### 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 Qual ity 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 disc harge and nutrient concentrations 25 Results: Biological responses to TP gradients 33 Results: Periphyton taxonomic resp onses to TP: ordinations 59 Results: Periphyton taxonomic responses to TP: Threshold Indicator Taxa Analysis (TITAN) 65

Results: Macroinvertebrate ta xonomic responses to TP 78 Section II: Experime ntal 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 al gal biomass response to P dosing 93 Results: Periphyton nutrient content re sponse 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 macroi nvertebrate taxa codes 110 Appendix B: Publications supporte d 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 financ ial support of this rese arch through Section 104(b)(3) Water Quality Cooperative Agreemen t #CP-966137-01. We appreciate the guidance of our EPA Project Managers Sharon Daughe rty, 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 subcont ract 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 “Devel opment of biological i ndicators of nutrient enrichment for application in Texas stream s,” Section106 Water Pollution Control Grant # 98665304. The field portion of this study was coordinated intentionall y so that the 26 field study sites for this project were also chosen as si tes within the study designs implemented by these other two TCEQ sponsored proj ects. These other studies pr ovided 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 de signed, implemented, and managed by RSK and members of his laborator y. Several individuals contributed si gnificantly to the collection of the data for the field component of the project. Je ffrey 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 fiel d portion of this study.

The experimental stream component of the st udy was made possible from a unique partnership with the City of Waco, who provi ded the majority of the financ ial and logistical support for construction of the Baylor Experimental Aquati c Research (BEAR) faci lity. Tom Conry, Nora Schell, John McMillan, Buck Alambar, and David Kerr from the City of Wa co deserve particular acknowledgement for making the BEAR facility a reality. Additiona l funds were contributed to Baylor for construction and personnel by an envi ronmental stewardship grant from Altria Group, Inc. to Ryan S. King (PI), Bryan W. Brooks, and Robert D. Doyle.

The experimental stream component of this st udy required numerous i ndividuals and many hours of labor to accomplish. The experimental stream study was designed, implemented, and managed by RSK and BWB. We thank Barry Fult on, Theodore Valenti, Jason Berninger, and Richard Brain from Bryan Brooks’ lab for l eading the design and implementation of the peristaltic pump dosing system, a nd 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 pr ocessing, and data entry phases of the BEAR study. Jeffrey Back performed all of the nutrien t chemistry analyses during this experiment.

We especially thank Dr. Barbara Winsborough, of Winsborough Conulting, L eander, TX, for her hard work and rapid delivery of algae speci es identifications from both the field and

### R.S. King et al. 2009 4 experimental studies. Dr. Winsborough conducted a ll algal species identifi cations reported in this document.

Study designs, choice of data analyses, presentati on and interpretation of re sults, and conclusions and recommendations are those of the Principal Inve stigator 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 wad eable streams in the Cross Timbers portion of Aggregate Nutrient Ecoregion IX of U. S. EPA Region 6. Our approach re lied on an integration of collected field data from wadeable streams in Texas with experimental data generated from stream mesocosms at the Baylor Experiment al Aquatic Research (BEAR) facility.

Observational Field Study

The observational field component of this st udy 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 dist ributed 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 th an 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 PO 4 

### -P and TP, were more strongly influenced by differences among individual streams than by seas onal 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 dur ing high discharge, reflecting tran sport 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 th e periphyton. Indeed, site s with different levels of surface-water PO 4 

### -P or TP differed markedly in thei r periphyton C:P (carbon-to-phosphorus) and N:P (nitrogen-to-phosphorus) molar ratios. TP consistently predic ted nonlinear, threshold declines in periphyton C:P ratios. Sites with el evated 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 ratio s 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 (di ssolved inorganic nitrog en) 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) gr owing 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 10 0 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 valu e (20 ug/L) usually had a thinne r, darker layer of biofilms on the rocks but also tended to have more filame ntous green algae. There were 2 distinct changepoints in filamentous alga l cover: a consistent increas e in cover among sites >20 ug/L TP, and a less consistent but more dramatic incr ease in cover at very high levels of TP (>200- 1000 ug/L). Because filamentous algal cover de pends highly on seasonal changes in light and water temperature, as well as sloughing from overg rowth and scouring from high flows, it is not surprising that many of the highly enriched sites had little filament ous algal cover on some dates. However, when high filamentous algal cover occu rred, 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 reveal ed a nonlinear shift to ward a greater fraction of chlorophyll-bearing organisms in the peri phyton in response to TP > 20-30 ug/L. A secondary threshold appeared around 200 ug/L, li kely related to the 2-tiered response of filamentous algae and chlorophyl l-a at 20 and 200 ug/L TP.

One of the most important patterns that emer ged 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 stream s 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 measur eable discharge. This lack of flow limited the turbulent mixing of the water column. During the day, stre ams were supersaturated with dissolved oxygen (up 20 mg/L DO; > 250% saturation) which concomita ntly 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 re latively 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 tu rblent 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 dr opped below 3.5 mg/L DO. The four streams located immediately downstream of effluent di scharges maintained re latively 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 ot her 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 be low the stream channel. Here the lack of reaeration by turbulent mixing couple d 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 th an typically considered in monitoring studies.

Minimum DO in 2006 was also predicted significa ntly 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 a nd other types of algae. Thus , these metrics appear to be sensitive to TP enrichment and may be linked quanti tatively to aquatic life use standards that rely on dissolved oxygen as a measur e of biological integrity.

Algal Species Composition Response to TP

Multivariate analyses of al gal species composition during August 2006 and September 2007 continued to support the earlier c onclusions that TP and related metrics had a strong influence on stream biota. In both years, most of the varian ce 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 cha nged 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, a nd the magnitude of its effect was similar regardless of interannual varia tion in stream discharge.

Threshold Indicator Taxa Analysis (TITAN) continued to reveal st ream biological thresholds at low levels of TP. Dozens of algal species esse ntially 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 declin es via competitive exclusion (e.g., shading by Cladophora, a significant increasing taxon in both years).

Although most taxa declined or in creased at relatively low leve ls 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 tie r 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 Indicato r Taxa Analysis (TITAN) on macroinvertebrate taxonomic composition during November 2006 showed that nutrients, specifically phosphorus, corresponded to shifts in community structur e. Because composition differed substantially between sites with and without flow, and because of establishe d relationship between flow and other water chemistry variables (e.g., DO), we sepa rated sites into groups by flow status prior to analysis.

Sites with no flow yielded more reliable relations hips to TP than sites with flow, but this result should be interpreted with caution because ther e 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, wh ereas 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 fr om 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, a nd pool sections and are designed mi mic natural habitat in central Texas streams. Water is pumped from the adja cent Lake Waco Wetland and delivered to the streams via a series of 12 valves, each adjust ed to regulate flow equivalently among all 12 streams (180 L/min). Before entering the str eams, 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 ch emical stock solution. Once dosed with the treatment levels of non-toxic chemi cals, 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 str eam in our field study. Stream flow was initiated on 31 January 2008 and calibrated to 182 L/min (+/- 5 L/min) for each str eam. Streams were seeded with organic matter, periphyton, and macroinvertebrates co llected from two of the intens ive field sites. These sites were chosen because their P concentrations were representative of th e 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-m 2 

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

The streams were allowed to run without any nut rient additions from 31 January to 10 March 2008 in an effort to allow growth of periphyt on at low nutrient concen trations. 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 pl aced in random cross sections throughout the

### R.S. King et al. 2009 9 reach of each experimental stream on 7 and 8 Ma rch, 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 st udy was 6 and increased to 8 ug/L during the experiment. Four streams were randomly assigne d 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 vari ables 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 experime ntal 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 ch lorophyll-a from the bare rocks and transplanted rocks was highly variable among P treatments and did not di ffer 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 Co ntrols on the bare rocks and transplant rocks (ROCK-01, NBOS-03) on Day 28.

Periphyton C:P ratios from non-transp lant bare rock samples differe d significantly by treatment on Day 28. High P treated streams had the lowest C:P ratio (~150), Cont rols had the highest (~320) whereas Low-P streams were intermediate ly enriched (~230). Control C:P ratios were approaching levels deemed to be near or below a C:P threshold in the fi eld 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 periph yton nutrient content.

Periphyton C:P ratios from the ROCK-01 transpla nts (a field site with PO4-P < 5 ug/L and TP<10 ug/L) responded very strongly to all 3 ex perimental 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. Th is 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 declin es in periphyton C:P with small increases in surface-water TP.

### R.S. King et al. 2009 10 In contrast, the NBOS-03 transp lants (a field site with PO 4-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 w ithin the periphyton is an important mechanism for maintaining low C:P ratios over time in streams with highl y variable surface-water TP. Moreover, it also showed that periphyton from NB OS-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 Taxo nomic Response to P Dosing

Transplanted algae from ROCK-01 were signifi cantly 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 wi th a nonlinear threshold response, algal species composition in Low and High P treatments did not di ffer. In contrast, transplanted algae species composition from NBOS-03 did not differ amon g 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 thre shold indicators in response to TP gradients in the field study, validating the res ponses 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 lo ng life cycle of these taxa, necessarily limited the potential response of this diverse group of organi sms 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 th e 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 con centrations of surface-water TP above 20 ug/L. Streams with TP > 200-1000 ug/L likely represent a seco nd 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 th at 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 rate s 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 fo r juvenile fishes and macroinvertebrates, and provide a source of dissolved oxyge n during low flows. Their d ecline likely represents key structural and functional degrada tion to these stream ecosystems.

