND INTERPRETATION, CONTINUED 

Macroinvertebrate Community Response to Experimental P Dosing 

Macroinvertebrate taxonomic composition did not differ among treatments on Day 0 or Day 28, 

although composition shifted significantly between dates (Figure 45). Forty-five taxa were 

identified from the experimental streams, including 17 genera from the orders Ephemeroptera, 

Plecoptera, and Trichoptera (Appendix A). The relatively short dosing period, in contrast to the 

relatively long life cycle of these taxa, necessarily limited the potential response of this diverse 

group of organisms to P treatments. Future experiments spanning a longer dosing period or 

focused on the effect of low flow and P interactions may produce more meaningful results in the 

context of animal responses to experimental P enrichment. 

Figure 45. Nonmetric multidimensional scaling ordination of macroinvertebrate community 

responses to experimental P additions at the BEAR facility, Day 0 and Day 28. Arrows indicate 

the direction and magnitude of community change within each stream between Day 0 and 28. 

Ellipses are drawn to envelop all 4 streams by treatment on Day 28. There was extensive overlap 

among treatments, and community composition did not differ (MRPP, p=0.13). 

Ordination ofmacroinvertebrate community composition

No difference in P treatments over time
R.S. King et al. 2009 106 

SECTION III. 

CONCLUSIONS AND RECOMMENDATIONS 

Shifts from periphyton communities comprised of sensitive diatoms, calcareous cyanobacteria, 

and other non-chlorophyll bearing microbes to communities with higher chlorophyll content and 

more filamentous green algae is highly likely at concentrations of surface-water TP above 20 

ug/L. Streams with TP > 200-1000 ug/L likely represent a second tier of degradation, and appear 

at greater risk for nuisance algal growth based on our threshold analyses. However, results from 

the P dosing experiment suggest that concentrations as low as 20 ug/L PO4-P can lead to high 

levels of Cladophora biomass in as little as 28 days. Adding more PO4-P (100 ug/L) did not 

result in more Cladophora in a 4-week period. However, faster growth rates were observed in 

the 100 ug/L treatment, which may explain why field streams with very high levels of TP (>200 

ug/L) had greater frequency of heavy growth of filamentous algae. 

Aquatic macrophyte cover consistently declined in streams with TP > 25-50 ug/L. These 

submersed plants serve as important refugia for juvenile fishes and macroinvertebrates, and 

provide a source of dissolved oxygen during low flows. Their decline likely represents a key 

structural and functional change in these stream ecosystems. 

Minimum dissolved oxygen levels are highly dependent upon an interaction between flow and 

nutrient enrichment. Our study suggests that TP levels > 20-30 ug/L, coupled with low flows, 

will cause detrimental declines in minimum dissolved oxygen levels. This is particularly 

important in the context of minimum flows, a contentious issue in the southwestern USA. It is 

unlikely that studies geared toward detecting effects of nutrients on DO will adequately 

characterize this relationship without sampling during periods of low flow when gas exchange is 

with the atmosphere is very slow. Distance downstream and flow status are two very important 

considerations when evaluating the influence of WWTP discharges on wadeable streams in semiarid regions. Future research is needed to better quantify the interaction between minimum 

flows and biological integrity in streams. 

Based on the weight of evidence from the coupling of the field stream study and experimental 

stream study suggests that streams of the study area are very sensitive to phosphorus enrichment. 

There is a very high probability that streams exposed to surface-water TP levels exceeding 20 

ug/L, and possibly 15 ug/L, will experience a sharp decline in biological integrity, including loss 

of characteristic structure (periphyton and macrophytes), loss of numerous species (algae and 

macroinvertebrates), minimum dissolved oxygen levels unsuitable for supporting native fauna 

during low flows, and increase likelihood of nuisance algal growth that limits recreational use of 

strreams. Streams exceeding 200 ug/L may represent a second tier of degradation, with more 

consistent nuisance algal growth and additional losses of algal and macroinvertebrate species. 

 
R.S. King et al. 2009 107 

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Management Reference Guide. August 24, 2004. TCEQ. Austin, TX. URL: 

http://www.tnrcc.state.tx.us/ water/quality/data/wqm/wdma/dmrg/2004dmrg.html 

Texas Commission on Environmental Quality. Surface Water Quality Monitoring 

Procedures,Volume 1: Physical and Chemical Monitoring Methods for Water, Sediment, 

and Tissue. 

Toms, J. and M. L. Lesperance. 2003. Piecewise regression: a tool for identifying ecological 

thresholds. Ecology 84:2034-2041. 
R.S. King et al. 2009 110 

Appendix A. List of algal taxa identified in the field and experimental dosing studies. Only taxa 

that occurred >2 times are included in the table (n=295). Taxonomic identifications were 

conducted exclusively by Dr. Barbara Winsborough, Winsborough Consulting, Leander, TX. 