Minimum dissolved oxygen levels are highly depe ndent upon an interaction between flow and nutrient enrichment. Our study s uggests that TP levels > 20-30 ug/L, coupled with low flows, will cause detrimental declines in minimum di ssolved oxygen levels. This is particularly important in the context of minimu m 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 dur ing periods of low flow when gas exchange with the atmosphere is very slow. Distance downs tream 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 inte raction between minimum flows and biological in tegrity 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 declin e in biological integrity, including loss of characteristic structure (peri phyton and macrophytes), loss of numerous species (algae and macroinvertebrates), minimum dissolved oxygen levels unsuitable for supporting native fauna during low flows, and increase likel ihood of nuisance algal growth th at limits recreational use of strreams. Streams exceeding 200 ug/L may represen t a second tier of de gradation, with more consistent nuisance algal growth and additional losses of algal and macr oinvertebrate species.

### R.S. King et al. 2009 12 Table 1. Key to variable short na mes used throughout this document.

Variable Description

SITE_ID First 4 letters of site name followed by nu mber (>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, spec ifically phosphorus (P) and nitrogen (N), have many negative effects on a quatic ecosystems. One of the most noticeable consequences of nutrient pollution is the accele rated growth of aquatic vegetation. Termed “eutrophication,” this increase in produc tivity caused by nutrient enrichment produces an undesirable disturbance to the balance of orga nisms present in the water. For instance, eutrophication is commonly associated with expl osive growths of nuisance algae that can taint drinking water supplies, cause foul odors, and ev en 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 sign ificantly limit the ability of a water body to support its designated uses, such as fishing, swimming, or other recr eational 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 aqua tic 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 lim iting algal and plant growth in many central Texas streams. TMDL development requires that numeri cal criteria for the parameters of interest be established in order to calculat e necessary load reductions. Howe ver, to date, numerical nutrient criteria have largely been de veloped subjectively and without knowledge of the biological consequences, despite the direc tive issued by the U.S. EPA in its National Strategy for the Development of Regional Nutrient Criteria (1998). In this docu ment, the U.S. EPA detailed a comprehensive plan for the development of scie ntifically defensible, numerical water-quality criteria. The plan emphasized the need for the in clusion of endpoints in cr iteria development that reflect physical, chemical, and biological integrity of aquatic ecosystems. However, quantitative linkages between critical levels of nutrients and stream biota ha ve 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 nutrie nt criteria is the lack of experimental evidence to support field monitorin g. Experimental studies allow for controlled nutrient treatments and, when coupled with fiel d observations, substantially improve our ability to establish causation between water quality and biological integr ity. 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, periphyt on and macroinvertebrates) as i ndicators of bi ological condition in response to observed and experimental phosphor us 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 nui sance 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 categ ories to be degraded at different stressor concentrations along a human dist urbance 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 response s in ecological indicators of aquatic life uses as defined by narrative bi ological criteria in the TALU framework.

The approach we employed is consistent with th e 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 associ ated aquatic life uses, (b ) developed cause-effect linkages between nutrient pollu tion and stream-ecosystem re sponses by reproducing field responses in an experimental setti ng, (c) identified critical levels of nutrient pollution that caused harm to biological integrity and aquatic life us es 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 resu lts 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 colle cted from 26 wadeable streams in the Cross Timbers Ecoregion of the Brazos River watershe d in Texas. These da ta represent one of the most comprehensive evaluations of the effects of nutrient en richment on wadeable streams in the USA. II. Experimental stream study. Part 2 of the re port details the resu lts of a short-term phosphorus dosing study conducted in the Ba ylor Experimental Aquatic Research (BEAR) stream facility.

The Executive Summary and Conclusions and Re commendations 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; Gri ffin et al. 2004). The Cross Timbers ecoregion (Ecoregion 29) is a mosaic of forest, woodland, sa vanna, and prairie and is currently used mostly for rangeland and pastureland (Figure 2, Table 2).

Watershed variables describi ng physical characteristics and topography, land use, and distribution of disturbance point s (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 dig ital elevation model (DEM) expressed as a 30 m raster, available from the U. S. Geological Su rvey. Mean slope and el evation were calculated for each watershed using the digital elevation mo del. Mean annual precipitation was calculated for each watershed from a polygon coverage of aver age monthly and annual precipitation for the climatological period 1961-90. This dataset was obtained from US DA-NRCS. Number of wastewater outfalls and cumulative outfall (MGD) were calculated for each watershed based on the TCEQ municipal and indus trial wastewater outfall shapefile available from http://www.tceq.state.tx.us/gis/sites.html. Landc over class percentages were calculated for each watershed using National Land Cover Da tabase (NLCD 2001) available from http://www.mrlc.gov/nlcd_multizone_map.php. All wa tershed 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 th e 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 [NH 3 

### -N], nitrate-nitrite nitrogen [NO 2 

### -NO 3 

### -N], total nitrogen [TN]), chlorophyll-a, and turbidity were collected from each stream on a quarterly basi s. Sampling periods were adjusted slightly to ensure sampling was conducted at least 2 weeks following storm flows. SRP, NH 3 

### -N, and NO 2 

### - NO 3 

### -N samples were collected in triplicate and f iltered 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. Si ngle 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 laborat ory 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 selecte d 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 ev ents; these are shown as intensive sites. The remaining streams were sampled twice for algal speci es 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. 200920Table 2. Physiographic and land cover characteristics among the 26 field-study watersheds.SITE_IDPrecip.(in)Elev(m)Slope(%)Area(km2)Dams(n)Outfalls(MGD)Outfalls(n)Water(%)Dev.(%)Forest(%)Shrub(%)Grass(%)Pasture(%)Crop(%)Wetland(%)Imp..(%)BLUF-01 33.4 216 2.83 68 0 0.00 0 0.1 0.6 8.8 0.0 69.1 1.4 17.2 2.9 0.0CORY-01 33.0 201 4.78 220 0 0.00 0 0.0 1.5 26.9 5.3 59.2 1.5 3.5 2.0 0.2COWH-01 31.4 232 4.53 1180 2 0.06 1 0.1 0.7 18.8 43.0 33.8 1.6 1.5 0.4 0.1DUFF-01 31.6 297 2.98 157 0 0.00 0 0.2 0.4 22.4 13.1 57.1 1.5 4.1 1.1 0.2HARR-01 34.4 153 1.48 77 0 0.00 0 0.1 12.0 1.6 0.0 42.6 3.0 38.6 2.1 2.0LAMP-01 30.3 331 3.01 720 20 0.00 0 0.3 0.6 10.9 56.6 28.8 1.5 0.9 0.3 0.1LAMP-02 31.1 271 3.08 1571 21 0.00 0 0.2 0.7 14.0 53.6 29.2 1.0 1.1 0.3 0.1LEON-01 30.5 304 3.13 4705 141 3.00 10 0.8 3.1 12.2 33.1 33.3 8.4 8.5 0.8 0.3LEON-02 32.2 226 3.31 6180 152 6.08 12 0.7 2.7 13.5 30.6 37.6 6.8 7.2 0.9 0.3MBOS-01 33.9 161 2.92 478 0 0.09 1 0.1 1.6 10.5 0.0 63.2 2.0 19.9 2.7 0.1MERI-01 32.6 198 5.14 480 4 0.04 1 0.4 0.3 32.3 0.4 63.7 0.5 1.0 1.4 0.2NBOS-01 31.3 366 2.75 257 16 3.50 2 0.6 8.0 11.0 17.6 36.8 12.1 12.0 1.9 1.6NBOS-02 31.0 324 2.94 489 30 3.50 2 0.6 6.5 10.7 20.1 43.9 8.5 7.8 1.9 1.3NBOS-03 31.2 302 3.18 925 49 3.50 2 0.5 4.3 13.3 22.6 44.6 7.1 6.0 1.6 0.8NBOS-04 32.1 220 3.60 1890 69 3.75 4 0.5 3.1 18.4 13.5 55.1 4.1 3.5 1.7 0.6NBOS-05 33.8 154 4.20 3097 82 5.28 8 0.5 2.5 23.8 8.5 56.2 3.4 3.1 2.0 0.5NEIL-01 32.9 177 5.20 357 1 0.00 0 0.2 0.2 35.6 1.4 57.7 0.7 2.2 1.8 0.2NOLC-01 33.6 167 3.27 275 17 33.77 9 0.5 32.4 24.0 7.9 30.2 1.4 1.3 1.6 11.7NOLR-01 33.6 200 2.35 451 2 6.73 5 1.6 12.3 3.2 0.1 65.7 8.8 5.9 2.3 2.5PALU-01 32.0 214 6.06 933 43 0.00 0 0.4 1.1 32.9 11.0 49.8 3.1 0.8 1.0 0.2PLUM-01 31.6 248 3.06 226 0 0.00 0 0.1 0.3 16.8 21.6 53.8 2.3 4.5 0.6 0.1ROCK-01 31.4 258 2.75 221 0 0.00 0 0.1 1.3 24.3 39.9 34.0 0.1 0.1 0.3 0.2SALA-01 33.0 198 1.94 215 0 0.25 1 0.1 2.1 27.9 5.2 59.7 0.6 3.1 0.9 0.4SBOS-01 34.5 144 2.40 220 1 1.10 1 0.2 6.7 3.7 0.0 48.5 4.9 33.9 2.2 0.9SLEO-01 30.1 335 4.75 518 4 0.08 1 0.4 0.5 14.7 52.4 26.8 2.6 2.1 0.5 0.1STEE-01 32.2 210 4.19 127 4 0.00 0 0.8 5.1 30.9 0.0 59.60.6 1.5 1.6 0.5

### R.S. King et al. 2009 21 Diel dissolved oxygen (DO) concentrations were measured from each stream once during TCEQ’s (2005) critical inde x period in 2006 and 2007 using YS I 6600 or 600 XLM Datasondes. One datasonde was deployed in shallow glide hab itats in each stream reach for at least 48 consecutive hours in order to char acterize diel variability in DO, particularly daily minimums, that could be indicative of im portant functional changes caused by nutrients. Diel dissolved oxygen sampling corresponded closely in space a nd 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 aqua tic vegetation growth. The first involved a direct sampling of periphyton occurring on rock (epil ithic) substrate for the analysis and calculation quantitative meas ures of biomass per unit area (chlorophyll- a and AFDM) and algal species composition. Quantitati ve 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 ta rgeted sampling approach (riffle or glide habitat with gravel, cobble, or be drock). In brief, 5 separate ro ck 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 a nd corresponding ratios of these three elements as indicators of nutrient availability and enrich ment. Methods are detailed in Back et al. (2008) and Scott et al. (2008).