TYPE SPECIES TAXON_ID 

Diatom Achnanthes biassolettiana ACbiasso 

Diatom Achnanthes brevipes ACbrevip 

Diatom Achnanthes hungarica AChungar 

Diatom Adlafia bryophila ADbryoph 

Diatom Achnanthidium exiguum AHexigum 

Diatom Achnanthidium minutissimum AHminuti 

Diatom Amphipleura pellucida ALpelluc 

Diatom Amphora bullatoides AMbullat 

Diatom Amphora coffeaeformis AMcoffea 

Diatom Amphora granulata AMgranul 

Diatom Amphora inariensis AMinarie 

Diatom Amphora libyca AMlibyca 

Diatom Amphora montana AMmontan 

Diatom Amphora ovalis AMovalis 

Diatom Amphora pediculus AMpedcls 

Diatom Amphora veneta AMveneta 

Diatom Anomoeoneis sphaerophora ANsphaer 

Diatom Aulacoseira ambigua AUambig 

Diatom Aulacoseira granulata AUgranlt 

Diatom Aulacoseira granulata var. angustissima AUgrnang 

Diatom Bacillaria paradoxa BApardxa 

Diatom Brachyseira vitrea BRvitrea 

Diatom Caloneis bacillum CAbacill 

Diatom Campylodiscus clypeus CAclypeu 

Diatom Caloneis molaris CAmolars 

Diatom Caloneis schumanniana CAschuma 

Diatom Caloneis silicula CAsilicu 

Diatom Cocconeis pediculus CCpedcls 

Diatom Cocconeis placentula CCplacen 

Diatom Cymbella cistula CMcistul 

Diatom Cymbella cymbiformis CMcymbis 

Diatom Encyonema delicatula CMdelcat 

Diatom Encyonema elginensis CMelgine 

Diatom Cymbella excisa CMexcisa 

Diatom Cymbella falaisensis CMfalais 

Diatom Cymbella hustedtii CMhusted 

Diatom Cymbella kolbei CMkolbei 

Diatom Cymbella laevis CMlaevis 

Diatom Cymbella tumida CMtumida 

Diatom Cyclostephanos tholiformis CStholif 
R.S. King et al. 2009 111 

Diatom Cymatopleura elliptica CTellipt 

Diatom Cymatopleura solea CTsolea 

Diatom Cyclotella cf. stelligera CYcfstel 

Diatom Cyclotella meneghiniana CYmenegh 

Diatom Denticula kuetzingii DEkuetzi 

Diatom Denticula subtilis DEsubtil 

Diatom Diadesmis (Navicula) confervacea DIconfer 

Diatom Diploneis ovalis DPovalis 

Diatom Diploneis pseudovalis DPpsudov 

Diatom Diploneis puella DPpuella 

Diatom Encyonema carina ECcarina 

Diatom Encyonema minutum ECminutu 

Diatom Encyonema silesiacum ECsilesi 

Diatom Encyonema triangulum ECtriang 

Diatom Epithemia adnata EPadnata 

Diatom Epithemia sorex EPsorex 

Diatom Epithemia turgida EPturgid 

Diatom Eucocconeis (Achnanthes) flexella ESflexel 

Diatom Eunotia pectinalis EUpectin 

Diatom Eunotia arcus EUarcus 

Diatom Encyonopsis cesatii EYcesati 

Diatom Eunotia bilunaris EUbilun 

Diatom Encyonema (Encyonopsis) evergladianum EYevergl 

Diatom Encyonema (Encyonopsis) microcephala EYmicroc 

Diatom Navicula (Fallatia) lenzii FAlenzii 

Diatom Fallacia litoricola FAlitori 

Diatom Fallacia monoculata FAmonoc 

Diatom Fallacia pygmaea FApygmae 

Diatom Fallatia tenera FAtener2 

Diatom Fragilaria capucina FRcapuci 

Diatom Fragilaria elliptica FRellptc 

Diatom Fragilaria famelica FRfameli 

Diatom Fragilaria nanana FRnanana 

Diatom Fragilaria pinnata var. lancettula FRpinlan 

Diatom Fragilaria tenera FRtenera 

Diatom Geissleria decussis GEdecu 

Diatom Gomphosphenia (Gomphonema) lingulatiformis GMlinfor 

Diatom Gomphosphenia reicheltii GMreicht 

Diatom Gomphonema acuminatum GOacumin 

Diatom Gomphonema affine GOaffine 

Diatom Gomphonema angustatum (micropus) GOangstt 

Diatom Gomphonema angustum GOangust 

Diatom Gomphonema clavatum GOclavat 

Diatom Gomphonema gracile GOgracil 

Diatom Gomphonema insigne GOinsign 
R.S. King et al. 2009 112 

Diatom Gomphonema intricatum GOintric 

Diatom Gomphonema intricatum var vibrio GOintvib 

Diatom Gomphonema mclaughlinii GOmaclau 

Diatom Gomphonema parvulum GOparvul 

Diatom Gomphonema pumilum GOpumilu 

Diatom Gomphonema rhombicum GOrhombi 

Diatom Gomphonema truncatum GOtrunca 

Diatom Gomphonema vibrioides GOvibdes 

Diatom Gyrosigma nodiferum GYnodfrm 

Diatom Gyrosigma obscurum GYobscur 

Diatom Gyrosigma spencerii GYspence 

Diatom Hantzschia amphioxys HAamphio 

Diatom Hippodonta (Navicula) hungarica HIhunga 

Diatom Craticula cuspidata KCcuspid 

Diatom Craticula (Navicula) halophila KChaloph 

Diatom Achnanthes (Lemnicola) hungarica LEhungar 

Diatom Luticola goeppertiana LUgoept2 

Diatom Luticola mutica LUmutica 

Diatom Meridion circulare MDcircul 

Diatom Melosira varians MEvarian 

Diatom Mastogloia elliptica MSellipt 

Diatom Mastogloia smithii MSsmithi 

Diatom Navicula (Mayamaea) atomus MYatomus 

Diatom Navicula antonii NAantoni 

Diatom Navicula cf. pseudanglica NAcfpsan 

Diatom Navicula circumtexta NAcirctx 

Diatom Navicula constans NAconstn 

Diatom Navicula cryptocephala NAcrypto 

Diatom Navicula cryptotenella NAcryten 

Diatom Navicula erifuga NAerifga 

Diatom Brachyseira neoexilis (Navicula exilis) NAexilis 

Diatom Navicula gregaria NAgregar 

Diatom Navicula ingenua NAingua 

Diatom Navicula integra NAintgra 

Diatom Navicula kotschii (texana) NAkotsch 

Diatom Navicula leptostriata NAleptos 

Diatom Navicula libonensis NAlibone 

Diatom Navicula menisculus NAmenscl 

Diatom Navicula (Eolima) minima NAminima 

Diatom Navicula oblonga NAoblong 

Diatom Navicula perminuta NApermnt 

Diatom Navicula phyllepta NAphylpt 

Diatom Navicula radiosa NAradios 

Diatom Navicula recens NArecens 

Diatom Navicula rhynchocephala NArhynch 
R.S. King et al. 2009 113 

Diatom Navicula sanctaecrucis NAsancru 

Diatom Navicula savannahiana NAsavana 

Diatom Navicula schroeteri var escambia NAschesc 

Diatom Sellaphora (Navicula) stroemii NAstroem 

Diatom Fallatia (Navicula) subminuscula NAsubmin 

Diatom Navicula symmetrica NAsymtrc 

Diatom Navicula texana (Grimmei) NAtexana 

Diatom Navicula tridentula NAtriden 

Diatom Navicula trivialis NAtrivis 

Diatom Navicula veneta NAveneta 

Diatom Navicula viridula var. rostellata NAvirdla 

Diatom Neidium iridis NEiridis 

Diatom Nitzschia acuminata NIacuata 

Diatom Nitzschia amphibia NIamphib 

Diatom Nitzschia amphibioides NIampoid 

Diatom Nitzschia angustatula NIangtu 

Diatom Nitzschia angustata NIangust 

Diatom Nitzschia brevissima NIbrevis 

Diatom Nitzschia calida NIcalida 

Diatom Nitzschia compressa var. balatonis NIcombal 

Diatom Nitzschia constricta NIcstric 

Diatom Nitzschia debilis NIdibili 

Diatom Nitzschia dissipata NIdissip 

Diatom Nitzschia filiformis NIfilifr 

Diatom Nitzschia fonticola NIfontic 

Diatom Nitzschia frustulum NIfrustu 

Diatom Nitzschia inconspicua NIincons 

Diatom Nitzschia linearis NIlinear 

Diatom Nitzschia obtusa NIobtusa 

Diatom Nitzschia palea NIpalea 

Diatom Nitzschia serpentiraphe NIserpen 

Diatom Nitzschia sigmoidea NIsigdea 

Diatom Nitzschia sigma NIsigma 

Diatom Nitzschia sinuata v delognii NIsinde 

Diatom Nitzschia solita NIsolita 

Diatom Nitzschia subacicularis NIsubaci 

Diatom Nitzschia tropica NItropic 

Diatom Nitzschia tryblionella NItrybla 

Diatom Nitzschia vitrea NIvitrea 

Diatom Pseudostaurosira brevistriata PDbrevis 

Diatom Pseudostaurosira parasitica var. subconstricta PDparsub 

Diatom Plagiotropis lepidoptera PGlepidp 

Diatom Pinnularia acrosphaeria PIacro 

Diatom Pinnularia gibba PIgibba 

Diatom Pinnularia microstauron PImicros 
R.S. King et al. 2009 114 

Diatom Pinnularia streptoraphe PIstrept 

Diatom Pleurosira (Ceratulina) laevis PRlaevis 

Diatom Achnanthes (Planothidium) lanceolata PTlanceo 

Diatom Reimeria sinuata REsinuta 

Diatom Rhoicosphenia abbreviata ROabbre 

Diatom Rhopalodia brebissonii RPbrebsn 

Diatom Rhopalodia gibba RPgibba 

Diatom Rhopalodia musculus RPmuscul 

Diatom Sellaphora laevissima SFlaevis 

Diatom Sellaphora pupula SFpupula 

Diatom Sellaphora seminulum SFseminu 

Diatom Fragilaria (Staurosira) construens SRconstr 

Diatom Fragilaria construens var. venter SRconven 

Diatom Stauroneis phoenicentron SSphoeni 

Diatom Stephanodiscus sp1 ST1 

Diatom Surirella angusta SUangust 

Diatom Surirella bifrons SUbifron 

Diatom Surirella brebissonii SUbreb 

Diatom Surirella elegans SUelegan 

Diatom Surirella minuta SUminuta 

Diatom Surirella ovalis SUovalis 

Diatom Surirella spiralis SUspiral 

Diatom Surirella splendida SUsplen 

Diatom Synedra (Fragilaria) acus SYacus 

Diatom Synedra goulardi SYgoular 

Diatom Synedra (Fragilaria) ulna SYulna 

Diatom Fragilaria (Tabularia) fasciculata TBfascic 

Diatom Terpsinoe musica TEmusica 

Diatom Thalassiosira weissflogii THweiss 

Diatom Tryblionella apiculata TYapicul 

Diatom Tryblionella (Nitzschia) calida TYcaldid 

Diatom Nitzschia (Tryblionella) levidensis TYlevid 

Soft Aphanothece sp. AFCsp 

Soft Aphanizomenon sp. AFNsp

Soft Aphanocapsa sp. AFPsp 

Soft Aphanochaete sp. AFTsp 

Soft Anabaena sp. ANBsp 

Soft Ankistrodesmus falcatus ANKfalca 

Soft Audouinella hermannii AUDhernn 

Soft Blennothrix sp. BLNsp 

Soft Bulbochaete sp. BULsp 

Soft Calothrix sp. CALsp 

Soft Chlorococcum sp. CHCOAUL 

Soft Chlamydomonas sp. CHLsp 

Soft Chlorophytan zoospores CHLzoo 
R.S. King et al. 2009 115 

Soft Chroococcus sp. CHOsp 

Soft Characium sp. CHRsp 

Soft Chaetophora sp. CHTsp 

Soft Cladophora glomerata CLAglomer 

Soft Chlorobotrys simplex CLBsimp 

Soft Closterium sp CLOsp2 

Soft Coelastrum sp. COEsp 

Soft Coelosphaerium sp. COHsp 

Soft Cosmarium botrytis COSbotry 

Soft Cosmarium galeritum COSgaler 

Soft Cosmarium sp. COSsp 

Soft Crucigenia sp. CRUsp 

Soft Cryptomonas sp. CRYsp 

Soft Dictyosphaerium sp. DICsp 

Soft Unidentified dinoflagellates DINuid 

Soft Euastrum binale EUAbinal 

Soft Euglena sp. EUGsp 

Soft Gloeocystis sp. GLCsp 

Soft Gloeothece sp. GLHsp 

Soft Gloeoskene turfosa GLKturf 

Soft Gloeocapsa sp. GLOsp 

Soft Gongrosira sp. GOGsp 

Soft Golenkinia sp. GOLsp 

Soft Gomphosphaeria sp. GOMsp 

Soft Homoeothrix sp. HOMsp 

Soft Hormidium sp. HORsp 

Soft Kirchneriella sp. KIRsp 

Soft Kirchneriella obesa KIRobesa 

Soft Merismopedia glauca MERglauc 

Soft Micractinium sp. MIAsp 

Soft Microcystis sp MICsp 

Soft Mougeotia sp. MOUsp 

Soft Oedogonium sp. OEDsp 

Soft Oocystis sp. OOCsp

Soft Ophiocytium capitatum OPHcapit 

Soft Oscillatoria sp. OSCsp 

Soft Pandorina morum PANmorum 

Soft Pediastrum boryanum PEDboryn 

Soft Pediastrum duplex PEDduplx 

Soft Pediastrum simplex PEDsimpl 

Soft Peridinium sp. PERsp 

Soft Phacus sp. PHAsp 

Soft Phormidium sp PHOsp 

Soft Raphidiopsis curvata RAPcurvt 

Soft Scenedesmus abundans SCEabund 
R.S. King et al. 2009 116 

Soft Scenedesmus acuminatus SCEacumn 

Soft Scenedesmus acutiformis SCEacutf 

Soft Scenedesmus armatus SCEarmat 

Soft Scenedesmus bijuga var. alternans SCEbialt 

Soft Scenedesmus bijuga SCEbijug 

Soft Scenedesmus dimorphus SCEdimor 

Soft Scenedesmus quadricauda SCEquadr 

Soft Scenedesmus sp. SCEsp 

Soft Schroderia setigera SCRsetig 

Soft Scytonema sp. SCYsp 

Soft Schizothrix sp. SCZsp 

Soft Sphaerocystis schroeteri SPHschro 

Soft Sphaerocystis sp. SPHsp 

Soft Spirogyra sp. SPIsp 

Soft Spirulina sp. SPLsp 

Soft Spondylosium pulchrum SPOpulch 

Soft Staurastrum sp. STAsp 

Soft Synechococcus sp. SYCsp 

Soft Tetraedron caudatum TETcaudt 

Soft Tetraedron minimum TETminum 

Soft Tetraedron trigonum TETtrign 

Soft Trachelomonas sp. TRAsp 

Soft Centric diatoms Uncent 

Soft Pennate diatoms Unpennt 

Soft Unknown Cyanophyte coccoid XBGcoc 

Soft Unknown Cyanophyte filament XBGfilam 

Soft Unidentified dinoflagellates XDFalga 

Soft Unknown alga sp. XXAsp 

Soft Zygnema sp. ZYGsp 
R.S. King et al. 2009 117 

Appendix A,continued List of dominant macroinvertebrate taxa identified in the BEAR experimental 

streams, Day 0 and 28, including several EPT (Ephemeroptera, Trichoptera, Plecoptera) taxa (bold). 

Argia 

Baetis 

Baetodes 

Berosus 

Brechmorhoga 

Caenis 

Camelobaetidius 

Cheumatopsyche 

Chimarra 

Chironomidae 

Chironomus 

Coptotmus 

Erpetogomphus 

Erpobdellidae 

Gammarus 

Gyrinus 

Haematopota 

Helicopsyche 

Hetaerina 

Hydracarina 

Hydrocanthus 

Hydroperla 

Hydropsyche 

Isonychia 

Lutrochus 

Macaffertium 

Micromenetus 

Naididae 

Neochoroterpes 

Oecetis 

Perlesta 

Petrophila 

Physella 

Simulium 

Sphaeriidae 

Stenelmis 

Stenonema 

Thraulodes 

Trichocorixa 

Tricorythodes 

Turbellaria 

Veliidae 
R.S. King et al. 2009 118 

APPENDIX B. PUBLICATIONS SUPPORTED IN PART OR FULL BY #CP-966137-01 

Back, J. A.*, J. M. Taylor*, R. S. King, E. Hintzen*, and K. Fallert*. 2008. Ontogenetic 

differences in mayfly stoichiometry influence growth rates in response to phosphorus. 

Fundamental and Applied Limnology (Archiv fur Hydrobiologie) 171:233-240. 

Scott, J. T.*, J. A. Back*, J. M. Taylor*, and R. S. King. 2008. Does nutrient enrichment 

decouple algal-bacterial production in periphyton? Journal of the North American 

Benthological Society 27:332-334. 

Scott, J. T.*, D. A. Lang*, R. S. King, and R. D. Doyle. 2009. Nitrogen fixation and 

phosphatase activity in periphyton growing on nutrient diffusing substrata: Evidence for 

differential nutrient limitation in stream benthos. Journal of the North American 

Benthological Society 28:57-68. 

*Baylor graduate students 
Introduction

Elemental stoichiometry is gaining favor in ecology 

as a way to study ecosystems, especially the interactions between organisms at all trophic levels and their 

environment. Stoichiometry examines the ratios of key 

elements (nutrients), particularly carbon (C), nitrogen 

(N) and phosphorus (P), in organisms and their food, 

and how differing elemental ratios affect organisms 

and ecosystems (Elser et al. 1996). Nutrient ratios are 

an important aspect of stream ecology because they 

DOI: 10.1127/1863-9135/2008/0171-0233 1863-9135/08/0171-0233 

 © 2008 E. Schweizerbart’sche Verlagsbuchhandlung, D-70176 S

Ontogenic differences in mayfl y stoichiometry 

infl uence growth rates in response to phosphorus 

enrichment

Jeffrey A. Back1*, Jason M. Taylor1, Ryan S. King1, Kari L. Fallert2 and

Emily H. Hintzen2

With 2 fi gures and 4 tables 

Abstract: We contrasted the carbon, nitrogen, and phosphorus (C : N : P) stoichiometry of Caenis spp. 

(Ephemeroptera:Caenidae) nymphs from 2 stream reaches differing in P enrichment. We also estimated growth 

rates of nymphs reared on algae of different P content across four development classes in a laboratory experiment. C : N ratios of fi eld-collected nymphs exhibited variable responses across development classes between sites 

whereas C : P and N : P ratios showed a clear unimodal response, increasing from classes II through IV but then 

declining sharply in class V (nymphs nearing eclosion) at both sites. C : P was lower at the highly enriched site for 

all but the last development class. Growth rates increased in response to P enrichment at the earliest development 

class, but this growth response diminished in later development classes resulting in a signifi cant interaction between 

P treatments and development classes. Trends in fi eld data imply that later stages of development have higher P 

requirements than earlier classes and nutrient enrichment may affect sequestration of P by nymphs. Laboratory data 

suggest that early development classes are more P limited but in light of fi eld results, nymphs may shift P allocation 

from somatic growth to reproductive development as organisms mature. 

Key words: C : N : P ratios, phosporus content, development classes, growth rates, Ephemeroptera, Caenidae.

Fundamental and Applied Limnology

Archiv für Hydrobiologie

Vol. 171/3: 233–240, April 2008

© E. Schweizerbart’sche Verlagsbuchhandlung 2008 

infl uence organism growth, reproduction and li

tory traits and are therefore the basis of trop

teractions. Studies on the stoichiometry of b

invertebrates and their food sources should im

overall understanding of trophic interactions, a

are an essential part of the food web. Howeve

rent knowledge of organism stoichiometry is l

in terms of species examined, ontogenic chang

spatial and temporal variation in elemental co

tion (Frost et al. 2002).

1 Authors’ addresses: Center for Reservoir and Aquatic Systems Research, Department of Biology, Baylor University, O

Place #97266, Waco, TX 76798-7266 USA.

2 Center for Reservoir and Aquatic Systems Research, Department of Environmental Studies, Baylor University, One Be

#97266, Waco, TX 76798-7266 USA.

E-mail addresses: Jeff_Back@baylor.edu, Jason_Taylor1@baylor.edu, Ryan_S_King@baylor.edu, Kari_Fallert@bay

Emily_Hintzen@baylor.edu

* Corresponding author
The importance of phosphorus in affecting growth 

rates has recently been demonstrated (Weider et al. 

2005). Phosphorus-rich ribosomal (r)RNA is necessary for protein synthesis which directly infl uences 

growth rates. Thus, rapid growth rates are associated 

with increased P requirements because organisms must 

disproportionately increase their allocation to P-rich 

rRNA to meet the protein synthesis demands of rapid 

growth. This is the basic tenet of the Growth Rate Hypothesis (GRH) which suggests that organisms with 

rapid growth rates must build P-rich biomass which 

makes them more susceptible to P-limitation (Elser et 

al. 1996, 2006, Vrede et al. 2002). 

Invertebrates at a given life stage are more homeostatic with respect to their body nutrient content than 

autotrophs despite variation in the chemical makeup of 

their food, although the degree of elemental homeostasis varies between species (Sterner & Elser 2002, Peck 

& Walton 2006). In a fi eld study examining consumer-resource stoichiometry, Cross et al. (2003) found 

that P content of invertebrates exhibited much greater 

variability than C or N, and that some taxa might not 

be stoichiometrically homeostatic across life stages. 

Although data from this study did not fi nd a signifi -

cant correlation between body size and %P for the 40 

invertebrate taxa examined, the P content of certain 

functional feeding groups did decrease with increasing body size. Frost & Elser (2002) found a negative 

linear relationship between P content and body size in 

Ephemerella spp. indicating that P content in this taxon is not necessarily fi xed for its lifespan but shifts ontogenetically during development. Similar ontogenetic 

shifts in P content have been observed in aquatic crustaceans (DeMott 2003, Faerøvig & Hesson 2003). 

Mayfl ies (Ephemeroptera) are a widespread and 

important component of stream ecosystems. Previous 

studies have examined the effects of C: N and C: P ratios on mayfl y species growth in both fi eld and laboratory settings. Data demonstrate that high quality food 

(high P and or N content) increases growth and fecundity of several species of mayfl y (Soderstrom 1988, 

Frost & Elser 2002). Mayfl ies are a good organism 

for studying possible stoichiometric difference across 

their ontogeny because development classes have been 

described (e.g. Taylor & Kennedy 2006) or can be easily determined for most species. Whether or not the 

growth rate is constant across all development classes 

is unknown. Moreover, in organisms which undergo 

metamorphosis (e.g. insects), growth and the loss of 

juvenile structures and development of adult structures 

follow a time sequence which may require different 

nutrient levels. Because adult mayfl ies do not feed, all 

the chemical requirements of the adults must be m

by the nymphs. Thus nymphal nutrition must pla

large role in adult reproductive success. Since egg 

emental composition is key to early nymph growth 

survivorship (Tessier et al. 1983, DeMott 2003), 

availability of nutrients to actively feeding nymph

of paramount importance. Because all egg product

is realized in mayfl y nymphs, there should be a dir

linkage between nymphal food quality, fecundity, 

egg nutrient content in mayfl ies. This relations

has been shown in Daphnia (Sterner 1993, Urabe 

Sterner 2001). On the other hand it is possible t

this linkage is relaxed because males may contrib

phosphorus in their sperm packet that is available 

incorporation into eggs. This has been demonstrate

Drosophila spp. (Markow et al. 2001).

The objective of this study was to determine grow

rates for Caenis spp. across a range of developm

classes spanning several levels of nutrient enrichm

We hypothesize that (1) nutrient stoichiometry w

vary through the development cycle of Caenis sp

with highest levels of P at early and late developm

stages due to relatively high levels of somatic and 

productive growth, respectively, (2) that nutrient s

chiometry within a development class from a site w

differ depending on nutrient content of food, and 

growth rates will increase with increasing P conten

food resources and decrease with increasing devel

ment class. 

Methods

Study area

The North Bosque River is a 4th-order (Straehler syst

1:250,000 scale) perennial tributary of the Brazos River loc

in central Texas, USA. The North Bosque fl ows predomina

through the Cross Timbers Level III ecoregion (Griffi th e

2004), an area characterized by semi-arid climate (annual 

cipitation 40–60 cm/y), shallow alkaline clay soils overlay

heavily fractured limestone bedrock, and fl ashy stream fl 

The longitudinal profi le of the North Bosque River exhib

strong nutrient gradient caused by municipal waste water 

puts and runoff associated with concentrated animal feed

operations. These inputs are highest in the upper reaches of 

watershed. Consequently, concentrations of dissolved and t

phosphorus and nitrogen decrease with downstream direc

(Back 2003). 

Two sites along the longitudinal P gradient were sele

for the fi eld study of Caenis spp. to contrast C : N : P ra

among nymphs of differing development classes and f

habitats differing in nutrient content of food resources. 

chose NBOS-03 (31.97692° N, 98.03974° W), a 4th-order re

near Hico, TX as a relatively high phosphorus site. We sel

ed NBOS-05 (31.63760° N, 97.36640° W), a 4th-order re
near Del Mar Ranch at Valley Mills, TX as a contrasting site 

of low-to-moderate levels of phosphorus enrichment (Table 1). 

Both stream reaches exhibited similar physical characteristics, 

typifi ed by short limestone cobble riffl es interspersed with long, 

shallow bedrock glides and pools with moderate deposits of 

fi ne sediment. Historical stream fl ows (United States Geological Survey, http://tx.usgs.gov/basins.html) at each site were also 

similar, although surface discharge was undetectable at the time 

of sampling and streams were reduced to a series of long glides 

and pools interconnected by subsurface fl ow. 

Field study: stoichiometry of Caenis development classes 

Caenis spp. nymphs were collected from both study reaches between 28 September and 7 October 2006. Caenis spp. nymphs 

were collected using a 250 μm Hess sampler from shallow 

gravel substrates at pool margins. Samples (15–25) from each 

site were sieved through soil sieves (2-mm, 1-mm, 0.5-mm, and 

0.25-mm). The retained material in the three smallest sieves 

was stored on ice for transport to the laboratory. We removed 

in the fi eld all large Caenis spp. nymphs retained in the 2 mm 

sieve. In the laboratory we sorted nymphs into one of fi ve development classes following Taylor & Kennedy (2006) using a 

Nikon SMZ 1500 dissecting microscope equipped with a Nikon 

DXM 1200f digital imaging system. Development classes corresponded to external wing pad morphology and pigmentation. Development classes I through V are determined as: I = 

no wing-pads present, II = clear wing-pads present in thoracic 

region, III = wing-pads with veins present in thoracic region, 

IV = wing-pads with veins present in abdominal region or with 

veins and mottling present in thoracic region, V = wing pads 

enlarged, with veins and dark mottling reaching abdominal region (Taylor & Kennedy 2006). These external morphological 

classes are also related to internal changes associated with adult 

development. The use of development classes is superior to arbitrarily making groups based upon size alone because equal 

size among individuals does not necessarily mean they are at 

an equivalent development stage. Because only mature development class V nymphs can be reliably identifi ed to species 

we could only contrast development classes at the genus level. 

We dried all development class samples to a constant mass at 

50 °C, pulverized and homogenized with a mini-beadbeater-8 

(Biospec Products) and stored the powder in a desiccator until 

determination of nutrient content was completed. 

Periphyton (defi ned here as a composite mixture of algae, 

fi ne particulate organic matter, and sediment) was collected 

from each site for determination of C : N : P stoichiometry to 

evaluate whether food resources at the two sites dif

terms of nutrient content. Periphyton was removed fro

by scrubbing with hard-bristle brushes. The entire slu

dried at 50 °C for 24 hours and then pulverized and 

enized the dried periphyton with a mini-beadbeater-8 (

Products) and stored the powder in a desiccator until det

tion of nutrient content was completed. 

Surface-water samples were collected from each 

determination of dissolved (0.45 μm fi lter) and total (un

N and P. Samples were collected in triplicate for NH4-N

NO3-N, TN, PO4-P, and TP and stored and analyzed 

according to standard methods (APHA 1998). All surfac

nutrients were analyzed on a Lachat Quik-Chem 8500 

jection autoanalyzer.

We estimated %C and N content of periphyton and 

spp. nymphs from the fi eld sites using a ThermoQue

EATM 1112 elemental analyzer. Percent P content was 

ed using a Lachat QuikChem 8500 fl ow-injection autoa

using the molybdate colorimetric method following d

in 5-mL 32M sulfuric acid at 350 °C on a digestion b

2-h. Soil (Thermo Finnigan 1.99 %C) and peach lea

1547, 0.137 %P, 2.98 %N) standards were used for QA

determine C, N, and P recoveries, which were all qu

(89–107 %) and consistent among replicates (n = 5 pe

ard). 

Because of the small size of Caenis nymphs (d

ment classes I–V mean mass per individual were 0.00

0.071, 0.289, and 0.461 mg, respectively) relative to t

required to achieve detectable concentrations of P in our 

(10–20 mg), we analyzed composite samples of several 

dreds of nymphs from each site (Table 2). In those cases 

had less than the required mass, we decreased the acid 

and dilution volume proportionately (3–9 mg materia

be digested in 2.5 ml H2SO4 and then diluted to 37.5 m

were unable to collect suffi cient biomass of developme

I nymphs for C : N : P determination.

Polynomial least-squares regression was used to fi t c

ous relationships between increasing development sta

C : P and N : P ratios of nymph composites. Regressio

tions were fi tted to data for each site and patterns were 

tively contrasted between sites to assess whether the h

site (NBOS-03) tended to have lower C : P and N : P rat

the lower-P site (NBOS-05).

Laboratory growth experiment

Green algae (Cladophora and Spirogyra spp.) mats w

lected from Neils Creek, a low-nutrient tributary of th

Bosque River, near Valley Mills, TX, to culture for foo

ments in the growth experiment. Algal mats were colle

24 September 2006 and split this material into four equ

es of approximately 20 g wet mass. We placed split frac

algae in 1 L beakers of fi ltered site water enriched with 

PO4 ranging from +0 (no enrichment, background TP 1

+ 30, + 90 and + 270 μg/L P, respectively. Algal cultur

held in control and enriched stream water at 20oC with c

ous light for 48 hours to allow uptake of P into algal tiss

did not determine the ingestion rates of Caenis spp. ac

development classes and potentially could have not p

enough food at higher development classes. However, o

est supply (0.15 mg C/cm2

) was 3-times higher than th

by Frost & Elser (2002) to represent low food quantity f

larly sized larvae (our development class I). Algal samp

Table 1. Nutrient concentrations for the North Bosque River 

sample locations corresponding to the fi eld collection of Caenis

nymphs. 

Site

Nutrient (μg/L) NBOS-03 NBOS-05

TP 104 46.1

PO4-P 10.9 9.3

TN 2058 610

NO2-N+NO3-N 2.1 6.6

NH3-N 50.2 71.3
removed from beakers and allowed to air dry at 20 oC for several days. Samples were ground to a fi ne powder using a mortar 

and pestle and stored in airtight containers. Nine subsamples 

were analyzed from each algal enrichment treatment for C, N 

and P content using laboratory methods previously described in 

the fi eld study.

We collected Caenis spp. nymphs for the growth study from 

a perennial pool in shallow gravel/sand substrates from the lower-P site (NBOS-05) on the North Bosque River. Twenty-fi ve 

Hess samples (250 μm mesh) were collected as described in 

the fi eld study in order to obtain enough nymphs for the experiment. Live nymphs were separated into the fi ve development 

classes and measured each individual for head capsule width 

(HCW) using a Nikon SMZ 1500 stereomicroscope equipped 

with a Nikon DXM 1200f digital imaging system. We initially 

considered all fi ve development classes, but preliminary results 

suggested that development class V nymphs were too close to 

maturation and would likely emerge during the experiment. 

Thus, we focused only on nymphs of development classes I–IV 

for the growth experiment.

Nymphs were placed individually into 20 mL glass scintillation vials fi lled with 15 mL of fi ltered stream water. Ten 

nymphs were reared from each of the four development classes, 

on each of the four algal treatments in incubators set at 20 oC 

with 12 hour light cycles for ten days. Nymphs were fed in order of increasing development class 0.15, 0.3, 0.6, and 1.2 mg 

C cm–2, respectively. We replaced fi ltered stream water and algae every 3–4 days. Post incubation HCW measurements were 

made for each individual and used these measurements to calculate growth rate. Growth rate was calculated as:

μ = [ln(B2) – ln(B1)] / time

where μ = growth rate, B1 = estimated initial mass and B2 = estimated fi nal mass. We estimated initial and fi nal masses (mg) 

by inserting HCW measurements (μm) into a published HCWweight regression (dry mass = 23.09548 e HCW + 3.19737, r

2

 = 0.97) 

developed for Caenis latipennis by Taylor & Kennedy (2006).

Two-way ANOVA was performed on growth-rate data using development class (I-III) and food quality (four C : P treatments) as main effects and a development class × food quality 

interaction term. High mortality of nymphs in class IV treatments reduced our degrees of freedom below an acceptable 

level for detecting statistical interactions so we only analyzed 

growth-experiment data from development classes I–III . Oneway ANOVA was run a posteriori to test the infl uence of food 

quality on growth rate within each development class, including development class IV. Tukey’s studentized range test 

used to assess the relationships of means between groups 

ANOVA results. Because not all data was distributed norm

all analyses were conducted on ranked data. Effects were c

sidered signifi cant when p ≤ 0.05. ANOVA was performe

SAS 9.1 (SAS Institute, Cary, NC, USA).

Results

Field study: stoichiometry of Caenis develo

ment classes

Surface-water and periphyton had higher N and P l

els at NBOS-03 when compared to the downstream 

(NBOS-05). This suggests that nutrient availability 

periphyton indeed was higher at NBOS-03 and nu

ent content of an important food resource to Cae

refl ected these differences in its elemental compo

tion (Table 2). 

C : P ratio of Caenis spp. nymphs exhibited a u

modal relationship with increasing development cl

(r2 = 0.993, F2, 1 = 74.7, p = 0.081 and r2 = 0.995, F2

107.8, p = 0.048 for NBOS-03 and NBOS-05, resp

tively; Fig. 1a). C : P ratio increased from devel

ment class II to III, but decreased slightly (NBOS-

to markedly (NBOS-05) from class III to V. C : P 

tio at NBOS-05 was greater than the more P-enric

NBOS-03 for all development classes except clas

(Fig. 1a). We observed a similar hump-shaped N 

relationship across development classes and betw

sites (r2 = 0.971, F2,1 = 17.32, p = 0.168 and r2 = 0.9

F2,1 = 207.8, p = 0.041 for NBOS-03 and NBOS-

respectively). This pattern in N : P ratio was larg

due to less dramatic shifts in %N content across 

velopment classes than those observed for %P (Ta

2, Fig. 1b). Neither C : N nor % C showed a consist

trend with increasing development class between si

increasing from II–V at NBOS-03, but no clear patt

at NBOS-05 (Table 2). 

Table 2. Elemental content of periphyton and Caenis spp. nymphs across developmental classes at each stream location.

Periphyton or NBOS-03 NBOS-05

Developmental

Class n1 % C % N % P n % C % N % 

Periphyton – 10.7 1.12 0.116 – 9.70 0.96 0.0

II 504 48.2 11.7 0.93 609 47.0 11.7 0.7

III 231 48.9 12.0 0.81 352 47.2 10.4 0.5

IV 40 51.4 12.0 0.83 40 50.7 10.8 0.7

V 18 53.1 11.5 0.89 9 48.1 10.6 1.

1 number of individual nymphs analyzed in each composite sample
Laboratory growth experiment

Artifi cially enriched algal samples produced a strong 

P gradient across the four algal treatments with P increasing substantially while little change was observed 

in C and N. This resulted in a shift in molar C : P ratios 

from 960 for the control to 62 for the highest P treatment (Table 3). 

Caenis spp. growth rates increased in response to 

P enrichment in the earliest development class but 

this growth response diminished in later development 

classes (Fig 2). Growth rate of Caenis spp. was signifi cantly infl uenced by development class (F2, 67 = 

3.41, p = 0.0388) and the interaction between development class and P treatment (F6, 67 = 2.36, p = 0.0397); 

however, P treatment (F3, 67 = 2.51, p = 0.0661) had 

no effect on growth rate (two-way ANOVA on ranked 

data). Tukey’s studentized range test (α = 0.05) could 

not separate the rank sums of the three development 

classes into statistically different groups. A post hoc 

analysis on data analyzed separately for develo

classes I through IV showed that P treatment 

highly signifi cant effect on growth rate in develo

class I but not development classes II, III and IV 

way ANOVA on ranked data; Table 4). Only th

sums of P treatments 62 and 960 were signifi 

different for development class I (Tukey’s stude

range test on ranked data, α = 0.05; Fig. 2).



















	





	












	

	




	


  !




Fig. 1. Regressions of molar C : P (A), and N : P (B) ratios across 

Caenis spp. development classes at NBOS-03 and NBOS-05 

collection sites.

Table 3. Elemental composition and molar ratios for t

trol and three P-enriched food treatments used in the 

experiments.

Treatment %C %N %P C : N C : P 

+ 0 (Control) 26.4 2.09 0.07 14.7 960 

+ 30 μg/L 25.6 2.02 0.26 14.8 254 

+ 90 μg/L 25.5 2.09 0.70 14.3 95 

+ 270 μg/L 25.2 2.06 1.04 14.3 62 

Table 4. One-way ANOVA and Tukey’s Studentized 

Test results for the effects of food P content on ranked 

rates for each development class of Caenis spp.

Development class df F 

I 3, 22 8.36 

II 3, 24 0.74 

III 3, 21 1.12 

IV 3, 15 0.18 



















" "" """ 

  !

$%&'()*

+



-

-

- - -

- -

-

- -

- - 

, .   . ..  
    



-

-

- - -

- -

-

- -

- - 

, .   . . .  
    

Fig. 2. Mean ± 1 SE growth rates of individually reared 

spp. nymphs in four different development classes acr

different food quality treatments. All food treatments 

sented as molar C : P ratios. Sample sizes are indicated 

bars. Lower case letters above bars represent signifi c

ferences between rank sums of food quality treatment

each development class determined by a Tukey’s Stund

Range Test.
Discussion

Ontogenetic differences in 

Caenis stoichiometry 

The C : P ratios for Caenis were all in the lower 

range of values reported 100–800 (mean 263 ± 113) 

for mayfl ies in Evans-White et al. (2005). Although 

we have no measurement of the amount of variation 

within each development class, C : P and N : P ratios 

showed a clear pattern of increasing and then decreasing values across development classes at both sites. 

Admittedly, having no measure of variation in C : P 

and N : P ratios decreases the certainty of a unimodal 

pattern. However Frost & Elser (2002) showed that the 

smallest mayfl ies had the highest P content. Extending 

this trend to Fig. 1 would further support a unimodal 

pattern. If individual species could be identifi ed for all 

development classes, their stoichiometry could result 

in patterns different than those we observed. However, 

Caenis are collector-gathering detritivores and variability in food type and quality among species is probably low. Therefore, the level of elemental imbalance 

between different Caenis species and their food should 

be similar. This could lead to species with unique elemental content but not necessarily differing patterns 

in stoichiometry across their ontogeny. 

Why would Caenis spp. nymphs differ in their 

C : N : P stoichiometry across development classes? We 

believe that it is a result of differing N and P demands 

needed for the development of somatic and reproductive structures across the ontogeny of Caenis spp. When 

eggs hatch and nymphs grow and metamorphosis progresses through development classes, the early development classes fi rst produce somatic tissue, and later 

(development classes IV and especially V) produce reproductive structures and gametes. Thus the decline in 

C : P ratios in development class IV and V represents a 

high P investment in gametes, which is probably most 

evident in females. This idea is supported by Markow 

et al. (1999, 2001) who demonstrated that adult Drosophila melanogaster and D. nigrospiracula females 

are 3-times more phosphorus rich than males and that 

eggs and male ejaculate are P rich. Furthermore, males 

of D. nigrospiracula, which feed on P-poor cacti, had 

a longer time lag in mating after eclosion than those of 

D. melanogaster, which feed on P-rich fruit. This time 

lag for D. nigrospiracula was thought to represent a 

longer P acquisition time for males because of the low 

P content of its food (Markow et al. 2001). Follicular 

development in D. nigrospiracula was much slower 

than that in D. melanogaster at eclosion. Markow et 

al. (2001) suggest that D. nigrospiracula may have to 

allocate a higher proportion of P to somatic grow

in the larval stages due to low P food. Thus more 

maturation takes place in the adult stage in this s

cies. Elser et al. (2006) showed that P content of lar

in fi ve Drosophila spp. decreased as larvae grew. T

is not the pattern we observed for Caenis spp. Ho

ever, mayfl ies have a major difference in their life h

tory: adults do not feed. Thus, larvae must acquire 

materials necessary for adult survival and reprod

tion. Based on the high P content of adult Drosoph

reproductive structures (Markow et al. 2001), it ma

sense that organisms with non-feeding reproduc

stages would necessarily have late development st

larvae that are richer in P than earlier stages beca

gametes develop and mature in the larvae. The onse

morphological sexual differentiation and developm

of reproductive structures coincides with decreas

C : P ratios in Caenis spp. nymphs in our study. 

aminations of mature female mayfl y larvae (i.e. da

ened wing pads) of Caenis spp. and two other mayfl 

at our sites, Neochoroterpes nanita and Stenone

femoratum, revealed their entire abdomens were 

of eggs and no digestive tract was evident. Fros

Elser (2002) showed a steady decline in %P conten

Ephemerella sp. mayfl ies. Although they did not rep

development classes in their study, mature larvae 

Ephemerella sp. are 9–15 mm in total length and t

complete their life cycle in 9–12 months (Edmund

al. 1976). Since Frost & Elser (2002) collected new

hatched larvae and their experiment duration was 

days, they could not have included late developm

class nymphs in their experiment. Thus the declin

body %P demonstrated in their study supports w

we demonstrated in early development class larvae 

Caenis spp.

Trends in Caenis spp. nutrient stoichiometry 

tween sites of differing nutrient status are consist

with those shown for aquatic insects by Cross et 

(2003). They showed insects from enriched sites 

higher nutrient content than those from less enric

sites. Mayfl y development classes showed higher p

cent nutrient content at the enriched (NBOS-03) 

relative to the less enriched site (NBOS-05). Perc

C increased across development classes. This pr

ably represents an increase in exoskeleton and othe

rich structures associated with growth (i.e. larger bo

size). The %N and P at NBOS-03 was equivalen

higher than NBOS-05 in every case except devel

ment class V. Again, this probably refl ects the nutri

content of the dominant food source which was hig

at NBOS-03 and those mayfl ies had the higher nu

ent content. 
The decrease in C : P ratio of development class V 

(Fig. 1a, b) at NBOS-05 when compared to NBOS-03 

may be the result of the majority of individuals being 

female, and contributing a disproportional amount of 

P to the sample. The latter is supported by Markow 

et al. (1999, 2001) fi ndings on the high P content of 

female Drosophila spp. Consideration of life history 

strategies and adult feeding status needs to be included 

in future studies of ontogenetic changes of elemental 

composition. Villar-Argaiz & Sterner (2002) demonstrated in a freshwater copepod that P defi ciency in 

late stages of development prevented larval Diaptomus 

clavipes from developing into adults, yet younger stages grew just as well as individuals fed a P replete diet. 

Thus timing of increased P acquisition is probably important. Even in insects that have feeding adults, the 

quality of food used by adults needs to be investigated 

in light of larval food quality. Many holometabolous 

insects have larvae and adults that do not use the same 

food resources and each stage may face differing elemental imbalances. Future work is needed to determine the variability of C : N : P ratios within Caenis

spp. development classes and between sexes to better 

understand the degree of plasticity in body chemistry 

among development classes and sexes. 

Laboratory growth experiment

A high potential for rapid growth provides many potential advantages to species because growth and 

development rates can affect many life history traits 

(e.g. age and size at fi rst reproduction) and ecological 

features (e.g. predation risk) (Elser et al. 2006). Our 

fi ndings suggest that increased P content of food increases growth rates of smaller development classes 

of Caenis mayfl y nymphs (Fig. 2). The GRH suggests 

that growth of organisms with higher potential for 

rapid growth will be more P limited. Our data supports this hypothesis within the context of the life 

history of a single taxon. As nymphs mature, growth 

rates and associated P requirements decrease resulting 

in less infl uence of food P content on somatic growth 

rate. Stream insects typically exhibit higher mortality 

during early stages of development (Benke & Huryn 

2007). While many factors such as density affect survivorship, it is plausible that increased growth rates 

during early stages of development have the potential 

to decrease mortality and increase overall production 

of populations when P is not limited. 