The second approach utilized the modified vers ion 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. Th e method entailed walking a zigzag transect from one end of the reach to the ot her 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 substrat e (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 co llected, homogenized, preserved, and identified in accordance with taxonomic met hods 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 with in the reach (n=20). Samples were composited, homogenized, and preserved in 5% (v/v) buffere d formalin stained with rose bengal.

A two-phase macroinvertebrate subsampli ng approach (King and Richardson 2002) was employed in the laboratory. The first phase wa s 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 met hod) 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 individu als (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/m 2 

### ; King and Richardson 2002).

Macroinvertebrates were identified in the laboratory using operational taxonomic units, usually genus or species for most taxa. A voucher collec tion of specimens is maintained in the Center for Reservoir and Aquatic Systems Research (CRASR).

Physical habitat, flow, substrate, riparian at tributes, 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 TC EQ (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 th e measurement endpoints to numerical levels of nutrients using nonparametric chan gepoint analysis (nCPA), a tech nique explicitly designed for detecting threshold responses using ecologica l data (King and Richardson 2003, Qian et al. 2003). This analysis is based on the fact that st ructural change in an ecosystem may result in a change in both the mean and the variance of an ecological response variab le used to indicate a threshold. When observations are ordered along an environm ental variable (gradient), a changepoint is a value that separa tes 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 poin t 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 ca se, the selected biologi cal 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 cha ngepoint. In reality, depending on the acuteness of the biological change in response to TP, several observations of TP could represent th e changepoint, each with varying probabilities. Thus, to assess the risk associated with particul ar levels of TP, nCPA

### R.S. King et al. 2009 23 incorporates estimates of uncertainty in th e 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. Th e result is a distributio n 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 eac h changepoint value in the distribution.

Important gradients in algal and macroinverteb rate species compostion were identified using non-metric multidimensional scaling (nMDS). NMDS is a distance based procedure that ordinates study units based on rank dissimilar ities (Minchin 1987, Cl arke 1993, Legendre and Legendre 1998). We used Bray-Curtis dissimilarity (BCD) as the distance measure, a coefficient that has been repeatedly demonstrated to be ro bust 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, alg ae or macroinvertebrate species o ccurring at only two sites within a data set were excluded, and abundances were log transformed. Variables from the watersheds and environmental measurements with high skew ness (> 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 envi ronmental and watershed variables to gradients in algal and macroinvertebrate community composit ion quantified by the NMS ordinations (Faith and Norris 1989). Vector fitting was used to fi nd the direction of the maximum correlation for each environmental variable. Significance ( P ≤ 0.05) of each environmental vectors was estimated using 1,000 random permutat ions of the data. Vector f itting was performed using the ECODIST package in R version 2.5.1 (© 2007, Th e R Foundation for Statistical Computing).

For species abundance thresholds, we employed a new analytical approac h, Threshold Indicator Taxa ANalysis (TITAN; Matthew E. Baker and Ryan S. King), with the goa ls of (1) exploring and identifying abrupt changes in both the occurence frequency and relative abundance of individual taxa along nutrient gr adients, (2) quantifying uncerta inty associated with both observed distributions of each taxon and the broa der 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 a nd detecting community ecological thresholds are not developed for distinguishing responses of i ndividual taxa with low occurrence frequencies or highly variable abundances (Dufrêne and Lege ndre 1997, Brenden et al. 2008, Andersen et al. 2008). Some methods assume a linear, univariate re sponse along all or part of an environmental gradient (e.g., Toms and Lesp erance 2003), whereas others fo cus solely on aggregate, community-level dissimilarity (e.g., De’Ath 2002, Ki ng et al. 2005) or speci es turnover between samples (i.e., beta-diversity). Noisy, non-linear, and poorly distri buted 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, especi ally 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. Ne gatively 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 variab les identified from the environmental vector fitting an alysis. TITAN was conducted in R version 2.5.1 (© 2007, The R Foundation for Statistical Computi ng) using the custom package TITAN written by M. E. Baker and R. S. King (Baker and King, accepted with revision; King and Bake r, 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, a nd 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 conclu sions about the effect of the predictor (e.g., TP) on bi ological responses because other fa ctors 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 spa tial problems, capturing th e full range of natural variability in geology, drainage ne tworks, 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 to tal phosphorus (TP, ug/L) and periphyton carbon-tophosphorus (C:P, bulk) ratio during the fi rst quarterly sampling event, August 2006.

### R.S. King et al. 2009 26 Streams throughout the study area had similar di scharge patterns during the study period (Figure 4). Differences in discharge were much gr eater 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 ceas e 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 le d 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 stream s were unwadeable so some variables were not sampled at those sites on that date. More rain and flooding con tinued through August.

Streams finally began to recede in September 2007 ( #6 in Figure 4), allowing us to reinstate our quarterly sampling schedule. However, ba seflows 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 ev ents 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 st reams spanning a period before, during, and after the field study (data acquired from www.usgs.gov). Th e gray-shaded bars represent the sampling window corresponding to each of the 8 quarterly samp ling 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 comprise d predominantly of calcareous gravel, cobble, boulders, and bedrock (e.g, Figure 5). Streams had short reaches of erosio nal (riffle) habitat comprised mostly of larger substrate (gravel a nd cobble). Erosional areas were interspersed by typically longer sections of eith er 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 reveal s 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. Nutr ient-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 measurem ents revealed that the hydro logy of the 26 study streams was quite similar over time (Figure 6). All streams except for the 4 sites immediately downstream of significant effluent discharg es (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 a nd 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 consisten tly 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 dur ing high discharge, reflecting tran sport 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 dilu ted by high surface-water runoff (LEON-02, NOLR01, SLEO-01).

### R.S. King et al. 2009 29 Figure 7. Temporal patterns of surface water ort hophosphate (PO4-P, ug/L) and dissolved inorganic nitrogen (DIN) among the 26 field sites, August 2006-J une 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 e ffluent discharges ( NOLC-01, NOLR-01, NBOS-01, LEON-01, SLEO-01), as most of the TP at th ese 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, LE ON-01, etc). Orthophos phate represented a very small fraction of the TP at the sites wi th the fewest sources of phosphorus, and was often at or near the minimum dete ctable limit of 1-2 ug/L.

Dissolved inorganic nitrogen beha ved much differently than PO 4-P over time. During winter and spring of 2007, sites with rowcrop in their watersheds exhibited ve ry high fluxes of DIN (mostly NO3-N). These sites tended to have consistently high DIN all year, but high concentrations were particularly evident as disso lved forms of N were transported from fields (mostly corn) via ground and surface water r unoff to the streams (e.g., BLUF-01, CORY-01, HARR-01, MBOS-01, SBOS-01 NBOS-05). These hi gh-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 co incident 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 a dditional 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 en richment of surface-water nutrie nts that were important would be reflected in enrichment of the tissues of the periphyton. Site s 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 th eir 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 th e periphyton = lower C: P or N:P ratio).