Our data showed a signifi cant interaction between 

P content of food and development class on growth 

rates of Caenis spp. This interaction appears to be 

driven by P content of food having an effect on 

rates in development class I but not developmen

II through IV (Fig. 2, Table 4). However, in l

our fi eld results it is plausible that later develo

classes shift P resources from somatic growth to 

ductive development. Nymphs within higher P c

treatments could have potentially begun deve

reproductive structures earlier than nymphs in 

treatments resulting in diminishing growth rates 

on HCW measurements earlier than in lower 

ments. Thus linear dimensions may not increas

though biomass is accumulated via reproducti

velopment. This supports the hypothesis that n

growth but development may be P limited. Inc

development rates have the potential to limit fec

as it is usually correlated with organism size. 

Although P limitation of growth has pre

ly been demonstrated for other mayfl ies inc

Caenis spp. (Frost & Elser 2002) we provide t

observation of ontogenetic shifts in P limitat

growth related to size and development class 

the life cycle of a benthic consumer. Our result

with studies on terrestrial insects done by Else

(2006) that showed similar shifts in P limitation 

the short life cycles of several species of Dro

la. However, our experimental design was lim

higher mortality rates than expected in develo

class IV and the short time period of the exper

We could not determine whether these shifts w

lated to size specifi c growth potential, shifts i

location from somatic growth to reproductive 

opment or a combination of the two. Nonethele

study demonstrates that P requirements for 

and development can vary across a species life 

and that P availability has the potential to limi

life history factors within aquatic benthic cons

Future stoichiometric studies of benthic con

should consider ontogenetic shifts in P limita

growth, the mechanisms that control these shi

the cumulative consequences that varying P lim

across life cycles may have on populations of a

organisms. 

Acknowledgements

We thank David Lang and Charles Stanley for help co

water samples from the two fi eld sites. Hui Huang an

Scott helped analyze nutrient samples. Steve Dwork

ciously allowed us to use his CN autoanalyzer. The qua

clarity of the manuscript were improved by three ano

reviewers. JAB and JMT were supported by grants f

U. S. EPA (CP-966137-01) and the Texas Commission 

vironmental Quality (TCEQ) (subcontract #470122) t

respectively. Although this research was funded in part 
agencies, it does not necessarily refl ect the views of these agencies and no offi cial endorsement should be inferred.

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2008 by The North American Benthological Society DOI: 10.1899/07-108.1

Published online: 25 March 2008

Does nutrient enrichment decouple algal–bacterial

production in periphyton?

J. Thad Scott1, Jeffrey A. Back2, Jason M. Taylor3, AND Ryan S. King4

Center for Reservoir and Aquatic Systems Research, Baylor University, Waco, Texas 76798 USA

Abstract. Coupled production between algae and bacteria in stream epilithon was assessed along a

nutrient-enrichment gradient in 8 Texas streams with open canopies. Photosynthesis (PS) and bacterial

biomass production (BBP) were measured simultaneously using a dual-label radioassay (14C-HCO3

– uptake

and 3

H-L-leucine incorporation into protein) on multiple samples within a stream reach. PS and BBP were

measured after light (1200–1500 lmol m2 s

1

) and dark incubations. The degree of coupled production

between algae and bacteria within a stream was estimated as the covariation (i.e., correlation or covariance)

between PS and BBP derived from unshaded replicates in each stream. Streamwater nutrients ranged from

0.18 to 8.1 mg/L total N and 0.009 to 2.0 mg/L total P. Epilithon N and P content (as % dry mass) and C:N:P

ratios varied widely among streams and were positively correlated with streamwater nutrient

concentrations. Mean BBP measured in light incubations (BBPL) was greater than mean BBP measured in

dark incubations (BBPD), and the difference between the 2 means (BBPL – BBPD) was positively correlated

with mean PS among streams (R2 1⁄4 0.53). Covariance between PS and BBPL within streams (COVPS–BBP)

decreased as epilithon nutrient content increased. COVPS–BBP was positively correlated with both epilithon

C:N (R21⁄4 0.78) and C:P (R21⁄4 0.77) among streams. These results suggest that algal and bacterial production

are decoupled by nutrient enrichment, and that algae might rely more heavily on bacterial-regenerated

nutrients than on streamwater nutrients to support production in nutrient-poor streams.

Key words: algal–bacterial interaction, bacterial production, ecological stoichiometry, eutrophication,

microbial interactions, nutrient criteria, nutrient ratios, nutrient regeneration, periphyton, photosynthesis, streams, water quality.

The inherent link between autotrophic and heterotrophic microbial production has been a recurring

topic in aquatic ecology. In planktonic systems, the

covariation between phytoplankton and bacterioplankton abundance and production often is a

function of bacterial metabolism of phytoplanktonderived extracellular organic C (EOC) (Cole et al.

1982, Coveney 1982, Coveney and Wetzel 1989).

However, bacterioplankton growth also can be limited

by P (Toolan et al. 1991), and inorganic P is the

preferred form of P for both phytoplankton and

bacterioplankton (Cotner and Wetzel 1992). Therefore,

2 competing models of covariation have been adopted

for pelagic systems. The 1st model suggests that

phytoplankton production varies with P concentration, and bacterioplankton vary in response to

phytoplankton. The 2nd model suggests that both

phytoplankton and bacterioplankton respond simultaneously to P. However, patterns in P dynamics and

phytoplankton and bacterioplankton production observed at multiple scales have not conformed consistently to either model. As a result, some researchers

have speculated that the observed covariation depends on interactions involving multiple resources

(i.e., P and C) used by, or pressures (i.e., grazing)

experienced by, both algae and bacteria (Currie 1990,

Coveney and Wetzel 1995).

Most microbial production in low- to mid-order

stream ecosystems generally occurs in benthic biofilms

(Vannote et al. 1980). As in planktonic systems,

bacterial production in stream biofilms (namely

epilithon) often is coupled to algal production and

has been attributed to bacterial metabolism of algal

EOC (Haack and McFeters 1982, Murray et al. 1987).

However, streams with significant canopy cover, such

1 Present address: Department of Crop, Soil, and

Environmental Sciences, University of Arkansas, Fayetteville, Arkansas 72701 USA. E-mail: jts004@uark.edu 2 E-mail addresses: jeff_back@baylor.edu

3 jason_taylor1@baylor.edu

4 ryan_s_king@baylor.edu

332
as forested systems, often have heterotrophic biofilms

that rely on allochthonous organic matter rather than

algal-derived EOC (Findlay et al. 1993). Nevertheless,

bacterial production usually is coupled with algal

production in epilithon when light intensities are

sufficient to stimulate benthic photosynthesis (Kaplan

and Bott 1989, Jones and Lock 1993).

The involvement of nutrients in the interplay

between autotrophic and heterotrophic production in

stream epilithon has been debated, but it has been

studied less than in planktonic systems. Rier and

Stevenson (2001) found that algal biomass was the best

predictor of bacterial cell densities and surmised that

nutrients indirectly influenced bacteria by controlling

algal biomass. They also found no quantitative

relationship between algal biomass and bacterial

density when algal biomass was ,5 lg chlorophyll

a/cm2

. Low algal biomass occurred primarily in

nutrient-poor streams, suggesting a potential competitive interaction between algae and bacteria for

nutrients. In a subsequent experimental study, Rier

and Stevenson (2002) demonstrated that epilithic

bacteria were colimited by organic C and inorganic

nutrients, but they also found that competition for

nutrients did not decrease algal and bacterial covariation. Carr et al. (2005) also found no evidence of

competition between algae and bacteria for nutrients.

Rier and Stevenson (2002) and Carr et al. (2005)

suggested that the coupling between algae and

bacteria in stream biofilms is related to the construction of the polysaccharide matrix, which provides

ample buffering to EOC supply (Freeman and Lock

1995) and is an area of intense nutrient retention and

recycling (Wetzel 1993, Scott et al. 2007). Rier and

Stevenson (2002) went one step farther to suggest that

nutrient-poor biofilms might exhibit stronger algal–

bacterial coupling than nutrient-rich biofilms.

Based on the results of these studies, we present 2

hypotheses: 1) Algae and bacteria do not compete for

nutrients in stream biofilms, but algal and bacterial

production are tightly coupled through simultaneous

cycling of C, N, and P. Increased nutrient availability

decouples algal and bacterial production as algae

become less reliant on bacterial remineralization of

nutrients and bacteria become less stressed for

photosynthetically derived EOC (Fig. 1A). 2) Algal–

bacterial coupling decreases with increasing nutrient

availability (as in hypothesis 1), but decoupling also

occurs in very-low-nutrient environments because of

algal–bacterial competition for nutrients (Fig. 1B). To

test these hypotheses, we measured algal and bacterial

production along a nutrient-enrichment gradient in

several streams in Texas. We quantified the covariation

between algal and bacterial production within each

stream and assessed whether this covariation was

correlated across streams with several metrics of

nutrient availability to periphyton. Based on evidence

from other systems (i.e., King and Richardson 2007),

we suspected that epilithon nutrient content would

track the gradient of nutrient enrichment in surface

water. In particular, we were interested in relating the

degree of coupling between epilithic algal and

bacterial production to the nutrient content of the

epilithon itself.

Methods

Study area

The study was conducted in the Cross Timbers

Ecoregion within the Brazos River Basin in central

Texas (Fig. 2). Eight wadeable and historically perennial streams were selected as candidates based on

surface-water soluble reactive P (SRP) and total P (TP)

concentrations as measured by the Texas Commission

on Environmental Quality within the previous 5 y

(TCEQ 2007). The candidate streams were intended to

represent a continuum of nutrient enrichment. Reconnaissance sampling in June 2006 revealed that these

streams were distributed along a steep nutrient

gradient, spanning ,10 lg/L to .1 mg/L TP. In a 3-

wk period from July to August 2006, one 300- to 500-m

study site was sampled in each stream to assess

coupling between algal photosynthesis (PS) and

heterotrophic bacterial biomass production (BBP),

and C, N, and P content of epilithon. Surface-water

total N (TN) and TP concentrations and discharge

were measured in each site.

Field sampling

In each site, 5 rocks (;50 cm2

) were collected for

simultaneous measurements of PS and BBP under

high-light (1400–1700 lmol m2 s

1

) conditions, 5

rocks were collected for PS and BBP measurements in

dark incubations, and 2 rocks were collected to serve

as killed (4% buffered formalin) controls. Rocks were

collected from shallow pools (,0.3 m) and from areas

of open canopy with high light exposure. Epilithon

growing in unshaded environments does not generally

experience photoinhibition (Boston and Hill 1991). The

rocks in all streams were colonized by thin biofilms.

Some small patches of macroalgae occurred sporadically in all streams, and these growth forms were

intentionally avoided.

Twenty-five rocks were collected from each site (5

rocks 3 5 locations/site) for measurement of epilithon

ash-free dry mass (AFDM) and C, N, and P content.

Epilithon was removed from rocks in the field. Rocks

2008] ALGAL–BACTERIAL COUPLING IN PERIPHYTON 333
were scraped with a knife, scrubbed with a toothbrush, and rinsed into a pan with stream water.

Epilithon from the 25 rocks was pooled into a single

sample, diluted to a known volume in a 1-L dark

bottle, and returned to the laboratory. Rock surface

area was measured by pressing aluminum foil over

each rock and cutting the foil margin to match the size

of the scraped area. We weighed the cleaned and dried

foil in the laboratory and estimated rock surface area

based on a foil mass–area relationship.

PS and BBP measurements

PS and BBP were estimated simultaneously on

individual rocks using 14C-HCO3

– uptake and 3

H-Lleucine incorporation into protein, respectively. Rocks

were put top-down into 60-mL wide-mouth jars. Jars

were filled with filtered (0.2-lm pore size) stream

water until no air space remained, and they were

sealed with open-top caps lined with silicon septa.

Rocks were inverted so that the jars could be placed

upside down in streams for incubation. Five jars were

left unwrapped for light incubation, and the other 5

were wrapped in aluminum foil for dark incubation.

Killed controls were incubated in light.

Each jar was injected with 50 lL of 14C-labeled

NaHCO3 solution (54 lCi/mL) and placed in the

stream at a depth of 7 to 10 cm for 1.5 h. After that

time, 50 lL of 3

H-labeled L-leucine were injected into

each jar, and the incubation was continued for 30 min.

Concentrations of leucine ranged from 500 to 580

nmol/L (specific activity 1⁄4 0.166 lCi/nmol leucine,

assuming negligible background leucine concentration), depending on the volume of rock being

FIG. 1. Two alternative models (hypotheses) of photosynthesis (PS) and bacterial biomass production (BBP) coupling

in stream epilithon along a nutrient-enrichment gradient.

Areas of no shading within each graph indicate the zone of

maximum facilitation (greatest coupling) along a nutrient

gradient. A.—Coupling is highest in nutrient-poor streams

and diminishes as nutrient availability increases. B.—Algae

and bacteria are slightly decoupled in lowest-nutrient

streams because of competition for nutrients, coupled under

moderate-nutrient availability, and decreasingly coupled as

nutrient availability increases.

FIG. 2. Study sites in 8 wadeable streams in the Cross

Timbers Ecoregion within the Brazos River watershed in

central Texas. Watershed boundaries for each stream

sampled are indicated with dark outline.

334 J. T. SCOTT ET AL. [Volume 27
incubated. Leucine concentrations .400 to 500 nmol/

L were necessary to saturate leucine incorporation into

protein in similar microbial communities (Thomaz and

Wetzel 1995, Gillies et al. 2006, JTS, unpublished data).

Incubations were stopped by adding buffered formalin

to a final concentration of 4%. Samples were returned

to the laboratory and stored at 48C until processing.

Proteins in intact epilithon were precipitated with

trichloroacetic acid (TCA). A previous study on

metaphyton showed that TCA protein precipitation

was necessary to maximize 3

H-L-leucine recovery and

had little effect on 14C retention by photoautotrophs

(Scott and Doyle 2006). Incubation water was removed

from each jar and replaced with 5% TCA. Jars were

placed on ice for 1 h, after which samples were

removed from TCA. Epilithon was scraped from rocks

with a toothbrush, rinsed into a slurry with 5% TCA,

and diluted to a known volume (30–50 mL) with 5%

TCA. Samples were homogenized with a vortex mixer.

A subsample was filtered onto a precombusted and

weighed glass-fiber filter (GF/F; pore size1⁄40.7 lm) for

determination of AFDM. A 2nd subsample was filtered

onto a 0.2-lm-pore-size polycarbonate filter and

washed twice with 5% TCA, once with 80% ethanol,

and once with deionized (DI) water. These filters were

placed in scintillation vials with 5 mL of alkaline

solution (0.5 mol/L NaOH, 25 mmol/L ethylenediamine-tetra-acetic acid [EDTA], and 0.1% sodium

dodecylsulfate) and agitated for 1 h at 858C. Material

attached to polycarbonate filters dissolved in the

alkaline solution (Buesing and Gessner 2003). A 2.5-

mL aliquot of this solution was radioassayed for 14C

and 3

H activity on a Beckman LS 6500 liquid

scintillation counter (Beckman Coulter, Fullerton,

California). Activities of both isotopes were corrected

for quench with external standards and then converted

from radioactivity estimates to HCO3

– and leucine

uptake rates based on the specific activity of each

isotope used in incubations.

The specific activity of inorganic C in each sample

was determined by combining the quantity of 14Clabeled HCO3

– with the quantity of unlabeled dissolved inorganic C (DIC) in incubation. DIC concentration in filtered incubation water was measured with

a Shimadzu TOC-Vcsh C analyzer (Shimadzu Scientific Instruments, Columbia, Maryland). DIC concentration was multiplied by the volume of water in

incubation jars to estimate the total quantity of

inorganic C available. The volume of each individual

rock was measured via liquid displacement and

subtracted from 60 mL to arrive at the final water

volume in incubation. The measured 14C radioactivity

of samples was converted into PS using the specific

activity of inorganic C for all incubations. PS was

normalized to the surface area of the rock from which

the epilithon originated (see Field sampling section) or

to measured AFDM and expressed as lg C cm2 h1

and lg C mg1 AFDM h1, respectively (Wetzel and

Likens 2000).

Background leucine concentration in incubation

water was assumed to be negligible. Therefore, the

specific activity of leucine was constant, and measured

3

H radioactivity was converted directly to leucine

uptake. BBP was calculated from leucine uptake (nmol

leucine/h) by assuming that the fraction of leucine in

protein 1⁄4 7.3%, cellular C/protein 1⁄4 86%, and isotope

dilution was negligible (Kirchman 2001). BBP was

normalized to the surface area of the rock from which

the epilithon originated (see Field sampling section) or

to measured AFDM and expressed as lg C cm2 h1 or

lg C mg1 AFDM h1

, respectively.