What is particularly compelling about these result s is that sites with elevated surface-water TP had virtually constant C: P or N:P ratios in the periphyton. Mo reover, 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) ve rsus 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 dire ctly the strong influence of surface-water TP on periphyton P content. There appear to be 3 type s 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 periphyt on C:P ratios. Importantly, these ratios almost always remained above 500. This showed that the periphyton was strongly limited by P, and variab ility in its P content was li kely due to subtle seasonal differences in availability (e ither via recycling or pulses of low enrichment), taxonomic structure, and/or accumu lation 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 s ubtle 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 le vels of TP (e.g., effluent disc harge 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 si tes with consistently mo derate levels of TP. This suggested that the periphyton was P-satura ted (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) peri phyton was strongly limited by P, (2) it was very efficient at sequestering and recycling P, and (3) its C:P ra tios 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 qua ntification of levels of surface-water TP that result in thresholds, or non linear changes, in periphyton nutr ient content and the implications of this enrichment on other bi ological 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 th e strikingly strong nonlinear relationship between surface-water TP and periphyt on nutrient content. These figures use all of the data combined to show the remarkable consis tency 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 ev ident 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 film s (microalgae and other microbes; non-filamentous algae) growing on gravel, cobble, boulde rs, or bedrock showed consis tently 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 thic kness 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 peri phyton was a consistent tr ait 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 th e reach-scale transect that ha d 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 consiste nt increase in cover among sites at about 20 ug/L TP, and a more diffuse but dramatic increase in cover at ve ry 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 co ver 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 acros s all 26 sites over the full study period. Bulk represents ratios of carbon, nitroge n, and phosphorus that were estim ated using homogenized periphyton that was not subjected to the centrifugation separati on. The OM (organic matter) ratio represents the fraction of homogenized periphyton that was sepa rated from inorganic sediment using the centrifugation method described in Scott et al. (2008). Each blue do t represents one of the 26 sites, and each site is represented 8 times in the analysis, once for each sampli ng date. The gray vertical line is the observed TP threshold (the level of TP resulting in the greatest diff erence 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 th e data (see King and Richardson 2003). The cumulative threshold frequency illustrates the range of possible threshold va lues; 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:N (bulk) C:P (bulk)

N:P (bulk) Surface

‐ water Total Phosphorus vs. Periphyton Nutrient Content (2006

‐ 2008)

### R.S. King et al. 2009 35 One of the most important patterns that emer ged from the field study was the sharp threshold declines in submersed macrophyte cover in resp onse to TP >25-50 ug/L. The percentage of points along the reach-scale transect that had e ither some cover of macrophytes (top right panel, Figure 11) or cover exceeding 25% of the substrate (bottom right pa nel) 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 functiona lly 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, Richar dson 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 impor tant structural compone nt of these streams.

Figure 11. Results from nonparametric changepoint an alysis using surface-water TP as a predictor of threshold changes in biofilm (non-filamentous periph yton) 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 (b iofilm) color and thickness from sites below the TP threshold of <15-20 ug/L (panel A) and above the th reshold (panel B). In panel A, the periphyton is mostly diatoms, calcareous cyanobacteria, and othe r 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 structur e 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 pl aces the results presented in Figure 13 into context and aids in their inte rpretation. Periphyton ash-free dr y mass was highly variable in response to TP, and showed a weak decline in re sponse to TP levels ranging from as low as 10 ug/L to about 50 ug/L. Because total ash-fr ee 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 signifi cant decline in AFDM, although the threshold level of TP is relatively uncertain.

Periphyton chlorophyll-a tended to track the patte rn 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 noi sy because diatoms and cyanobacteria characteristic of lo w-P sites also contain chlorophyll.

Figure 13. Results from nonparametric changepoint an alysis using surface-water TP as a predictor of threshold changes in periphyton ash-free dry mass (AFDM, g/m 2 ), periphyton chlorophyll-a (ChlA (peri); mg/m 2 ), 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-bear ing organisms in the periphyton in response to TP > 20-30 ug/L. A secondary thres hold appears to be near 200 ug/L, coincident with the 2-tiered response of fila mentous 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 su rface-water chlorophyll-a at 20 ug/L TP. Streams with high TP did not consistently have high chlo rophyll-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 mu ch 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 stream s may be an important contributor of non-point source phosphorus (Figure 14): C:P ratios in sedime nt 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 partiall y caused by P-enriched organic ma tter (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-i mpacted 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. Ho wever, many of the streams with TP>20 ug/L did not have obvious sedimentation problems, thus se dimentation 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 bi ological responses.

The frequency of sediment cover classified as sufficiently heavy to obscure the color of periphyton (Sed>3) corresponded closely to the per centage of the reach dominated by fine siltclay substrate. It also showed a sharp increase at 20 ug/L TP. This partic ular 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 response s of silt cover and the sediment index, sharply increasing at 20 ug/L TP. TP is partially de pendent 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 an alysis using surface-water TP as a predictor of threshold changes in (1) periphyton C:P ratio (sedimen t 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=sufficien tly 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 comb ined, 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 re sponse 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 changepoi nts at ~20 and ~200 ug/L TP.

Table 3. Results of nonparametr ic changepoint analysis using surface-water TP as a predictor of threshold responses in selected biological and wate r quality response variables across 26 sites and 8 quarterly sampling events during 2006-2008. See Ta ble 1 for full names and descriptions corresponding to variable IDs, and figures 10 through 14 fo r 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 predic tor of periphyton nutrient content. The relationships depicted in Figure 15 are very similar to the TP th resholds 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 relativ e to TP as a predictor, howev er, 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 lo w C:P ratios. TP is le ss variable and thus is a better indicator of P enrichment, but is still in fluenced 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 p hosphorus that were estimated using homogenized periphyton that was not subjected to the centrif ugation 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

‐ water Orthophosphate (PO 4 

### ‐ P) vs.

Periphyton Nutrient Content (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 e ffluent discharges (i.e., high P and N, but the periphyton was resp onding to the P), and the noise in the relationship is attributed to sites with high amounts of rowcrop in their waters heds but little pasture or effluent discharges (low P but high N, thus no response of the pe riphyton 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 pred ictor 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

‐ water Dissolved Inorganic N (DIN) and Total N (TN) vs. Periphyton Nutrient Content (2006

‐ 2008)

### R.S. King et al. 2009 43 Table 4. Results of nonparametr ic changepoint analysis using surface-water PO4-P, DIN, TN, and periphyton C:P ratio (bulk) as a predictors of threshold res ponses 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. S ee Table 1 for full names and descriptions corresponding to variable IDs.

Cumulative Threshold Quantiles Predictor Variable Response Obs. P value 10% 50% 90%
PO 4 -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 predic tors of threshold changes in each of the biological responses listed in Ta ble 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 id entify 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 consiste ntly predicted the same th reshold responses in other variables that were predicted by TP, a nd 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 we re inconsistent responders to P enrichment, but usually this could be explained by seasonal patterns in light and/or discha rge (scouring). The consistency of the direction of respons e (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 repor ted thresholds will not always exhibit these responses (due to seasonal or hydrological limiting factors).

• Some metrics responded consistently among y ears within a particular season. In particular, MICALG3+ (% of reach with biofilm thickness > 1 mm) significantly declined only in the summer months (A ug 2006, Sep 2007, and June 2008), but this is perhaps the most critical tim e 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 nonparametr ic 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 nonparametr ic 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 nonparamate ric 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 dur ing 2006-2008. TP thres holds (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 nonparamate ric 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 dur ing 2006-2008. Periphyton C:P thresholds ( Obs. ) and bootstrap quantiles are shown for each response vari able and date combination. Direction (increase or decrease) of the response with increasing periphyt on 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 strong ly influenced the daily (diel) variation in dissolved oxygen, water temperature, and pH (Figure 19).

Figure 19. Patterns of dissolved oxygen, water temp erature, 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 influen ce of stream flow on diel water chemistry. In 2006, a drought year, most of the field-study stream s had very low-to-no measureable discharge. This lack of flow limited the turbulent mixing of the water co lumn. Without turbulence, gas exchange between air and wate r 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 st reams 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, reach ing 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 precedi ng months, and had just recently subsided to wadeable flows. Even under these higher fl ows and turbulent mixing conditions, dissolved oxygen showed clear diel variati on, peaking around 16:00 at concen trations up to 12 mg/L (120- 150% saturation), and dec lining during the night to typically 6-8 mg/L DO. However, minimum DO levels were rarely below 5 mg/L, and no st ream had minimum DO le vels that would be considered detrimental to native fishes or macroi nvertebrates in this region, especially in moving
water.

Figure 20. Results of nonparametric changepoint anal ysis 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 stream s during this sampling event. 

### Minimum dissolved oxygen, 48 hr (mg/L), September 2006 Symbols scaled 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 (Figur e 20). All 6 streams with no detectable flow and TP > 27.2 had minimum DO values < 2 mg/L, a nd 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 concentrat ions despite very high TP. This was related to reaeration associated with turbulent mixing. Despite these outliers, TP was nevertheless a significant predictor of threshold d eclines in minimum DO (Table 7).

The diel pattern of DO at NBOS-02 (North Bos que River - 02) is further evidence that WWTP discharge sites maintained relatively high mini mum DO because of turbulent mixing associated with flow. NBOS-02 is located a few kilomete rs downstream of NBOS- 01, one of the 4 WWTP discharge sites depicted in Fi gure 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 enrich ment stimulated photosynthesis during the day whereas microbial respiration, also fueled by P-ri ch 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 previous ly shown, thicker layers of calcareous periphyton comprised mostly of diat oms, cyanobacteria, and other microbes were associated with low-P streams, and as TP incr eased, 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 stan dards that rely on dissolved oxygen as a measure of biological integrity.

These flow-dependent results imply that studi es on the effects of nutrients on DO will not adequately characterize risk to biota and asso ciated 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 ex cluded from the analysis because these were the only flowing streams during this sampling event. 