AFDM and nutrients

Epilithon from samples used in PS and BBP

incubations was processed for AFDM only. Epilithon

from the 25-rock composite sample collected from each

stream was processed for determination of AFDM and

C, N, and P content. The epilithon–streamwater slurry

was homogenized using a blender and diluted to a

known volume with DI water, and 3 subsamples were

removed. While the epilithon slurry was mixed

vigorously, a 2- to 5-mL subsample was transferred

onto a precombusted (5008C, 4 h) and weighed GF/F

for AFDM measurement. A 2nd 100- to 160-mL

subsample was transferred into a large weighing boat.

The water was allowed to evaporate, and the sample

was dried at 608C. The bulk sample was then

homogenized into a powder for C, N, and P analysis.

A 3rd subsample was transferred into a 50-mL

centrifuge tube. Epilithon in these tubes was separated

from inorganic material by centrifugation in colloidal

Si using the method of Hamilton et al. (2005).

Following separation and several rinsing steps to

remove Si, organic matter (OM) samples were dried

and homogenized for C, N, and P determination. C

and N content in bulk epilithon and epilithon OM

were measured simultaneously using a Thermo

Finnigan FlashEA 1112 elemental analyzer (Thermo

Fisher Scientific, Waltham, Massachusetts). P content

was measured colorimetrically on a Lachat Quickchem

8500 (Hach, Loveland, Colorado) following a 3-h

digestion in concentrated H2SO4 at 3508C (APHA

1998). Total N and P content in water samples collected

from each stream was measured colorimetrically on a

Lachat Quickchem 8500 following either alkaline

persulfate (N) or acid persulfate (P) digestions (APHA

1998).

2008] ALGAL–BACTERIAL COUPLING IN PERIPHYTON 335
Data analyses

The amount of BBP stimulated by PS in each stream

was calculated by subtracting the mean BBP rate

measured in dark incubations (BBPD) from the mean

BBP rate measured in light incubations (BBPL) (mean

BBPL – mean BBPD). The respective errors were

propagated additively. Model II major-axis regression

was used to assess the relationship between (mean

BBPL – mean BBPD) and mean PS because our

predictor (mean PS) and response (mean BBPL – mean

BBPD) variables were random estimates with error

(Legendre and Legendre 1998). Statistical tests on

slope and y-intercept were conducted using 999

random permutations of our data. A FORTRAN

program available online (Casgrain and Legendre

2007) was used for this analysis because model II

regression is not available in most standard statistical

software packages. The relationship between PS and

BBPL across all streams was assessed using ordinary

least squares (OLS) regression (Proc GLM, SAS version

9.1; SAS Institute, Cary, North Carolina). The covariance value between PS and BBPL within each stream

reach was calculated as

COVPSBBP 1⁄4 Xn

i1⁄41

ðPSi PSÞðBBPLi BBPLÞ

n ;

where i is the incubation replicate. The correlation

coefficient (CORRPS–BBP) was the covariance divided

by the product of individual standard deviations (SD)

of PS and BBPL. OLS regression was used to determine

how strongly each metric of PS–BBPL coupling (i.e.,

correlation and covariance) was related to streamwater

nutrient concentrations and epilithon (bulk and OM)

nutrient content. Negative relationships were expected

between PS–BBPL coupling metrics and streamwater

and epilithon nutrient concentrations. Positive relationships, indicative of decreasing covariance between

PS and BBPL with increasing nutrient content in

epilithon, were expected between PS–BBPL coupling

and epilithon C:N or C:P.

Results

Habitat and epilithon elemental composition

Streamwater nutrient concentrations confirmed that

a strong nutrient-enrichment gradient existed among

sites. TP levels ranged from ,10 lg/L to .2 mg/L,

and TN levels ranged from ,200 lg/L to .8 mg/L

among sites (Table 1). DIC concentrations varied little

among sites, with the exception of NBOS-03, where

DIC was 23 to 33 lower than in other sites. Three sites,

DUFF-01, LEON-01, and NBOS-03, had substantial

standing water but no detectable surface flow during

sampling events. Surface flow was only slightly above

detection levels at PALU-01, ROCK-01, and SALA-01,

but it was relatively high in sites below wastewater

discharges (LEON-02 and NOLC-01) (Table 1).

Epilithon elemental composition also confirmed the

existence of a nutrient-enrichment gradient. Epilithon

bulk P content ranged from 0.028% at ROCK-01 to

0.224% at NOLC-01, and bulk N content ranged from

0.28% at PALU-01 to 1.4% at NOLC-01 (Table 2).

Epilithon OM P content ranged from 0.030% at ROCK01 to 0.235% at NOLC-01, and OM N content ranged

from 0.76% at ROCK-01 to 1.98% at LEON-02. Both

bulk and OM C content were highly variable among

sites but did not appear to be related to the nutrientenrichment gradient (Table 2). Epilithon OM nutrient

content was more similar among streams than were

streamwater nutrient concentrations. For instance,

sites with very similar streamwater P (PALU-01,

ROCK-01, and SALA-01; Table 1) had decidedly

different OM P contents (Table 2). Epilithon AFDM

was variable among sites, but this variation was not

explicitly related to differences in nutrient levels

among sites (Table 2).

Epilithon PS and BBPL

PS and BBPL were highly variable among sites

(Table 3). BBPL rates were always greater than BBPD

rates, and mean BBPL – mean BBPD was positively

correlated with mean PS across all sites (Fig. 3).

TABLE 1. Water chemistry and flow at sampling locations. DIC 1⁄4 dissolved inorganic C.

Stream Site code DIC (mg/L) Total N (lg/L) Total P (lg/L) Discharge(m3

/s)

Paluxy River PALU-01 26.6 215.0 9.89 0.001

Rocky Creek ROCK-01 19.9 176.3 10.0 0.009

Salado Creek SALA-01 17.2 296.0 12.3 0.005

Duffau Creek DUFF-01 16.7 507.3 24.4 0.0

North Bosque River NBOS-03 8.92 1686 45.6 0.0

Leon River upstream LEON-01 22.2 1197 62.7 0.0

Nolan Creek NOLC-01 23.4 6925 1860 0.556

Leon River downstream LEON-02 25.9 8110 2030 0.046

336 J. T. SCOTT ET AL. [Volume 27
Epilithon PS and BBPL values in individual replicates

were positively correlated across streams sites regardless of whether surface area (Fig. 4A) or AFDM (Fig.

4B) was used to normalize PS and BBPL. However, the

relatively large amount of epilithon biomass at NBOS03 (Table 2) exerted disproportional influence on areal

production rates. When this site was excluded from

the regression analysis of area-normalized rates, the

slope of the linear fit decreased from 0.4 to 0.16, and

the amount of BBPL variation explained by PS

increased by 23% (Fig. 4A). In a similar analysis using

the AFDM-normalized rates, slope did not change, but

the amount of explained variation increased by 20%

(Fig. 4B).

Replicate data within sites indicated that PS and

BBPL were positively correlated in all streams, with the

exception of area-normalized rates at DUFF-01 (Table

3). Mean correlation coefficients for PS and BBPL

among sites were similar for area-normalized (r 1⁄4 0.78

6 0.42 SD) and AFDM-normalized (r1⁄40.79 6 0.26 SD)

rates. PS–BBPL covariance was much more variable,

and covariance of area- and AFDM-normalized rates

ranged across 4 orders of magnitude (Table 3). Average

area-normalized PS–BBPL covariance was 1.6 3 103 6

1.8 3 103

, whereas average AFDM-normalized PS–

BBPL covariance was 2.3 3 103 6 4.7 3 103

.

PS and BBPL covariation along nutrient-enrichment

gradient

Correlation coefficients (CORRPS–BBP) and covariance values (COVPS–BBP) for area-normalized PS and

BBPL rates were not correlated with any measure of

nutrient concentration. CORRPS–BBP for AFDM-normalized PS and BBPL rates also were not correlated

with any measure of nutrient concentration. However,

COVPS–BBP values for AFDM-normalized PS and BBPL

(COVPS–BBP,AFDM) were correlated with multiple metrics of nutrient concentration across sites.

COVPS–BBP,AFDM was not significantly related to

streamwater TN (Fig. 5A) or TP (Fig. 5B), epilithon

bulk % N (Fig. 5C) or bulk % P (Fig. 5D), or to

epilithon bulk C:N (Fig. 5E). In 2 streams, study sites

were directly downstream of wastewater inputs.

Data from these 2 sites were particularly influential

in several of these regressions (white circles; Fig.

5A–F). Exclusion of these streams from the regression analyses improved the relationship between

COVPS–BBP,AFDM and streamwater TN and TP (Fig.

5A, B), and epilithon bulk % P and bulk C:P (Fig.

5D, E, respectively), but only the relationship

between COVPS–BBP,AFDM and bulk % P was statistically significant after excluding these streams (Fig.

5D). The relationship between COVPS–BBP,AFDM and

bulk C:P was statistically significant regardless of

whether wastewater-influenced streams were includTABLE 2. C, N, and P content (% of dry mass) measured from bulk epilithon and epilithon organic matter (OM) separated by

centrifugation. Values represent a single composite sample from 25 rocks sampled in each stream reach. Mean (61 SD) ash-free dry

mass (AFDM) is of epilithon from 12 individual rocks used in 14C and 3

H assays (5 replicate light incubation, 5 replicate dark

incubation, 2 killed controls). IS 1⁄4 insufficient amount of sample available for analysis.

Site code % bulk C % bulk N % bulk P % OM C % OM N % OM P AFDM (mg/cm2

)

PALU-01 8.61 0.28 0.036 IS IS 0.080 0.99 6 0.32

ROCK-01 13.57 0.55 0.028 14.99 0.76 0.030 0.67 6 0.19

SALA-01 18.53 0.75 0.030 18.11 0.98 0.050 1.39 6 0.27

DUFF-01 7.83 0.45 0.050 12.38 0.82 0.073 1.39 6 0.40

NBOS-03 12.42 0.77 0.117 18.19 1.45 0.142 1.87 6 0.65

LEON-01 8.42 0.62 0.068 12.71 1.10 0.090 1.35 6 0.59

NOLC-01 16.14 1.38 0.224 19.19 1.68 0.235 0.85 6 0.21

LEON-02 11.25 0.86 0.100 33.00 1.98 0.189 1.21 6 0.33

FIG. 3. Major-axis regression of mean (61 SE) difference

between bacterial biomass production in light (BBPL) and in

dark (BBPD) incubations (BBPL – BBPD) versus photosynthesis (PS) of epilithon at sites in 8 Texas streams along a

nutrient-enrichment gradient. Data were normalized relative

to AFDM of the epilithon; n 1⁄4 5 in each 8 streams.

2008] ALGAL–BACTERIAL COUPLING IN PERIPHYTON 337
ed, but the relationship was much stronger when

wastewater-influenced streams were excluded (Fig.

5F).

COVPS–BBP,AFDM was not significantly related to OM

% N regardless of whether the wastewater-influenced

streams were included (Fig. 6A). COVPS–BBP,AFDM was

not significantly related to OM % P when all sites were

included, but it was significantly negatively related to

OM % P when wastewater-impacted streams were

excluded (Fig. 6B). COVPS–BBP,AFDM was significantly

positively related to OM C:N and C:P (Fig. 6C, D).

Exclusion of wastewater-influenced sites had little

effect on the relationship between COVPS–BBP,AFDM

and OM C:N (Fig. 6C) but strengthened the relationship between COVPS-BBP,AFDM and OM C:P (Fig. 6D).

Discussion

The objective of our study was to assess how

nutrient availability was related to coupling between

algal and bacterial production in stream epilithon. We

hypothesized that: 1) nutrient enrichment would

decrease the covariation between algal and bacterial

production, and 2) covariation between algal and

bacterial production would diminish in nutrient-poor

epilithon because of competition for nutrients. Our

results support hypothesis 1 but not hypothesis 2.

COVPS–BBP,AFDM decreased with increasing nutrient

concentration, and the relationship was particularly

strong when comparing COVPS–BBP,AFDM with epilithon OM C:N and C:P (Fig. 6C, D). This result

suggests that algae (and perhaps both algae and

bacteria) rely on regenerated nutrients to support

production in nutrient-poor streams, but they rely less

on regenerated nutrients for production as nutrient

availability increases. This paradigm has been well

established in oceanic plankton communities (sensu

Dugdale and Goering 1967), but it has not been well

FIG. 4. Linear regression of bacterial biomass production

in the light (BBPL) versus photosynthesis (PS) normalized

relative to surface area (A) and ash-free dry mass (AFDM) (B)

of epilithon on 5 individual rocks at sites in 8 Texas streams

along a nutrient-enrichment gradient. Solid lines are regressions based on data from all streams. Dashed lines are

regressions based on data that excluded site NBOS-03. Site

codes are given in Table 1.

TABLE 3. Mean (61 SD) photosynthesis (PS) and bacterial biomass production in the light (BBPL) for all streams and multiple

metrics for estimating the relationship between PS and BBPL at each site. CORRPS–BBP is the correlation coefficient between PS and

BBPL in light incubations in each stream. COVPS–BBP is the covariance between these variables in each stream. Metrics were

calculated for rates normalized to surface area of rocks and epilithon ash-free dry mass (AFDM).

Site code PS (lg C cm2 h1

) BBPL (lg C cm2 h1) CORRPS–BBP,area COVPS–BBP,area CORRPS–BBP,AFDM COVPS–BBP,AFDM

PALU-01 0.061 6 0.021 0.029 6 0.005 0.96 7.45 3 105 0.96 3.03 3 104

ROCK-01 0.184 6 0.059 0.053 6 0.014 1.0 5.24 3 104 1.0 1.37 3 102

SALA-01 0.272 6 0.193 0.086 6 0.033 0.97 4.92 3 103 0.88 2.30 3 103

DUFF-01 0.121 6 0.009 0.066 6 0.007 –0.24 –7.52 3 106 0.86 1.64 3 104

NBOS-03 0.315 6 0.101 0.213 6 0.062 0.76 3.77 3 103 0.21 6.70 3 105

LEON-01 0.130 6 0.010 0.046 6 0.018 0.86 1.25 3 103 0.64 2.44 3 104

NOLC-01 0.287 6 0.079 0.052 6 0.013 0.92 7.81 3 104 0.79 2.02 3 104

LEON-02 0.212 6 0.082 0.057 6 0.018 0.99 1.19 3 103 0.99 1.06 3 103

338 J. T. SCOTT ET AL. [Volume 27
demonstrated in attached microbial communities,

such as epilithon.

We found no evidence that competitive interactions

decoupled algal and bacterial production in nutrientpoor streams. In fact, COVPS–BBP,AFDM was greatest in

the most nutrient-poor streams, and this result is

similar to a model proposed for planktonic microbial

communities (Cotner and Biddanda 2002). The lack of

competitive decoupling between epiphytic algae and

bacteria also is consistent with results of other studies

on epilithic stream biofilms (Rier and Stevenson 2002,

Carr et al. 2005), which provide support for the

existence of mutually beneficial interactions between

microbial autotrophs and heterotrophs. Efficient recycling of C, N, and P within biofilm matrices has long

been suspected as an important driver of community

function in biofilms (Wetzel 1993). A recent stableisotope study of metaphyton N cycling provides

support for this idea (Scott et al. 2007). Larned et al.

(2004) demonstrated that mass transfer of nutrients

into epilithon is the most likely rate-limiting step in

nutrient transfer from stream water into epilithic algal

cells. Therefore, nutrient regeneration in biofilms

might be the most significant process affecting nutrient

FIG. 5. Linear regression of the covariance of photosynthesis (PS) and bacterial biomass production in the light (BBPL)

normalized to epilithon ash-free dry mass (AFDM) (COVPS–BBP,AFDM) vs streamwater total N (TN) (A) and total P (TP) (B), epilithon

bulk % N (C) and % P (D) content, and epilithon bulk C:N (E) and C:P (F) in 8 Texas streams along a nutrient-enrichment gradient.

Solid lines are regressions based on data from all streams. Dashed lines are regressions based on data excluding 2 streams from

which sites were directly below wastewater discharges (open circles; NOLC-01 and LEON-02). Site codes are given in Table 1; n 1⁄4 5

rocks/stream for COVPS-BBP,AFDM.

2008] ALGAL–BACTERIAL COUPLING IN PERIPHYTON 339
availability to cells growing within its polysaccharide

matrix.

Influences of streamwater nutrients vs epilithon nutrients

on algal–bacterial coupling

COVPS–BBP,AFDM was more strongly related to

epilithon OM nutrient content than to epilithon bulk

nutrient content or streamwater nutrient concentrations. Previous studies exploring algal–bacterial covariation along nutrient gradients have used primarily

streamwater nutrient concentrations as metrics of

nutrient availability (Carr et al. 2005). However,

epilithon nutrient content, including extracellular

nutrients within the biofilm matrix, integrate shortterm temporal changes in streamwater nutrient availability. Therefore, epilithon nutrient content might be a

more representative indicator of average stream

conditions than a single water sample. In our study,

epilithon nutrient content, and OM nutrient content in

particular, differed more among streams than did

streamwater nutrient concentrations. This contrast was

particularly evident in streams with low streamwater

nutrient concentrations. Thus, our study indicates that

epilithon nutrient content is a better indicator of

nutrient availability to biofilm microorganisms than

are streamwater nutrient concentrations.