## Minimum dissolved oxygen, 48 hr (mg/L), September 2006

vs. surface

‐ water TP, filamentous algae, and periphyton

### R.S. King et al. 2009 58 Table 7. Results from nonparametric changepoint anal ysis 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 fro m 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/cm 2 ) was log 10 (x+1) transformed prior to analysis. Bray-Curtis distance was used as the dissi milarity 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 directio n 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: nutrien ts (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, filamentous macroalgae 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 al so significant predictors in these ordinations. See Figure 22 for details. Increasing P enrichment, sedimentation, periphyton chlorophyll Decreasing periphyton C:P, C:N ratios, biofilm thicknessDecreasingperiphyton 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 areaIncreasingpasture,andreservoirsurfacearea

### 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 c onclusions 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 a nd highest periphyton C:P ra tios were placed at the low end of axis 1 whereas sites with the highest TP and lowest C:P ra tios were at the opposite end of axis 1. Figure 24 is particularly intere sting because it showed th at 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 com position was altered by nutrient enrichment, and the magnitude of its effect was similar regardless of inte rannual 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 NBOS363 NBOS463 NBOS563

NEIL163

NOLC163 NOLR163 PALU163 PLUM163

ROCK163 SALA163 SBOS163

SLEO163 STEE163

BLUF173 CORY173

COWH173 DUFF173

HARR173 LAMP173 LAMP273

LEON173

LEON273 MBOS173

MERI173 NBOS173 NBOS273

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 Augus t 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 s ites 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 ax is 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 appr oximate position along axis 1 (the nutrient axis) over time. ROCK-01, COWH-01, a nd 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 similari ty is to be expected.

PALU-01 remained consistently intermediate alo ng Axis 1, distinctly separated from the low-P sites during the first few sampling events, but ov erlapping with COWH-01 near the end of the study. NBOS-03 and LEON-02, both highly enriched si tes, consistently st ayed 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 coinci dent with a slight shift to the left in algal species composition, possibly indicati ng 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: Thre shold 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 le vels 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 shar p increases, either replacing taxa as they declined, or driving the declines via competitive exclusion (e.g., shading by Cladophora =CLAglome, a significant increasing ta xon in both years; Table 10).

Although most taxa declined or in creased at relatively low leve ls 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 th e protracted period of flooding prior to September 2007, many of the streams were in a period of rapid recoloniza tion (community succession). Scouring of rocks reduced periphyton biomass substantially compared to 2006. It is likely that part of the explanation was that fast-respondi ng “weedy” algal taxa proliferat ed 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 200 6 and 2007 was that surface-water TP was not as strong of an indicator of P-en richment status during higher and more variable flows. There is some support for this hypothesis given that mo re 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 availabi lity (via recycling, sedi ment-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+) thr eshold indicators based on the direction of response to TP. The upper panels show the ob served TP threshold value (colored sy mbols) 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 cumulativ e threshold frequency for negative (green) and positive (red) responses, respectively. See Tables 8 and 9 fo r 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 peri phyton nutrient concentrations during August 2006 (drought, n=26 sites). Thresholds ( Obs. ) are based on the value of the predic tor 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 repres ent measures of uncertainty around the observed threshold. *Note that lower C:P values = higher P enri chment 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 peri phyton 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 abunda nce 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 ANa lysis (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 ar e negative indicators (z-) in these results are associated with higher levels of P-enrichment,, whereas positive thre shold 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 ANa lysis (TITAN) using periphyton C:P ratio (organic fraction, sediment removed) as a predictor of threshol d changes in individual algal species during August 2006 and September 2007. Taxa are classified as eith er 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 highe r 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 (s um(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 en richment 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,, wh ereas “increaser” taxa are associat ed 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 TBfasci c 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 signi ficant 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 in creasing C:P are associated with higher levels of P-enrichment,, whereas “increas er” 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 Indicato r Taxa Analysis (TITAN) on macroinvertebrate taxonomic composition during November 2006 showed that nutrients, specifically phosphorus, corresponded to shifts in community structur e (Figure 30, 31). Because composition differed substantially between sites with and without flow, and becaus e of established relationship between flow and other water chemistry variab les (e.g., DO), we separated sites into groups by flow status prior to analysis.

Sites with no flow yielded more reliable relations hips to TP than sites with flow, but this result should be interpreted with the caveat that ther e 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 negativ e 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 in fluence of nutrient enrichment on macroinvertebrates. Many of the sites had gone dry or nearly so between A ugust and October 2006, so some of the sites with flow during this event had been reduced to ti ny 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 mo re completely evaluate macroinvertebrate responses to TP enrichment.

### R.S. King et al. 2009 79 Figure 30. Nonmetric multidimensional scaling or dination of macroinvertebrate species composition during a period of drought in November 2006. Di ssimilarity (Bray-Curtis) was computed using logtransformed densities (no./m2) per taxon. Ordinations were conducted separately fo r 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 greates t 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 Thre shold 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 samp ling (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 Ta ble 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 fo r the development of scientifically defensible water quality standard s, yet most studies ar e limited only to field observations from natural lakes, wetlands, and st reams because these habitats are too large and complex to manipulate experimentally. The in tention of the Baylor Experimental Aquatic Research (BEAR) facility in Waco, TX, is to br idge 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 variable s yet are too small and unrepresen tative of natural conditions to be realistic. Because of its si ze (over 30,000 square feet), out door 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 re search studies (Figure 32).

The BEAR facility supports 12 e xperimental 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 regul ate 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 dos ed 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 in to the streams and is di scharged 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, a nd pool sections and are designed mi mic 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 adaptati ons 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 redu ce variability of sunlight ac ross experimental units.

### R.S. King et al. 2009 86 Figure 32. Aerial view of the Lake Waco Wetland an d the location of the Baylor Experimental Aquatic Research (BEAR) stream facility (red arrow, upper pane l). 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/aqua ticlab/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, peri phyton, and macroinvertebr ates 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 re duced probability of introducing taxa that proliferate under high P conditions.

Streams were seeded twice: 1 February and 15 Fe bruary 2008. The first event was to primarily introduce algae, bacteria, f ungi, and detritus, as we anticipated high mortality of macroinvertebrates due to the lack of food in the streams at the begi nning of the colonization period. The second seeding was done to facilitate esta blishment of macroi nvertebrates found in both low and high P streams in the region.

Seeding was accomplished by collecting five 1-m 2 

### kick screen samples from ROCK-01 and NBOS-03 for each of the 12 expe rimental 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 du ring 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 nut rient 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 nut rients 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-0 1 and NBOS-03 were placed in random cross sections throughout the r each of each experimental stream on 7 and 8 March 2008, respectively, a few days pr ior 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 relativ ely 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 change s in numerous biological response variables between 10 and 20 ug/L PO4-P (usu ally corresponding to 15-25 ug/L TP), we selected a target PO4-P concentration of 20 ug/L for our lower nomi nal 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 receive d +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 enri chment that was correlated with significant biological changes in the field study.

Streams were dosed with NaH 2 

### PO 4 

### 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 illu strating the layout of the stream facility (top left), a view from within a glide section of a stream channe l (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 st reams. 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, di el dissolved oxygen was not monitored in this study.

Biotic sampling was stratified into riffle, and g lide habitats. Each section was partitioned into approximately forty 20-cm wide cross-sections. Each cross section wa s assigned a number for random sampling location for biota sampling.

Quantitative periphyton samples from rocks that we re exclusively colonized in the experimental streams (“bare rock”, non-tran splant periphyton) were collect ed on Day 0 (day before dosing commenced) and Day 28 (4 weeks of dosing) ofthe 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 acco rdance 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 transpla nted cobbles (ROCK-01 and NBOS-03) were processed on Day -2 and -1 (two and one days pr ior to dosing, the same da y rocks were collected from the field streams) to characterize the ba seline AFDM, chlorphyll a, and CNP content prior to transplanting. Four separate composite sample s were collected from each site (four sets of 5 rocks). Five transplanted cobbles per field stre am 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 compos ition was measured from samples from Day -2/-1 (hereafter, Day 0) and Day 28.

An additional set of quantitative periphyton sample s was collected from 5 x 5 cm ceramic tiles. Clean tiles were placed in th e 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 cro ss-sections were sampled on the same dates. Samples were collected with a custom-made quan titative sampler developed specifically for the BEAR facility. The sampler, called a BEAR Tr ap, is functionally simila r 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 m 2 

### ). Four quantitative samples we re collected per stream per riffle and run sections per date and were proces sed 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 transformatio ns were applied when necessary. Multiple comparison tests were done when ANOVA deemed the main effect si gnificant (p<0.05).

Algal and macroinvertebrate species com position were analyzed using nonmetric multidimensional scaling ordination (see Section I) and analysis of successional vectors. Tests for diffrerences in treatments were done usi ng Multi-Response Permutation Procedure (MRPP). Indicator Species Analysis was c onducted to identify taxa that ch anged 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 amon g the Experimental Streams

Experimental PO4-P additions yielded concentrati ons 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 nut rients in the wetland during the study period. The average PO4-P for the Low P treatment ranged fr om 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 ta rget 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 dosi ng 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 Febr uary – 10 March 2008) and dosing (11 March – 7 April 2008) phases of the experiment. The magent a points correspond to the samples collected from the outlet of each stream, whereas the blue points are sa mples 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 concen trations 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 PO 4 -P DIN NH 3 -N NO 2 NO 3 - 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 ra pidly to experimental P dosing (Figure 35). There were no differences in treatments on Day 0 (Figure 36). High P treatments had significantly more chlorophy ll 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 ch lorophyll-a than Controls (Figure 35, 36). The lack of difference between low and high P treatmen ts is consistent with the threshold response observed in the field study.