The strongest correlations with COVPS–BBP,AFDM

were revealed when epilithon OM was explicitly

isolated and elemental ratios of this organic fraction

(e.g., C:N and C:P) were used. Epilithon from

wastewater-impacted streams had the highest % N

and % P, but also the highest % C. Therefore, C:N and

C:P values in epilithon of wastewater-impacted

streams were higher than in other streams, even

though wastewater-impacted streams had the most

nutrient-rich epilithon (as a percentage of dry mass).

This result suggests that epilithon C content in

wastewater-impacted streams might be influenced by

the dissolved organic C available in effluent. For

example, COVPS–BBP,AFDM was higher than expected

for wastewater-impacted streams when evaluated

using streamwater nutrient (Fig. 5A, B), bulk epilithon

nutrient (Fig. 5C, D), or epilithon OM nutrient (Fig. 6A,

B) models. However, elemental ratios accurately

predicted COVPS–BBP,AFDM across all streams, particularly when derived from epilithon OM (Fig. 6C, D).

Thus, a stoichiometrically explicit approach (sensu

FIG. 6. Linear regression of the covariance of photosynthesis (PS) and bacterial biomass production in the light (BBPL)

normalized to epilithon ash-free dry mass (AFDM) (COVPS–BBP,AFDM) vs epilithon organic matter (OM) % N content (A), % P

content (B), C:N (C), and C:P (D) in 8 Texas streams along a nutrient-enrichment gradient. Solid lines are regressions based on data

from all streams. Dashed lines are regressions based on data excluding 2 streams from which sites were directly below wastewater

discharges (open circles; NOLC-01 and LEON-02). Site codes are given in Table 1; n 1⁄4 5 rocks/stream for COVPS-BBP,AFDM.

340 J. T. SCOTT ET AL. [Volume 27
Sterner and Elser 2002) might be particularly useful for

understanding trophic dynamics in stream microbial

communities (Frost et al. 2002, 2005).

Nutrient regeneration as a basis for trophic coupling

Based on evidence gathered in this and other studies

(Cotner and Biddanda 2002, Rier and Stevenson 2001,

2002, Carr et al. 2005), we suggest that nutrient

deficiency strengthens trophic coupling between algae

and bacteria in the epilithon of oligotrophic streams

through the interdependence of C, N, and P cycles

within the biofilm matrix. For example, bacterial

metabolism of algae-derived EOC is supported by

studies that show increased bacterial extracellular

enzyme activity associated with algal photosynthesis

(Espeland et al. 2001, Francoeur and Wetzel 2003).

Furthermore, the decomposition of P-containing compounds at night, or during periods of low photosynthetic activity, is supported by observations of

concomitant increases in bacterial phosphatase activity

(Espeland and Wetzel 2001). Rier et al. (2007) found

that bulk phosphatase (derived from algae, bacteria,

and fungi) activity remained high at night in stream

periphyton growing on inert substrata and on leaf

detritus in a high-light environment. Therefore, sustained bulk phosphatase (Rier et al. 2007) or increased

bacterial phosphatase (Espeland and Wetzel 2001)

activity coupled with decreased algal uptake of P at

night (Reshkin and Knauer 1979) would result in

substantially higher bacterial P uptake or P accumulation in the biofilm matrix. P accumulation at night

could fuel P-limited photosynthesis and bacterial EOC

decomposition in the subsequent daylight hours.

Other studies have suggested that bacteria specifically

control P recycling within biofilm communities.

Sharma et al. (2005) found that phosphatase activity

was spatially segregated from chlorophyll-containing

microorganisms in metaphyton from the strongly Plimited Florida Everglades. They hypothesized that

bacteria provided the primary mechanism for P

remineralization, which directly increased algal photosynthesis and subsequently EOC production within

metaphyton.

Recent studies have indicated that dissolved biochemicals such as deoxyribonucleic acid (DNA) can be

an important source of P in marine sediments

(Dell’Anno and Danavaro 2005) and that bacteria

might use extracellular DNA specifically as a nutritional supplement (Palchevskiy and Finkel 2006).

Furthermore, decomposition of biochemical aggregates, which are likely to include P-rich lipids, might

provide another important source of P recycling in

biofilms (Wotton 2007).

The same argument might be presented for N

cycling. However, fewer studies on N recycling in

periphyton exist, and results of those studies are

inconsistent. For instance, Francoeur and Wetzel (2003)

found that experimental periphyton communities

exhibited higher leucine-aminopeptidase (LAMP) activity when grown in dark conditions with low

dissolved inorganic N (DIN) availability. However,

the addition of glucose resulted in higher LAMP

activity in DIN-deficient periphyton grown under

lighted conditions. Furthermore, LAMP activity in

natural periphyton communities was affected inconsistently by light, glucose, and DIN additions (Francoeur and Wetzel 2003). However, Scott and Doyle

(2006) demonstrated that bacterial production and

algal production were strongly coupled in nutrientdeficient metaphyton, but they became decoupled

following N enrichment.

Interactions between microbial autotrophs and

heterotrophs no doubt exist, and growing evidence

from photosynthesis, microbial enzyme linkages (Romanı ́ and Sabater 1999, Espeland et al. 2001, Francoeur

and Wetzel 2003, Francoeur et al. 2006, Rier et al. 2007),

and coupled algal–bacterial production studies (Scott

and Doyle 2006, our study) suggest that mutually

beneficial interactions might be common. In mutualistic interactions, bacteria facilitate increased algal

production via nutrient regeneration, and algae in

turn, facilitate increased bacterial production via

increased EOC generation. Mutualism in this form

might increase production efficiencies, support the

relatively high and sustained production observed in

nutrient-poor systems (Wetzel 1993), and exert substantial influence on the energy budgets of oligotrophic ecosystems (Vadeboncoeur et al. 2002).

Spatial heterogeneity within streams and sources of

variation among streams

Our results support the idea that epilithon functions

as microbial landscapes (Battin et al. 2007). Both PS

and BBPL were highly variable within and among

sites. In our study, the standard deviation of epilithic

biomass on rocks within the same stream (determined

from samples used in radioassays) was usually 30% to

50% of mean biomass value (Table 2). Furthermore,

biomass substantially influenced both PS and BBPL on

individual rocks (Fig. 4A). However, AFDM-normalized PS and BBPL still showed considerable variation

within sites (possible sources of variation discussed

next). We exploited this spatial variation to better

understand the coupling between PS and BBP along

the nutrient gradient. However, many studies overlook microscale variability and struggle to control the

2008] ALGAL–BACTERIAL COUPLING IN PERIPHYTON 341
substantial noise generated by spatial heterogeneity in

ecological data. We echo the sentiments of Battin et al.

(2007) and urge benthic microbial ecologists to exploit

microscale heterogeneity in biofilm communities,

which is undoubtedly important to ecological processes.

Our study was designed to identify only correlative

relationships across a stream nutrient-enrichment

gradient. Therefore, we could not control other factors

that might have contributed to differences in epilithon

metabolic activity and coupling between algal and

bacterial production. We attempted to minimize

differences in stream habitat conditions, but some

variables exhibited substantial variation among

streams. For instance, our intention was to sample

open-canopy stream habitats with similar flow conditions, but an extended drought in Texas during the

summer of 2006 resulted in diminished stream flow in

most of our study streams and no measurable flow in

some (Table 1). The 2 streams with greatest current

velocity were those in which wastewater effluent

provided most of the flow. Current velocity and

discharge were not correlated directly with any

measurements of algal and bacterial metabolism or

epilithon nutrient content in our study. However,

indirect effects of reduced flow, which might have

contributed to measured variations between streams,

such as boundary-layer conditions, periphyton thickness, and periphyton community composition (Stevenson 1996), were not explicitly examined.

The intentional focus of our study was on stream

epilithon growing in open-canopy, high-light conditions. Similar patterns seem unlikely for periphyton

growing in shaded habitats. Findlay et al. (1993) found

that algae and bacteria were not strongly coupled in

heterotrophic biofilms of forested streams. Nutrient

cycling might be governed by similar mechanisms in

shaded and unshaded periphyton communities, but

the links between autotrophs and heterotrophs probably are damped by the reliance of heterotrophs on

allochthonous organic matter.

Concluding remarks

Evidence from our study suggests that the degree of

coupling between autotrophs and heterotrophs in

periphyton decreases along a nutrient-enrichment

gradient (Fig. 1A). However, decoupling does not

appear to occur as a result of competition for nutrients

in nutrient-poor streams (Fig. 1B). Because our study

was correlative by design, these results should be

confirmed with reach-scale or stream-mesocosm manipulative experiments. Our results support the idea of

mutual facilitation between photoautotrophs and

heterotrophic bacteria in aquatic microbial communities. Microbial mutualisms of this sort might support

high sustained production in attached communities

and heavily influence the energetic budgets of oligotrophic ecosystems. Nutrient enrichment of surface

waters and accelerated eutrophication of aquatic

ecosystems undoubtedly alter ecosystem functions.

Our study provides another example of the effect of

anthropogenic nutrient enrichment on aquatic ecosystem function by demonstrating that stream-nutrient

enrichment can decouple algal and bacterial production in stream epilithon.

Acknowledgements

Financial support for our study was provided by a

grant from the US Environmental Protection Agency

(cooperative agreement #CP-966137–1) to RSK and

Bryan Brooks. The authors thank Charles Stanley and

David Lang for field support and Sara Seagraves for

assistance with water-chemistry analyses. We also are

grateful to Thomas Neu for his insights on the

biochemical composition of biofilm matrices. We thank

Jim Cotner, Andre Amado, and Ed Hall for their

helpful comments on a previous draft of this manuscript.

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Received: 27 August 2007

Accepted: 5 February 2008

344 J. T. SCOTT ET AL. [Volume 27
J. N. Am. Benthol. Soc., 2009, 28(1):57–68

2009 by The North American Benthological Society

DOI: 10.1899/07-107.1

Published online: 18 November 2008

Nitrogen fixation and phosphatase activity in periphyton growing on

nutrient diffusing substrata: evidence for differential nutrient

limitation in stream periphyton

J. Thad Scott1

Center for Reservoir and Aquatic Systems Research, Department of Biology, Baylor University, Waco, Texas

76798 USA, and Department of Crop, Soil, and Environmental Sciences, University of Arkansas,

Fayetteville, Arkansas 72701 USA

David A. Lang2, Ryan S. King3, AND Robert D. Doyle4

Center for Reservoir and Aquatic Systems Research, Department of Biology, Baylor University,

Waco, Texas 76798 USA

Abstract. We explored N2 fixation and alkaline phosphatase activity (APA) in periphyton from a N-limited

stream ecosystem by coupling measurements of these processes with nutrient diffusion substrata (NDS)

experiments. We measured periphyton biomass accumulation (as ash-free dry mass [AFDM] and

chlorophyll a [CHLA]), N2 fixation, and APA to evaluate the relative importance of N2 fixation as an N

source to the periphyton community and APA as an indicator of P deficiency in a seemingly N-limited

system. We used fritted-glass-disc NDS and estimated AFDM, CHLA, N2 fixation, and APA on days 6, 18,

and 29 after deployment. Periphyton AFDM steadily increased on NDS over time, but was not influenced by

nutrients. CHLA was elevated in the N treatment on days 18 and 29, indicating autotrophic N limitation.

Consistent with N limitation, N2 fixation was high but not different in the control and P treatments and was

virtually undetectable on the N treatment. N2 fixation in control and P treatments was detectable in both

light and dark incubations, and dark rates were 4 to 73% of the light rates on days 18 and 29. The average

contribution of total N2 fixation to periphyton in control and P treatments was 0.93 mg N/m2 on day 18 and

1.0 mg N/m2 on day 29. APA was significantly elevated on the control and was highest in the N treatment

despite no apparent P limitation of periphyton biomass accumulation. P enrichment always decreased APA.

Measurable N2 fixation and the change in CHLA suggest that autotrophs were primarily N limited.

However, APA observed in controls demonstrated that some portion of the periphyton community was

experiencing P deficiency. This result suggests that periphyton metabolism was related to both N and P

availability, but that biomass accumulation might have been limited primarily by N. One explanation for

these findings is that different organisms, perhaps occupying different trophic positions within the

community, might have been limited by different elements.

Key words: algae, epilithon, NDS, nitrogenase activity, nitrogen fixation, phosphatase activity.

A common paradigm in aquatic ecology has been that

N usually limits primary production in coastal marine

environments, whereas P usually limits primary production in freshwater environments. However, this

paradigm is not withstanding challenges, particularly

those directed from meta-analyses on large data sets

(Francoeur 2001, Elser et al. 2007). As a result, the

efficacy by which freshwater systems were thought to

overcome N limitation and come into equilibrium with P

limitation has come into question. Schindler (1977)

suggested that P generally would limit primary production in freshwater lakes over long time scales because N2

fixation could supply sufficient N to counterbalance

ecosystem N deficiency. This paradigm has been

generally accepted by stream ecologists, but few studies

have explored the role of instream N2 fixation as a source

of N to streams (Marcarelli et al. 2008), and the

1 E-mail addresses: jts004@uark.edu

2 davidalanlang@gmail.com

3 ryan_s_king@baylor.edu

4 robert_doyle@baylor.edu

W:/xml/jnabs/jnbs_2801/jnbs-28-01-06.3d Wednesday, 12 November 2008 10:35 am Allen Press, Inc. Page 57

57
importance of N2 fixation in alleviating N limitation to

benthic microbial communities has been assessed only

rarely (Scott et al. 2007). What is certain is that N limits

primary production in streams over relatively short time

scales as frequently as P does (Francoeur 2001) and that

populations within the periphyton community might

experience differential nutrient limitation (i.e., some

limited by N, some limited by P).

Nitrogenase catalyzes the fixation of gaseous N2 into

biologically available NH4

þ (Voet and Voet 2004). In

aquatic environments, N2 fixation is carried out by

certain cyanobacteria, heterotrophic bacteria, and

some diatoms with cyanobacterial endosymbionts

(Howarth et al. 1988). Periphyton N2 fixation and its

relationship with dissolved inorganic N (DIN) availability has been studied extensively, but most of these

studies have focused on lentic habitats (Doyle and

Fisher 1994, Rejma ́nkova ́ and Koma ́rkova ́ 2000, Inglett

et al. 2004, Scott et al. 2007). Even modest levels of DIN

will signal organisms to suspend nitrogenase synthesis

and subsequently stop N2 fixation because N2 fixation

is energetically expensive. However, N2 fixation can

provide a significant amount of N to the entire

periphyton community when DIN concentrations are

low. For instance, Grimm and Petrone (1997) demonstrated that fixed N2 provided up to 85% of the net N

flux into the benthos of a desert stream.

Marcarelli and Wurtsbaugh (2006) recently showed

that P and temperature interacted to control N2 fixation

in periphyton from oligotrophic streams. P enrichment

increased periphyton N2 fixation rates by increasing the

density of N2-fixing organisms within the periphyton

community. Higher temperature also increased N2

fixation by increasing the density of N2-fixing organisms

and increasing the rate of cell-specific N2 fixation. P

enrichment also increased periphyton biomass (as

chlorophyll a [CHLA]), but N enrichment had no effect

on periphyton biomass. Therefore, the study by

Marcarelli and Wurtsbaugh (2006) provided a good

example of how N2 fixation can supplement the N

supply to periphyton communities that are strongly P

limited. What remains unclear is how much N can be

provided by N2 fixation in streams experiencing

stronger N limitation. Eutrophic streams might experience stronger and more frequent N limitation than

oligotrophic streams because anthropogenic nutrient

sources usually have a lower N:P ratio than do natural

nutrient sources (Downing and McCauley 1992). However, N2 fixation in eutrophic streams is an area of

research that has been neglected (Marcarelli et al. 2008).

Enzyme activities are an important source of

information on the physiological state of microbial

communities. In particular, enzymes associated with

nutrient cycling are informative indicators of nutrient

status (Hill et al. 2006). For instance, phosphatases

catalyze the hydrolysis of dissolved organic phosphate

esters and are generated by a number of aquatic

microbes, including bacteria, algae, and protozoans

(Chro ́ st 1991). Studies on natural systems and in

laboratory cultures have demonstrated that microbial

phosphatase activity increases when P limits growth

(Healey and Hendzel 1980, Chro ́ st and Overbeck

1987). Alkaline phosphatase activity (APA) has been

widely studied in planktonic systems (Rose and Axler

1998), but has received less attention in benthic

environments. Patterns of increasing periphyton APA

are related to decreasing inorganic P availability and

decreasing periphyton P content in lotic (Klotz 1992,

Mulholland and Rosemond 1992) and lentic (Kahlert et

al. 2002, Scott et al. 2007) benthic habitats. However,

most periphyton APA measurements, particularly for

streams, were done in studies with the goal of relating

microbial enzyme activities to microbial biomass

distributions (Sinsabaugh et al. 1991, Romanı ́ and

Sabater 2000, 2001). Scott et al. (2007) used APA as an

indicator of P deficiency in N2-fixing microbial

communities in a freshwater wetland, but similar

studies have not been done in streams.

Nutrient-diffusing substrata (NDS) are useful tools for

identifying nutrient limitation in periphyton communities over short time scales (15–60 d). NDS have been used

in lakes (Fairchild and Lowe 1984, Pringle and Bowers

1984, Fairchild et al. 1985), wetlands (Scott et al. 2005),

streams (Lowe et al. 1986, Winterbourn and Fegley 1989,

Tate 1990, Marcarelli and Wurtsbaugh 2006, Tank et al.

2006), and large rivers (Corkum 1996, Scrimgeour and

Chambers 1997). Periphyton accumulates on diffusion

surfaces when NDS are deployed in natural waters, and

differences in accumulation rates between nutrient

treatments and controls are interpreted as evidence of

periphyton nutrient limitation. Periphyton accumulation is most often measured as ash-free dry mass

(AFDM) or CHLA. AFDM represents the combined

organic matter of algae and heterotrophs (e.g., bacteria,

fungi) in the periphyton, whereas CHLA represents the

biomass of algae only. Many studies also have used

taxonomic assessments to explore changes in algal

community composition after nutrient enrichment with

NDS. However, few studies have coupled NDS experiments with more-complex measurements of periphyton

community function such as N2 fixation and APA.

We measured periphyton N2 fixation, APA, and

biomass accumulation (as AFDM and CHLA) to

understand the importance of N2 fixation in alleviating

N limitation to stream periphyton. We worked in a

stream-like outflow of a constructed freshwater marsh.

N2 fixation and APA must be measured in enclosed

containers. Therefore, we used the micro-NDS design

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58 J. T. SCOTT ET AL. [Volume 28
developed by Gibeau and Miller (1989) and modified

by Marcarelli and Wurtsbaugh (2006), wherein fritted

glass discs (FGDs) are used as diffusion surfaces at the

end of plastic tubes containing enrichment media.

FGDs provide a solid surface that can be removed

from NDS and placed inside closed syringes for N2

fixation and APA assays. We estimated nutrient

diffusion rates from NDS by measuring the rate of

nutrient depletion from enriched agar because our

NDS design differed slightly from those previously

described in the literature.

Based on ambient nutrient levels and previous

periphyton work in our study area (Scott et al. 2005),

we expected that surface-water N would be sufficiently low and P would be sufficiently high to induce N

limitation in periphyton. Thus, we hypothesized that

biomass accumulation rates would be enhanced by N

enrichment but not by P enrichment. We also

hypothesized that N2 fixation would be elevated on

unenriched control NDS and highest on P-enriched

NDS, where the N:P ratio of nutrient supply was

lowest. We also hypothesized that N2 fixation would

be deactivated by N enrichment, whereas APA would

be elevated on the N treatment only if P became

limiting in response to N enrichment. Last, we

hypothesized that N2 fixation and APA would be

more sensitive than AFDM and CHLA to changes in

nutrient availability. Our goals were to evaluate N2

fixation as a source of N to the periphyton community

and to assess the importance of P deficiency in a

seemingly N-limited system.

Methods

Study site

We deployed NDS in the stream-like outflow of the

Lake Waco Wetland (LWW), near Waco, Texas, USA,

from 23 May 2006 to 21 June 2006. Previous research on

metaphyton in the study area indicated that N limits

periphyton production primarily in the downstream

areas of this created wetland (Scott et al. 2005, 2007). The

outflowing stream connecting LWW with Lake Waco is

;100 m long, has an average width of 3 m, and has

primarily sandy substrate with Typha latifolia growth

along its margins. Thalweg depth at the deployment

location was 0.4 m. Velocity and discharge during the

study were ;0.15 m/s and ;0.1 m3

/s, respectively. The

deployment location was an open site with low

marginal Typha growth and lacked canopy cover.

NDS and experimental design

We constructed NDS based on the designs of Gibeau

and Miller (1989) and Marcarelli and Wurtsbaugh

(2006). Both of these designs used a FGD as a growth

substrate that enabled nutrients to diffuse from an agar

medium through the porous surface of the FGD. Our

design differed from previous designs in that larger

vessels (50-mL centrifuge tubes) were used to store

agar in an effort to minimize the ratio of FGD surface

area to agar volume. We cut 22-mm-diameter holes in

the caps of 50-mL centrifuge tubes (Carolina Biological

Supply, Burlington, North Carolina), and inserted

FGDs (25-mm diameter, 4–5.5-lm porosity; Wilmad

LabGlass, Buena, New Jersey) inside the cap, exposing

most of the FGD surface through the top of the cap. We

inserted a 3-mm-wide rubber gasket inside the cap to

seal the bottom of the FGD to the top rim of the

centrifuge tube.

We prepared NDS with 20 g/L agar and either no

nutrient enrichment (control), N enrichment (0.51 M

N from NaNO3), or P enrichment (0.065 M P from

Na3PO412H2O). We randomly placed NDS in a rack

(60 3 90 cm) made from wire netting framed with

plastic-coated plant stakes. We used a 2nd set of stakes

to anchor the rack in the substrate at its deployment

location in the wetland outflow. We deployed 51 NDS

of each treatment (control, N, and P) to determine rates

of biomass accumulation (AFDM and CHLA), N2

fixation, APA, and N and P diffusion from NDS for a

29-d study period. We retrieved 3 randomly chosen

NDS from each treatment on days 1, 2, 3, 12, and 24 to

determine agar N and P concentrations and periphyton AFDM. We retrieved 12 NDS from each treatment

on days 6, 18, and 29 to determine agar N and P

concentrations and periphyton AFDM, CHLA, N2

fixation rates, and APA.

Water chemistry and temperature

We collected 50-mL surface-water grab samples

upstream and immediately downstream of the NDS

rack on days 3, 6, 12, 18, and 24 after deployment. We

estimated total N (TN), total P (TP), NO2 þ NO3-N

(NOx-N), and PO4-P levels using colorimetric methods

(APHA 1992) on a Lachatt Quik-Chem 8500 flowinjection autoanalyzer (Hach, Loveland, Colorado). We

analyzed TN and TP following alkaline and acid

persulfate digestions, respectively. We monitored

water temperature throughout the study period with

a HOBOt Water Temp Pro temperature logger (Onset

Computer Corporation, Bourne, Massachusetts) attached to the NDS rack.

N and P diffusion rates

We estimated nutrient diffusion rates by monitoring

how rapidly nutrients disappeared from NDS agar. We

sampled 3 randomly chosen NDS from each treatment

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2009] PERIPHYTON N2 FIXATION AND APA 59
(control, N, and P) on days 1, 2, 3, 6, 12, 18, 24, and 29

after deployment. We extracted agar from the centrifuge tubes and melted it in a beaker with ;900 mL

deionized water. We cooled the solution to room

temperature, diluted it to 1 L, and collected 7-mL

duplicate subsamples that we held frozen for ,28 d

until analysis for dissolved NOx-N and PO4-P concentrations.

Estimation of N2 fixation, APA, AFDM, and CHLA

We kept periphyton intact on FGDs from NDS for

measurement of N2 fixation and APA before removal

for biomass estimation (AFDM, CHLA). We measured

N2 fixation on 5 FGDs and APA on another 5 FGDs

from each treatment 3 sampling day combination. We

used the 2 remaining NDS as dark incubation controls

in N2-fixation assays. We conducted N2 fixation and

APA assays in 50-mL Popper micromate syringes with

3-way stopcocks (Popper and Sons, New Hyde Park,

New York). We transferred periphyton on FGDs into

syringes with either 30 mL of stream water for N2

fixation assays or 30 mL of 1.2% Tris buffer for APA

assays.

N2 fixation.—We estimated N2 fixation using acetylene reduction (Flett et al. 1976). We injected 5 mL of

acetylene gas, generated from the dissolution of

calcium carbide, into syringes containing samples in

30 mL stream water. We gently agitated samples to

dissolve the acetylene and then incubated them under

artificial lighting (250 lmol m2 s

1

) for 2 to 4 h. We

incubated 2 samples from each treatment in the dark to

estimate the importance of N2 fixation by heterotrophs.

In addition, we incubated streamwater samples under

lights to account for any background N2 fixation in

seston. We removed samples from incubation and

analyzed them one at a time and recorded the length

of incubation to the nearest minute. We drew 15 mL of

air into each syringe and agitated the samples to

establish equilibrium between water and vapor phases. We withdrew 1 lL of air from each syringe and

injected it into a Carle AGC series gas chromatograph

(Hach Carle, Loveland, Colorado) equipped with a

flame ionization detector and a 1.8-m column packed

with 80% Porapack N and 20% Porapack Q. The

column temperature was 708C. We used 10-ppm

ethylene standards to calibrate the instrument daily.

To estimate N2 fixation, we assumed that the production of 3 lmol ethylene was equivalent to the fixation

of 1 lmol N2 (Flett et al. 1976).

APA.—We estimated APA using the rate by which

phosphate was cleaved from 4-methylumbelliferyl

phosphate (MUFP) resulting in the production of

methylumbelliferone (MUF), which fluoresces (emission wavelength 455 nm) when irradiated at 365 nm

wavelength (Healey and Hendzel 1979). We injected 4

mL of 0.2-mM MUFP into syringes containing samples

in 1.2% Tris buffer. We gently agitated syringes,

incubated samples at room temperature, and measured fluorescence after 5, 20, and 45 min. We removed

subsamples from incubation and analyzed samples

one at a time and recorded the length of incubation to

the nearest minute. We measured fluorescence on a

Turner 10 AU fluorometer (Turner Designs, Sunnyvale,

California) calibrated with 50-, 100-, 250-, 500-, and

1000-lg/L MUF standards. We used the slope of a

linear regression of time (min) vs MUF concentration

to calculate APA activity. Instances in which the MUFP

concentration was not saturating were rare. However,

we derived APA activity from the exponential increase

in MUF concentration vs time using regression when

we observed this phenomenon.

Periphyton biomass.—After N2 fixation and APA

assays, we removed FGDs from syringes and scraped

and rinsed periphyton to form a 50-mL slurry. We

collected two 25-mL aliquots onto separate glass fiber

filters (GFFs) for AFDM and CHLA analyses, respectively. We estimated AFDM on prewashed, precombusted, and preweighed GFFs. We dried AFDM

samples at 1008C for 1 h, weighed them to estimate

dry mass, combusted them for 1 h at 5008C, and

weighed them again to estimate ash content. We

determined phaeophytin-corrected CHLA using the

method described by Biggs and Kilroy (2000). We

extracted CHLA overnight in 90% ethanol (heated)

and measured pigments by absorbance on a Beckman

DU series spectrophotometer (Beckman Coulter, Fullerton, California).

Data analysis

We used paired t-tests (SAS 9.1.3; SAS Institute,

Cary, North Carolina) to compare water-column TN,

TP, NOx-N, and PO4-P concentrations upstream and

downstream of the NDS rack. We used nonlinear

regression models of nutrient diffusion vs day of

retrieval (SigmaPlot 9.0; SYSTAT, Point Richmond,

California) to estimate nutrient diffusion rates from

agar.

We used 2-factor analysis of variance (ANOVA;

SAS) with day of retrieval (date) and nutrient

treatment (nutrient) as main effects and a date 3

nutrient interaction term on AFDM, CHLA, N2

fixation, and APA. We used standard 2-way factorial

ANOVA rather than repeated-measures ANOVA

because we did not take repeated measurements from

the same samples over time, but rather sampled a

unique set of NDS during each collection; thus,

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60 J. T. SCOTT ET AL. [Volume 28
adjustments for serial autocorrelation were inappropriate. Post hoc comparisons of significant main effects

or interaction terms were done with LSMEANS with

Tukey adjustments (SAS). The significance level for all

tests was p , 0.05.

We applied log10(x) transformations to most data

sets before analyses because group variances tended to

increase with mean response magnitude. Before

transformation, we anchored each data set by setting

the minimum data value to 1.0 by adding or

subtracting a constant to the values in the data set.

This technique standardizes the effect of transformations (Sokal and Rohlf 1995, Osborne 2002) and keeps

the resultant log-transformed values positive.

Results

Water chemistry and N and P diffusion rates from NDS

Mean NOx-N concentrations were higher downstream than upstream of the NDS rack during the 29-d

study period (t 1⁄4 2.46, p 1⁄4 0.03). TN, TP, and PO4-P

concentrations did not differ between upstream and

downstream samples (Table 1). Mean water temperature was 25.58C (range: 23–288C) during the study

period.

Mean agar concentrations of NOx-N and PO4-P

declined exponentially during the study period. Day29 NOx-N and PO4-P remained within ;40% and 75%

of day-0 concentrations, respectively. NOx-N concentrations of the control and P treatment were ;0. PO4-P

concentrations of the control and N treatment also

were low, but the PO4-P concentrations tended to be

slightly higher in the N treatment than in the control.

The 1st derivatives of exponential-loss equations

predicted the release rates for NOx-N and PO4-P of

the N and P treatments, respectively (Fig. 1A, B). The

predicted release rate of NOx-N from the N treatment

decreased by .50% by day 2, but the release rate

decreased more gradually after day 3 (Fig. 1A). NDS

released 2.9 mg NOx-N cm2 d1 on day 6, 1.2 mg

NOx-N cm2 d1 on day 18, and 0.57 mg NOx-N cm2

d1 on day 29. The release rate of PO4-P from the P

treatment did not exhibit a rapid initial decline (Fig.

1B). NDS released 0.41, 0.26, and 0.18 mg PO4-P cm2

d1 on days 6, 18, and 29, respectively.

Effects of N and P enrichment on periphyton accrual

Periphyton AFDM increased significantly during the

study period (F7,129 1⁄4 92.88, p , 0.0001; Fig. 2).

However, area-normalized AFDM did not differ

significantly among nutrient treatments (F2,129 1⁄4 1.30,

p 1⁄4 0.2751), nor was the nutrient 3 date interaction

significant (F14,129 1⁄4 0.54, p 1⁄4 0.9056). AFDM accrual

appeared linear and was similar among nutrient

treatments between days 1 and 6. AFDM accrual rate

was 0.1197 mg cm2 d1 between days 1 and 6 and was

0.2197 mg cm2 d1 between days 24 and 29. Areanormalized CHLA varied significantly among nutrient

treatments over the study period (F4,97 1⁄4 5.12, p 1⁄4

0.0009; Fig. 3). CHLA was significantly greater in the N

treatment than in control or P treatments on days 18

and 29.

N2 fixation and APA following nutrient enrichment

N2 fixation varied significantly among nutrient

treatments during the study period, regardless of

TABLE 1. Mean (61 SE) surface-water N and P concentrations upstream and downstream of the nutrient diffusing

substrate rack. TN 1⁄4 total N, TP 1⁄4 total P, NOx-N 1⁄4 NO2 þ

NO3-N. n 1⁄4 5.

Analyte

Concentration (lg/L)

Upstream Downstream Paired t 1-tailed p

TN 556.2 6 27.3 592.8 6 35.2 1.58 0.09

TP 105.5 6 5.3 104.3 6 6.1 –0.64 0.28

NOx-N 49.6 6 3.2 62.8 6 4.5 2.46 0.03

PO4-P 24.5 6 0.4 29.9 6 3.1 1.70 0.08

FIG. 1. N (A) and P (B) release rates from nutrient

diffusing substrates.

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2009] PERIPHYTON N2 FIXATION AND APA 61
normalization method (p , 0.001; Fig. 4A–C). N2

fixation did not differ significantly among nutrient

treatments on day 6. However, N2 fixation was

significantly greater in control and P than in N

treatments on days 18 and 29. N2 fixation did not

differ significantly between control and P treatments

(Fig. 4A–C). Biomass (CHLA)-normalized N2 fixation

in control and P treatments was significantly lower on

day 29 than on day 18 (Fig. 4C). A similar pattern was

apparent for biomass (AFDM)-normalized N2 fixation,

but the difference between days was not significant

(Fig. 4B). N2 fixation in dark incubations was

consistently .0 in control and P treatments (Table 2).

N2 fixation in dark incubations accounted for 4.0 to

73.3% of N2 fixation in light incubations, except in the

FIG. 2. Mean (61 SE) periphyton biomass measured as

area-normalized ash-free dry mass (AFDM) on control, N,

and P nutrient diffusing substrate treatments over 29 d.