Figure 35. Comparison of periphyton growing on cer amic 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 chlo rophyll-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 HighChlorophyll-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 PChlorophyll-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 HighChlorophyll-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 transplant ed rocks was highly variable among P treatments and did not differ signi ficantly. 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 Co ntrols 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 indistingui shable, 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/cm 2 ) among P treatments on Day 28. Means with the same letter are not different (p>0.05). Phosphorus treatment Control P Low P High PLog 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 nutri ent 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 PLog Chlorophyll-a(μg cm-2)

0.0 0.5 1.0 1.5 2.0 2.5

Phosphorus treatment Control P Low P High PLog 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 Resp onse to Experimental P Dosing

Periphyton C:P, C:N, and N:P ratio s from non-transplant bare ro ck samples did not differ among streams on Day 0, but differed significantly by treat ment on Day 28 (Figure 41). High-P treated streams had the lowest C:P ratio (~150), Contro l streams had the highest (~320), whereas Low-P streams were intermediate (~230). Even the cont rol C:P ratios were approaching levels deemed to be near or below a C:P threshold in th e field study, suggesting th at the background PO4-P concentrations were high enough to allow peri phyton to sequester much higher amounts of P than typical of the lowe st nutrient field sites.

Periphyton C:P ratios from the ROCK-01 transpla nt rocks responded very strongly to all 3 experimental treatments (Figure 42). On Day 0, ROCK-01 periphyt on had C:P ratios above 2,000. After transplanting these ro cks 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 ill ustrated the remarkable affinity of stream periphyton for phosphorus, and provid ed additional evidence in s upport of nonlinear uptake of P as the explanation for sharp dec lines in periphyton C:P with sma ll increases in surface-water TP.

In contrast to the ROCK-01 transplants, the NB OS-03 transplants did not respond to any of the experimental P treatments on Day 7 or 28 (F igure 42, 43), supporting the hypothesis that recycling of P within the peri phyton 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 alr eady 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) am ong 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 H i g h PPeriphytonC:Pratio(bulk) A

B

C 

## ANOVA, F=25.6, p=0.0001

0 50 100 150 200 250 300 350

Control Low P H i g h PCladophoraC:Pratio 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 pa nel) and NBOS-03 (bottom pane l) 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 lette r are not significan tly different. 0 500 1000 1500 2000 2500 3000

ROCK

‐ 01 Control Low P High PPeriphytonC:Pratio(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 PPeriphytonC:Pratio(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 lette r are not significan tly different. 0 50 100 150 200 250 300

Control Low P H i g h PPeriphytonC:Pratio(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 H i g h PPeriphytonC:Pratio(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 Cont rol, 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 compos ition 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 re sponse, algal species compositi on 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 RO CK-01 transplants from Day 28 reve aled 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 indicato rs of the Low/High P treatments (Table 17). Five of these 7 taxa were identi fied as significant threshold indicat ors in response to TP gradients in the field study. All five responded in the same direction (decline, increas e) in the BEAR study as in the field study.R.S. King et al. 2009103Figure 44. Nonmetric multidimensional scaling ordination of algal species composition from ROCK-01 and NBOS-03 transplantrocks on Day 0 and Day 28. Algal species composition from the 6intensive field sites during February (before) and June 2008 (after)are also ordinated with the BEAR samples fora frame of reference. Sites sorted fromlow P sites (left end of Axis 1) to highP sites(right end of Axis 1). All ROCK-01 samples shifted toward NBOS-03 samples after28 days of exposure in the BEAR facility, butLow and High P treated rocks shifted fartheraway from the natural stream and were significantly different than Controls. In contrast,NBOS-03 rocks shifted in the same direction as the June 2008 NBOS-03 sample, and algal species composition did not differ amongtreatments 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 specie s composition growing on ROCK-01 transplanted cobbles at Day 28 of the BEAR experimental st ream study. Only taxa with significant IndVal scores are shown below. Taxa th at showed significant de clines 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 Respon se to Experimental P Dosing

Macroinvertebrate taxonomic composition did no t 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 shor t 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 interactio ns 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 BE AR facility, Day 0 and Day 28. Arrows indicate the direction and magnitude of community chan ge within each stream between Day 0 and 28. Ellipses are drawn to envelop all 4 streams by tr eatment on Day 28. There was extensive overlap among treatments, and community compos ition did not differ (MRPP, p=0.13). 

## Ordination of macroinvertebrate 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 degr adation, and appear at greater risk for nuisance algal growth based on our threshold analyses. However, results from the P dosing experiment suggest that concentrat ions 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 fiel d streams with very high levels of TP (>200 ug/L) had greater frequency of hea vy growth of filamentous algae.

Aquatic macrophyte cover consistently declined in streams with TP > 25-50 ug/L. These submersed plants serve as important refugia fo r juvenile fishes and macroinvertebrates, and provide a source of dissolved oxyge n during low flows. Their decl ine likely represents a key structural and functional change in these stream ecosystems.

Minimum dissolved oxygen levels are highly depe ndent upon an interaction between flow and nutrient enrichment. Our study s uggests that TP levels > 20-30 ug/L, coupled with low flows, will cause detrimental declines in minimum di ssolved oxygen levels. This is particularly important in the context of minimu m 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 duri ng periods of low flow when gas exchange is with the atmosphere is very slow. Distance downs tream 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 inte raction between minimum flows and biological in tegrity in streams.

Based on the weight of evidence from the coupli ng of the field stream study and experimental stream study suggests that streams of the study ar ea are very sensitive to phosphorus enrichment. There is a very high probability that streams e xposed to surface-water TP levels exceeding 20 ug/L, and possibly 15 ug/L, will experience a shar p decline in biological integrity, including loss of characteristic structure (periphyton and macr ophytes), loss of numerous species (algae and macroinvertebrates), minimum dissolved oxygen levels unsuitable for supporting native fauna during low flows, and increase likel ihood of nuisance algal growth th at limits recreational use of strreams. Streams exceeding 200 ug/L may represen t a second tier of de gradation, with more consistent nuisance algal growth and additional losses of algal and macr oinvertebrate species.

### R.S. King et al. 2009 107 LITERATURE CITED

American Public Health Association, et al. St andard Methods for the Examination of Water and Wastewater. American Public Health Associ ation, American Water Works Association, and Water Environment Federation. 20th edition, 1998.Washington, D.C. American Society for Testing and Materials In ternational. Annual Book of Standards, Vol 11.02. May 2004. West Conshohocken, PA Anderson T., J. Castensen, E. Hernandez-Garcia , C. M. DUarte. 2008. Ecological thresholds and regime shifts: approaches to identification. Trends in Ecol ogy and Evolution (In Press). Back, J. A.*, J. M. Taylor*, R. S. King, E. Hintzen*, and K. Fallert*. 2008. Ontogenetic differences in mayfly stoichiometry influe nce growth rates in response to phosphorus. Fundamental and Applied Limnology (A rchiv fur Hydrobiologie) 171:233-240. Baker, M. E., and R. S. King. In revision. Threshold Indicator Taxa Analysis (TITAN): A method for identifying and interpreting ecological community thresholds. Barbour, M. T., J. Gerritsen, B. D. Snyder, a nd J. B. Stribling. 1999. Rapid bioassessment protocols for use in streams and wadeable rivers: periphyt on, benthic macroinvertebrates, and fish. EPA 841-B-99-002. US Environmen tal Protection Agency, Office of Water, Washington, DC, USA. Belanger, S. E. 1997. Literature review and anal ysis of biological complexity in model stream ecosystems: influence of size and ex perimental design. Ecotoxicology and environmental safety 36:1-16. Breiman, L., J. H. Friedman, R. A. Olshen, C. J. Stone. 1984. Classificati on and regression trees. Wadsworth Int. Brenden, T.O, L. Wang, and Z.Su. 2008. Quantitati ve identification of dist urbance thresholds in support of aquatic resource management. Environmental Management 42:821-832. Brooks BW, Stanley JK, White JC, Turner PK, Wu KB & TW La Point. 2004. Laboratory and field responses to cadmium in effluent-dom inated stream mesocosms. Environmental Toxicology & Chemistry 24: 464-469. Clarke, K. R. 1993. Nonparametric Multivaria te Analyses of Changes in Community Structure.Aust. J. Ecol. 18: 117-143 De’Ath, G. 2002. Multivariate regression trees: a new technique for modeling speciesenvironment relationshi ps. Ecology 83:1105-1117. De’Ath, G. and K.E. Fabricius. 2000. Classifica tion and regression trees: A powerful yet simple technique for ecological data analysis. Ecology 81:3178-3192. Dufrêne, M., and P. Legendre. 1997. Species asse mblages and indicator species: the need for a flexible asymmetrical approach. Ecological Monogr aphs 67:345-366. Dyer, S. D., and S. E. Belanger. 1999. Determin ation of the sensitivity of macroinvertebrates in stream mesocosms through field-derived a ssessments. Environmental Toxicology and Chemistry 18:2903-2907. EPA (United States Environmental Protec tion Agency). 2000. Multi-resolution land characteristics consortium (MRLC) da tabase. (http://www.epa.gov/mrlcpage) Faith, D.P. & R.H. Norris. 1989. Correlation of environmental variables with patterns ofdistribution and abundance of common and rare freshwater macroinvertebrates.Biologi cal Conservation 50: 77-98. Griffith, G.E., S. A. Bryce, J. M. Omernik, J. A. Comstock, A. C. Rogers, B. Harrison, S. L.Hatch, and D. Bezanson. 2004. Ecoregions of Texas (color poster with map,