FIG. 3. Mean (61 SE) periphyton biomass measured as

chlorophyll a (CHLA) on control, N, and P nutrient diffusing

substrate treatments over 29 d. Values with the same letter

are not significantly different (Tukey–Kramer adjusted

posthoc comparisons).

FIG. 4. Mean (61 SE) N2 fixation rates normalized by area

(A), periphyton ash-free dry mass (AFDM) (B), and

periphyton chlorophyll a (CHLA) (C) on control, N, and P

nutrient diffusing substrate treatments over 29 d. Values with

the same letter are not significantly different (Tukey–Kramer

adjusted post hoc comparisons).

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62 J. T. SCOTT ET AL. [Volume 28
control treatment on day 6 when N2 fixation was 73

higher in dark than in light incubations (Table 2).

APA always was significantly lower in P than in

control or N treatments, regardless of normalization

method (F2,36 1⁄4 57.17–91.67, p , 0.0001; Fig. 5A–C).

APA varied significantly among nutrient treatments

over the study period (F4,36 1⁄4 2.92–5.19, p 1⁄4 0.0022–

0.0343; Fig. 5A–C). On day 18, area-normalized APA

was greater in the N than in the control treatments

(Fig. 5A). In the control treatment, area-normalized

APA did not differ significantly between days 6 and

18, but was significantly greater on day 29 than on

days 6 and 18. In the N treatment, area-normalized

APA increased significantly between days 6 and 18,

but did not change between days 18 and 29. In control

and N treatments, biomass-normalized APA was

significantly lower on day 29 than on day 18 (Fig.

5B, C).

Discussion

NDS diffusion rates and surface-water chemistry

Initial rapid loss of nutrients from NDS limits their

usefulness in long-term studies (Corkum 1996, Rugenski et al. 2008). Most NDS studies are limited to the

period of measurable nutrient diffusion from treatments. Deployment periods for NDS designs similar to

ours range from 2 wk (Bernhardt and Likens 2004) to 2

mo (Von Schiller et al. 2007), but typically are 3 to 4 wk

so that diffusion rates remain relatively constant (Tank

and Dodds 2003). Bernhardt and Likens (2004)

reported that day-14 N and P diffusion rates were

only 10% and 25%, respectively, of day-2 rates. In our

study, depletion of N was initially rapid and exponential, but resulted in only 60% loss during the 29-d

study period. Depletion of P was more gradual, and

resulted in only 25% loss during the study period. Our

results also suggest that N and P diffusion remained

high enough on day 29 (0.57 mg NOx-N cm2 d1

, 0.18

mg PO4-P cm2 d1

) to inhibit periphyton N2 fixation

and APA. Furthermore, N and P diffusing from NDS at

the end of the study period was high relative to N and

P requirements of periphyton growing on them. We

TABLE 2. Mean periphyton N2 fixation rates (61 SE) in

dark incubations. n 1⁄4 2.

Treatment Day

Dark N2 fixation

(lgNm2 h1

)

% of mean light

N2 fixation

Control 6 15.0 6 9.17 727.0

18 19.1 6 3.28 37.0

29 10.0 6 6.14 11.6

N 6 0.00 6 0.00 0.0

18 0.00 6 0.00 0.0

29 0.00 6 0.00 0.0

P 6 0.81 6 0.81 33.3

18 32.3 6 16.18 73.3

29 1.96 6 0.67 4.0

FIG. 5. Mean (61 SE) alkaline phosphatase activity

(APA), expressed as mass phosphatase (Pase) per minute

and normalized to area (A), periphyton ash-free dry mass

(AFDM) (B), and periphyton chlorophyll a (CHLA) (C) on

control, N, and P nutrient diffusing substrate treatments over

29 d. Values with the same letter are not significantly

different (Tukey–Kramer adjusted posthoc comparisons).

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2009] PERIPHYTON N2 FIXATION AND APA 63
did not measure periphyton N and P content, but N

and P content of stream epilithon (Stelzer and

Lamberti 2001) and metaphyton from the wetland

that provides the water for our study stream (Scott et

al. 2007) range from 1.0 to 2.0% N and 0.1 to 0.2% P.

Mean periphyton dry mass on day 29 of our study was

;3 mg/cm2

. If we apply N and P contents from Stelzer

and Lamberti (2001) and Scott et al. (2007) to

periphyton in our system, periphyton N content

probably ranged from 30 to 60 lg/cm2 and periphyton

P probably ranged from 3 to 6 lg/cm2

. Thus, the N

and P diffusion rates were sufficient to replace

periphyton N 10 to 203 and periphyton P 30 to 603

on day 29. Sufficient N and P were provided by NDS

by the end of our study period to maintain the

integrity of our nutrient-enrichment treatments.

We attempted to minimize the effect of confounding

experimental factors by randomizing NDS positions in

the stream. Thus, concentrations of dissolved N might

have been slightly higher at downstream than at

upstream NDS. NOx-N concentrations were slightly

higher in water collected 0.5 m downstream than in

water collected immediately upstream of the rack. The

slightly higher NOx-N concentrations supplied to

downstream NDS might have dampened periphyton

response to nutrient treatments in downstream samples, but the concentrations of NOx-N in downstream

water were certainly lower than the exposure concentrations of NOx-N on the NDS. Moreover, we did not

detect any spatial pattern in biomass or enzyme

activity as a function of the position of samples in

the rack. Thus, we conclude that upstream–downstream differences in streamwater nutrients had

minimal influence on our results, but the relative

position of treatments should be considered carefully

in the design of future NDS studies (Tate 1990).

N2 fixation, APA, and biomass on NDS

N2 fixation.—Periphyton N2 fixation ranged from 44

to 86 lgNm2 h1 in control and P treatments on days

18 and 29. This range is 4 to 83 higher than the median

of 22 values reported by Marcarelli et al. (2008).

However, mean rates in some of the studies reviewed

by Marcarelli et al. (2008) were as high as 410 to 8400

lgNm2 h1 (Buckley and Triska 1978, HowardWilliams et al. 1989, Grimm and Petrone 1997,

Marcarelli and Wurtsbaugh 2006). The highest rates

were reported from Sycamore Creek (Grimm and

Petrone 1997), a strongly N-limited stream in the

Sonoran Desert (Grimm and Fisher 1986). Furthermore,

the highest rates occurred during mid-summer in all

studies, consistent with summer N2-fixation maxima in

lentic systems (Howarth et al. 1988). Our study was

done in late May through mid-June, and rates might be

substantially higher during July and August.

We hypothesized that N2 fixation would be highest

in control and P treatments and that N enrichment

would suppress N2 fixation by periphyton. N2 fixation

rates were lower in N than in control treatments, but P

enrichment did not stimulate N2 fixation in our

system. Mean N2 fixation was always lower on P

treatments than on controls, although this difference

was never statistically significant. That P enrichment

did not stimulate N2 fixation was unexpected. We see

no obvious explanation for this result, but offer the

following possibilities. 1) The composition of the

microbial community might have changed in the P

treatment (sensu Fairchild et al. 1985), with the

consequence that a different community of algae and

bacteria had slightly lower N2 fixation potential.

However, we lack taxonomic data to support this

hypothesis. Marcarelli and Wurtsbaugh (2006) found

that P enrichment increased the number of N2-fixing

taxa in an oligotrophic stream, but other studies have

reported that P enrichment decreased the rate of N2

fixation in some stream periphyton communities

(Marcarelli and Wurtsbaugh 2007). 2) Increased N

remineralization might have occurred in response to P

enrichment. Several studies have demonstrated the

importance of N-mineralizing extracellular enzymes in

periphyton (Francoeur and Wetzel 2003, Francoeur et

al. 2006, Rier et al. 2007), but to our knowledge, no

studies have explored how P enrichment affects N

remineralization within the periphyton matrix. 3)

Heterotrophic N2 fixation might have decreased in

response to P enrichment. Sundareshwar et al. (2003)

reported reduced heterotrophic N2 fixation in estuarine sediments after P enrichment, but found that

glucose alone and glucose þ P additions stimulated N2

fixation. We did not replicate dark N2 fixation

measurements sufficiently for statistical comparisons,

but N2 fixation in the dark was 4 to 73% of N2 fixation

in the light on days 18 and 29. Thus, heterotrophic N2

fixation could have influenced our results and might

have been affected by availability of dissolved organic

C in the periphyton matrix. More work is needed to

elucidate these mechanisms.

Daily N2 fixation was 0.93 mg N m2 d1 on day 18

and 1.0 mg N m2 d1 on day 29 (mean of control and

P treatment N2 fixation rates integrated over a 14:10 h

day:night cycle). The rates of N diffusion in the N

treatment suppressed N2 fixation on days 18 (12.0 g N

m2 d1

) and 29 (5.7 g N m2 d1) and supplied 6 3 103

to 12 3 103 times more N to the N-enriched periphyton

than was supplied by N2 fixation to periphyton in

control and P treatments. We cannot say what

minimum rate of N supply from NDS might have

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64 J. T. SCOTT ET AL. [Volume 28
inhibited N2 fixation. However, agar N concentrations

can be modified to manipulate diffusion rates (Rugenski et al. 2008), and future studies might be able to

clarify the relationships between diffusion rates of

NO3

– or NH4þ from NDS and up- or down-regulation

of N2 fixation by periphyton.

N2 fixation rates in streams generally are much

lower than instream denitrification and stream DIN

uptake rates (Marcarelli et al. 2008). This conclusion

suggests that N2 fixation might seldom be an

important source of N in stream ecosystems. However,

the rates used in these comparisons were derived for

the whole-reach scale and were expressed on an areal

basis. Therefore, they do not consider N standing

stocks within stream reaches. A more appropriate rate

to compare might be the N-specific uptake rate (or N

turnover rate of the periphyton N pool) expressed in

units of time1

. If we assume that periphyton N in our

study probably ranged from 30 to 60 lg N/cm2 on

NDS, then N-specific uptake from N2 fixation on

control and P treatments in our study was 1.5 3 103

/d

to 3.1 3 103

/d on day 18 and 1.7 3 103/d to 3.4 3

103

/d on day 29. We did not measure total N uptake,

but Dodds et al. (2004) reported that N-specific total N

uptake by primary producers across several streams in

North America ranged from 104

/d to 101/d, a range

that brackets our N2 fixation estimates. Therefore, the

importance of N2 fixation as a source of N to primary

producers and, ultimately, to consumers within the

food web might be more obvious when considered in

terms of stream biomass N.

APA.—We hypothesized that P enrichment would

inactivate APA and that N enrichment would stimulate APA. As in other studies (e.g., Klotz 1992), P

enrichment always decreased APA. N enrichment

stimulated area-normalized APA on day 18 but not

on day 29. N enrichment stimulates APA in phytoplankton (e.g., Rose and Axler 1998) and in stream

epilithon (Klotz 1992). Stimulation of APA by N

enrichment suggests that organisms originally limited

by N become P limited after N enrichment or that N

enrichment enables P-limited organisms to up-regulate

APA. Thus, P deficiency appears to be an important

limitation on periphyton biomass production and

metabolism. The concentration of inorganic P in our

study stream (24.4–29.9 lg P/L) approached concentrations that saturate periphyton biomass accumulation (Bothwell 1989). However, mass transfer of

nutrients from stream water into the periphyton

matrix is the primary rate-limiting step for nutrient

uptake by periphytic algal cells (Larned et al. 2004).

Thus, internal nutrient regeneration within the periphyton matrix could be important in both oligotrophic

and eutrophic streams because the rate at which

nutrients enter the biofilm matrix might not keep up

with demand by cells. This idea is supported by our

data, which suggest that P deficiency existed in control

and N treatments even though streamwater inorganic

P concentration was consistently high.

Periphyton APA values approached 0 only on Penriched NDS. We cannot say what minimum rate of P

supply from NDS was necessary to inhibit periphyton

APA. However, as with N, agar P concentrations can

be modified to manipulate diffusion rates (Rugenski et

al. 2008), and future studies might be able to clarify the

relationships between diffusion rates of P from NDS

up- or down-regulation of APA by periphyton.

Biomass.—We hypothesized that N enrichment, but

not P enrichment, would increase periphyton biomass.

CHLA, a measure of autotrophic biomass (Steinman et

al. 2006), was higher in N than in control or P

treatments on days 18 and 29, but N enrichment had

no effect on AFDM. P enrichment had no effect on

CHLA or AFDM. However, both N2 fixation and APA

were greatly influenced by periphyton biomass.

AFDM and CHLA increased substantially between

days 18 and 29. Area-normalized N2 fixation and APA

were highest (or equal to the highest observed value)

on day 29 when CHLA and AFDM were highest, but

AFDM- and CHLA-normalized N2 fixation and APA

were highest on day 18 when CHLA and AFDM were

relatively low. This pattern suggests that periphyton

was more metabolically active (with regard to N2

fixation and APA) on day 18 than on day 29 and that

the higher area-normalized rates on day 29 were

driven by high periphyton biomass.

We hypothesized that N2 fixation and APA would be

more sensitive than periphyton biomass to differences

in nutrient enrichment. N2 fixation and APA were

strongly influenced by nutrient treatments. CHLA

accrual was stimulated by N enrichment, but not P

enrichment, on days 18 and 29, but AFDM accrual was

not sensitive to N or P enrichment. These results

indicate that autrotrophs must have been limited by N

availability throughout the study. However, the high

rates of APA on control and N treatments suggest that

some part of the periphyton community (autotrophs,

heterotrophs, or both) was P limited. This conclusion is

supported by the lower APA in P than in control or N

treatments. Thus, our results suggest the potential for

differential nutrient limitation among individual autotrophic and, perhaps, heterotrophic taxa in this

periphyton community.

Differential nutrient limitation in periphyton

Our study design did not enable us to distinguish

whether APA was derived from autotrophs or heteroW:/xml/jnabs/jnbs_2801/jnbs-28-01-06.3d Wednesday, 12 November 2008 10:35 am Allen Press, Inc. Page 65

2009] PERIPHYTON N2 FIXATION AND APA 65
trophs. We do not know whether only a subset of

autotrophs, mostly heterotrophs, or a combination of

both were experiencing P deficiency. Different autotrophic taxa within the same community can be

limited by different elements (Dodds 1991, Armitage

et al. 2006), as can autotrophs and heterotrophs within

the same community (Sundareshwar et al. 2003,

Zohary et al. 2005). Differential nutrient limitation of

autotrophs and heterotrophs might maximize resource

use within an ecosystem if groups facilitate resource

delivery to each other, rather than compete for the

same resource (Sundareshwar et al. 2003). The strength

of algal–bacterial coupling in periphyton decreases as

nutrient availability increases (Scott and Doyle 2006,

Scott et al. 2008). This result suggests facilitation of N

and P exchange between trophic levels. Thus, periphyton communities experiencing differential nutrient

limitation between trophic levels might be more

productive than predicted under a single-limitingnutrient scenario. Exchange of resources between

trophic levels in microbial benthos experiencing

differential nutrient limitation could explain how

benthic communities increase ecosystem production

efficiency in nutrient-poor systems and could provide

a mechanism to explain why benthic production is of

primary importance in oligotrophic ecosystems (Vadeboncoeur et al. 2002).

Results of our study suggest that N2 fixation can

provide an important source of N to periphyton

communities, but that P deficiency might still play a

critical role in periphyton community metabolism and

production. Both N2 fixation and APA are sensitive

indicators of microbial nutrient limitation in periphyton. N enrichment increased autotrophic biomass in

our periphyton community. Moreover, although

changes in total periphyton biomass did not generally

reflect a response to added nutrients, N enrichment

inhibited N2 fixation and stimulated APA, and P

enrichment inhibited APA. Therefore, microbial metabolism was related to both N and P availability.

Furthermore, the simultaneous activity of both N2

fixation and APA and their respective responses to

nutrient enrichment suggests the importance of differential nutrient limitation and supports the need for a

multielement approach to understanding nutrient

constraints on ecological communities and the systems

in which they live (sensu Sterner and Elser 2002).

However, more work is needed to determine if

differential nutrient limitation is a general characteristic of aquatic microbial communities and if patterns

observed in nature might be explained by differences

in elemental constraints among microorganisms of the

same or of different trophic positions.

Acknowledgements

We are indebted to Jeff Back, Hui Huang, and Sara

Seagraves for assistance with nutrient analyses. Bryan

Brooks provided valuable feedback on an earlier

version of this manuscript. Financial support was

provided by a Department of Biology Folmar Research

Scholarship and a grant from the US Environmental

Protection Agency (US EPA), cooperative agreement

#CP-966137–01. Although this research has been

funded in part by the US EPA, this paper has not

been subjected to agency peer review and no official

endorsement should be inferred.

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Received: 22 August 2007

Accepted: 15 August 2008

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