### R.S. King et al. 2009 108 descriptivetext, and photographs): Reston, Virginia, U.S. Geological Survey (map scale1:2,500,000). Hawkins, C., Ostermiller, J., Vinson, M., and R. J. Stevenson. 2001. Stream algae, invertebrate, and environmental sampling associated with biological water quality assessments: field protocols. Utah State University Web site: www.usu.edu/buglab/monitor/USUproto.pdf. Kennedy JH, La Point TW, Balci P, Stanley J, Johnson ZB. 2002. Model aquatic ecosystems in ecotoxicological research: c onsiderations of design, impl ementation, and analysis. In Hoffman DJ, Rattner BA, Burton, Jr. GA, Cairns, Jr J, eds, Handbook of Ecotoxicology,Vol. 2. Lewis Publishers, Boca Raton, FL. pp. 45-74. King, R. S. and C. J. Richardson. 2002. Evaluating subsampling approaches and macroinvertebrate taxonomic resolution for wetland bioassessment. Journal of the North American Benthological Society 21:150-171. King, R. S. and C. J. Richardson. 2003. Inte grating bioassessment and ecological risk assessment: an approach to developing nume rical water-quality criteria. Environmental Management 31:795-809. King, R. S., M. E. Baker, D. F. Whigham, D. E. Weller, T. E. Jordan, P. F. Kazyak, and M. K. Hurd. 2005. Spatial considerations for lin king watershed land c over to ecological indicators in streams. Ecol ogical Applications 15:137-153. King, R. S., and M. E. Baker. In preparation, invited paper. Consid erations for quantifying ecological community thresholds in response to anthropogenic environmental gradients. Journal of the North Amer ican Benthological Society ( Bridges special section on thresholds). Legendre, P. & Legendre, L. 1998. Numerical Ecology. Elsevier Science, Amsterdam, TheNetherlands McCune, B., and J. B. Grace. 2002. Analysis of ecological communities. MjM Software Design, Gleneden Beach, OR. Minchin, P. R. 1987. An Eval uation of the Relative R obustness of Techniques for EcologicalOrdination. Vegetatio 69: 89-107 Moulton, Stephen R. II, Jonathan G. Kennen, Robert M. Goldstein, and Julie A. Hambrook. 2002. Revised Protocols for Sampling Algal, In vertebrate,and Fish Communities as Part of the National Water-Quality Assessment Program. USGS Open-File Report 02-150, Reston, VA. Richardson, C. J., R. S. King, S. S. Qian, P. Vaith iyanathan, R. G. Qualls, and C. A. Stow. 2007. Estimating ecological thresholds for phosphor us in the Everglades. Environmental Science & Technology 41:8084-8091 (c over photo, featured paper). Scott, J. T.*, D. A. Lang*, R. S. King, and R. D. Doyle. 2009. Nitrogen fixation and phosphatase activity in periphy ton growing on nutrient diffusing substrata: Evidence for differential nutrient limitation in stream benthos. Journal of the North American Benthological Society 28:57-68. Scott, J. T.*, J. A. Back*, J. M. Taylor*, and R. S. King. 2008. Does nutrient enrichment decouple algal-bacterial produc tion in periphyton? Journal of the North American Benthological Society 27:332-334. Sonderegger, D. L. , H. Wang, W. H. Clemen ts, B. R. Noon. 2009. Using SiZer to detect thresholds in ecological data. Fronti ers in Ecology and the Environment 7, doi:10.1890/070179

### R.S. King et al. 2009 109 Texas Commission on Environmental Quality (TCEQ). 2005. Surface Water Quality Monitoring Procedures, Volu me 2: Methods for Collecti ng and Analyzing Biological Community and Habitat Data (draft report) . Surface Water Quality Monitoring Program, Monitoring and Operations Division , Austin, Texas. Report #RG-416. Texas Commission on Environmental Qualit y. Guidance for Assessing Texas Surface and Finished Drinking Water Quality Data. 2004. TCEQ. Austin, TX. URL:http://www.tnrcc.state.tx.us/ wa ter/quality/04_twqi303d/04_guidance.pdf Texas Commission on Environmental Quality. Su rface Water Quality Monitoring Procedures, Volume 1: Physical and Chemical Monitori ng Methods for Water, Sediment, and Tissue. Publication No. RG-415. Decembe r 2003. TCEQ. Austin, TX URL: http://www.tceq.state.tx.us/ comm_exec/ forms_pubs/pubs/rg/rg-415/index.html Texas Commission on Environmental Quality. Su rface Water Quality Monitoring Procedures, Volume 2: Methods for Collection and An alyzing Biological Community and Habitat Data. Draft November 2004. TCEQ. Austin, TX Texas Commission on Environmental Qualit y. Surface Water Quality Monitoring Data Management Reference Guide. Augus t 24, 2004. TCEQ. Austin, TX. URL: http://www.tnrcc.state.tx.us/ water/qu ality/data/wqm/wdma/dmrg/2004dmrg.html Texas Commission on Environmental Qual ity. Surface Water Quality Monitoring Procedures,Volume 1: Physical and Chemical Monitoring Methods for Water, Sediment, and Tissue. Toms, J. and M. L. Lesperance. 2003. Piecewis e 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 dosi ng studies. Only taxa that occurred >2 times are included in the table (n=295). Taxonomic identifications were conducted exclusively by Dr. Barbara Wi nsborough, 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 ko lbei 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 d ecussis GEdecu Diatom Gomphosphenia (Gomp honema) 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 ko tschii (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 so lita NIsolita Diatom Nitzschia subacicularis NIsubaci Diatom Nitzschia tropica NItropic Diatom Nitzschia tryblionella NItrybla Diatom Nitzschia vitrea NIvitrea Diatom Pseudostaurosira brevistriata PDbrevis Diatom Pseudostaurosira parasitic a 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 influe nce growth rates in response to phosphorus. Fundamental and Applied Limnology (A rchiv 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 produc tion 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 periphy ton 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 studentsJ. N. Am. Benthol. Soc., 2008, 27(2):332–3442008 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 Scott 1 

### , Jeffrey A. Back 2 

### , Jason M. Taylor 3



### , AND 

### Ryan S. King 4

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 ( 14 C-HCO 3 – 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 l mol m

2 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 (BBP L ) was greater than mean BBP measured in dark incubations (BBP D ), and the difference between the 2 means (BBP L – BBP D ) was positively correlated with mean PS among streams ( R 2

1⁄4 0.53). Covariance between PS and BBP L within streams (COV PS–BBP ) decreased as epilithon nutrient content increased. COV PS–BBP was positively correlated with both epilithon C:N ( R 2

1⁄4 0.78) and C:P ( R 2

1⁄4 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, thecovariation between phytoplankton and bacterioplankton abundance and production often is afunction 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 1 st model suggests that phytoplankton production varies with P concentration, and bacterioplankton vary in response to phytoplankton. The 2 nd model suggests that bothphytoplankton and bacterioplankton respond simultaneously to P. However, patterns in P dynamics andphytoplankton and bacterioplankton production observed at multiple scales have not conformed consistently to either model. As a result, some researchershave 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, andEnvironmental 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

332as 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 l g chlorophyll

a /cm 2 . Low algal biomass occurred primarily innutrient-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 fornutrients 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 andbacteria 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 centralTexas (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 torepresent a continuum of nutrient enrichment. Reconnaissance sampling in June 2006 revealed that these streams were distributed along a steep nutrient gradient, spanning , 10 l g/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 cm 2 ) were collected for simultaneous measurements of PS and BBP under high-light (1400–1700 l mol m

2 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] 333 A LGAL –B ACTERIAL C OUPLING IN P ERIPHYTONwere 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 14 C-HCO 3 – 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- l m 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 l L of 14 C-labeled NaHCO 3 solution (54 l Ci/mL) and placed in the stream at a depth of 7 to 10 cm for 1.5 h. After that time, 50 l L 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 l Ci/nmol leucine,assuming negligible background leucine concentration), depending on the volume of rock being F IG . 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. F IG . 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 [Volume 27 J. T. S COTT ET AL

.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 4 8 C 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 14 C 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 size 1⁄4 0.7 l m) for determination of AFDM. A 2 nd subsample was filtered onto a 0.2- l m-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 alkalinesolution (0.5 mol/L NaOH, 25 mmol/L ethylenediamine-tetra-acetic acid [EDTA], and 0.1% sodium dodecylsulfate) and agitated for 1 h at 85 8 C. 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 14 C 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 HCO 3 – 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 14 Clabeled HCO 3 – with the quantity of unlabeled dissolved inorganic C (DIC) in incubation. DIC concentration in filtered incubation water was measured witha 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 14 C 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 l g C cm

2 h

1 and l g C mg

1 AFDM h

1 , 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 l g C cm

2 h

1 or l g C mg

1 AFDM h

1 , 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 (500 8 C, 4 h) and weighed GF/F for AFDM measurement. A 2 nd 100- to 160-mL subsample was transferred into a large weighing boat. The water was allowed to evaporate, and the sample was dried at 60 8 C. The bulk sample was then homogenized into a powder for C, N, and P analysis. A 3 rd 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 H 2 SO 4 at 350 8 C (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] 335 A LGAL –B ACTERIAL C OUPLING IN P ERIPHYTONData analyses The amount of BBP stimulated by PS in each stream was calculated by subtracting the mean BBP rate measured in dark incubations (BBP D ) from the mean BBP rate measured in light incubations (BBP L ) (mean BBP L – mean BBP D ). The respective errors were propagated additively. Model II major-axis regression was used to assess the relationship between (mean BBP L – mean BBP D ) and mean PS because our predictor (mean PS) and response (mean BBP L – mean BBP D ) 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 BBP L across all streams was assessed using ordinary least squares (OLS) regression (Proc GLM, SAS version9.1; SAS Institute, Cary, North Carolina). The covariance value between PS and BBP L within each stream reach was calculated as

COV PS

BBP 1⁄4 X n

i 1⁄4 1 ð PS i PS Þð BBP L i BBP L Þ

n ;

where i is the incubation replicate. The correlation coefficient (CORR PS–BBP ) was the covariance divided by the product of individual standard deviations (SD) of PS and BBP L . OLS regression was used to determine how strongly each metric of PS–BBP L 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–BBP L coupling metrics and streamwaterand epilithon nutrient concentrations. Positive relationships, indicative of decreasing covariance between PS and BBP L with increasing nutrient content in epilithon, were expected between PS–BBP L 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 l g/L to . 2 mg/L, and TN levels ranged from , 200 l g/L to . 8 mg/L among sites (Table 1). DIC concentrations varied little among sites, with the exception of NBOS-03, where DIC was 2 3 to 3 3 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 amongsites 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 BBP L

PS and BBP L were highly variable among sites (Table 3). BBP L rates were always greater than BBP D rates, and mean BBP L – mean BBP D was positively correlated with mean PS across all sites (Fig. 3). T

ABLE 1. Water chemistry and flow at sampling locations. DIC 1⁄4 dissolved inorganic C.

Stream Site code DIC (mg/L) Total N ( l g/L) Total P ( l g/L) Discharge(m 3 /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 [Volume 27 J. T. S COTT ET AL

.Epilithon PS and BBP L values in individual replicateswere positively correlated across streams sites regardless of whether surface area (Fig. 4A) or AFDM (Fig. 4B) was used to normalize PS and BBP L . However, therelatively 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 BBP L 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 BBP L were positively correlated in all streams, with the exception of area-normalized rates at DUFF-01 (Table 3). Mean correlation coefficients for PS and BBP L among sites were similar for area-normalized ( r 1⁄4 0.78
6 0.42 SD) and AFDM-normalized ( r 1⁄4 0.79 6 0.26 SD) rates. PS–BBP L 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–BBP L covariance was 1.6 3 10

3 6 1.8 3 10

3 , whereas average AFDM-normalized PS– BBP L covariance was 2.3 3 10

3 6 4.7 3 10

3

.

PS and BBP L covariation along nutrient-enrichment gradient

Correlation coefficients (CORR PS–BBP ) and covariance values (COV PS–BBP ) for area-normalized PS and BBP L rates were not correlated with any measure of nutrient concentration. CORR PS–BBP for AFDM-normalized PS and BBP L rates also were not correlated with any measure of nutrient concentration. However, COV PS–BBP values for AFDM-normalized PS and BBP L (COV PS–BBP,AFDM ) were correlated with multiple metrics of nutrient concentration across sites.

COV PS–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 th e relationship between COV PS–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 COV PS–BBP,AFDM and bulk % P was statistically significant after excluding these streams (Fig. 5D). The relationship between COV PS–BBP,AFDM and bulk C:P was statistically significant regardless ofwhether wastewater-influenced streams were includT

ABLE 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 ( 6 1 SD) ash-free dry mass (AFDM) is of epilithon from 12 individual rocks used in 14 C 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/cm 2 )

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

F IG . 3. Major-axis regression of mean ( 6 1 SE) difference between bacterial biomass production in light (BBP L ) and in dark (BBP D ) incubations (BBP L – BBP D ) 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] 337 A LGAL –B ACTERIAL C OUPLING IN P ERIPHYTONed, but the relationship was much stronger when wastewater-influenced streams were excluded (Fig. 5F). COV PS–BBP,AFDM was not significantly related to OM % N regardless of whether the wastewater-influenced streams were included (Fig. 6A). COV PS–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). COV PS–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 COV PS–BBP,AFDMand OM C:N (Fig. 6C) but strengthened the relationship between COV PS-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. COV PS–BBP,AFDM decreased with increasing nutrient concentration, and the relationship was particularly strong when comparing COV PS–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 F IG . 4. Linear regression of bacterial biomass production in the light (BBP L ) 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 streamsalong 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.

T

ABLE 3. Mean ( 6 1 SD) photosynthesis (PS) and bacterial biomass production in the light (BBP L ) for all streams and multiple metrics for estimating the relationship between PS and BBP L at each site. CORR PS–BBP is the correlation coefficient between PS and BBP L in light incubations in each stream. COV PS–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 ( l g C cm

2 h

1 ) BBP L ( l g C cm

2 h

1 ) CORR PS–BBP,area COV PS–BBP,area CORR PS–BBP,AFDM COV PS–BBP,AFDM

PALU-01 0.061 6 0.021 0.029 6 0.005 0.96 7.45 3 10

5 0.96 3.03 3 10

4 ROCK-01 0.184 6 0.059 0.053 6 0.014 1.0 5.24 3 10
4 1.0 1.37 3 10
2 SALA-01 0.272 6 0.193 0.086 6 0.033 0.97 4.92 3 10

3 0.88 2.30 3 10

3 DUFF-01 0.121 6 0.009 0.066 6 0.007 –0.24 –7.52 3 10

6 0.86 1.64 3 10

4 NBOS-03 0.315 6 0.101 0.213 6 0.062 0.76 3.77 3 10

3 0.21 6.70 3 10

5 LEON-01 0.130 6 0.010 0.046 6 0.018 0.86 1.25 3 10
3 0.64 2.44 3 10
4 NOLC-01 0.287 6 0.079 0.052 6 0.013 0.92 7.81 3 10

4 0.79 2.02 3 10

4 LEON-02 0.212 6 0.082 0.057 6 0.018 0.99 1.19 3 10

3 0.99 1.06 3 10

3 338 [Volume 27 J. T. S COTT ET AL

.demonstrated in attached microbial communities, such as epilithon.

We found no evidence that competitive interactionsdecoupled algal and bacterial production in nutrientpoor streams. In fact, COV PS–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 betweenmicrobial autotrophs and heterotrophs. Efficient recycling of C, N, and P within biofilm matrices has long been suspected as an important driver of communityfunction 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 F IG . 5. Linear regression of the covariance of photosynthesis (PS) and bacterial biomass production in the light (BBP L ) normalized to epilithon ash-free dry mass (AFDM) (COV PS–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 COV PS-BBP,AFDM . 2008] 339 A LGAL –B ACTERIAL C OUPLING IN P ERIPHYTONavailability to cells growing within its polysaccharide matrix.

Influences of streamwater nutrients vs epilithon nutrients on algal–bacterial coupling

COV PS–BBP,AFDM was more strongly related to epilithon OM nutrient content than to epilithon bulknutrient 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 extracellularnutrients 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 COV PS–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, COV PS–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 COV PS–BBP,AFDM across all streams, particularly when derived from epilithon OM (Fig. 6C, D). Thus, a stoichiometrically explicit approach (sensu F IG . 6. Linear regression of the covariance of photosynthesis (PS) and bacterial biomass production in the light (BBP L ) normalized to epilithon ash-free dry mass (AFDM) (COV PS–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 COV PS-BBP,AFDM . 340 [Volume 27 J. T. S COTT ET AL

.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 leafdetritus 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 insubstantially 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 subs equent 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-containingmicroorganisms in metaphyton from the strongly Plimited Florida Everglades. They hypothesized that bacteria provided the primary mechanism for Premineralization, 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 bacteriamight 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 p eriphyton communitiesexhibited 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 innatural periphyton communities was affected inconsistently by light, glucose, and DIN additions (Francoeur and Wetzel 2003). However, Scott and Doyle (2006) demonstrated that bacterial production andalgal 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 evidencefrom 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 mutuallybeneficial 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 innutrient-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 BBP L 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 BBP L onindividual rocks (Fig. 4A). However, AFDM-normalized PS and BBP L 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 alongthe nutrient gradient. However, many studies overlook microscale variability and struggle to control the 2008] 341 A LGAL –B ACTERIAL C OUPLING IN P ERIPHYTONsubstantial 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 heterogeneit y 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 sampleopen-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 streamepilithon 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, butthe 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 beconfirmed 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 communitiesand 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 ofanthropogenic nutrient enrichment on aquatic ecosystem function by demonstrating that stream-nutrientenrichment 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 theirhelpful comments on a previous draft of this manuscript.

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ALCHEVSKIY , V., AND S. E. F INKEL . 2006. Escherichia coli competence gene homologs are essential for competitive fitness and the use of DNA as a nutrient. Journal of Bacteriology 188:3902–3910. R ESHKIN , S. J., AND G. A. K NAUER . 1979. Light stimulation ofphosphate uptake in natural assemblages of phytoplankton. Limnology and Oceanography 24:1121–1124. R IER , S. T., K. A. K UEHN , AND S. N. F RANCOEUR . 2007. Algal regulation of extracellular enzyme activity in stream microbial communities associated with inert substrataand detritus. Journal of the North American Benthological Society 26:439–449. R IER , S. T., AND R. J. S TEVENSON . 2001. Relation of environmental factors to density of epilithic lotic bacteria in two ecoregions. Journal of the North American Benthological Society 20:520–532. R IER , S. T., AND R. J. S TEVENSON . 2002. Effects of light, dissolvedorganic carbon, and inorganic nutrients on the relationship between algae and heterotrophic bacteria in stream periphyton. Hydrobiologia 489:179–184. R OMANI  ́ , A. M., AND S. S ABATER . 1999. Effect of primary producers on the heterotrophic metabolism of a stream biofilm. Freshwater Biology 41:729–736. S COTT , J. T., AND R. D. D OYLE . 2006. Coupled photosynthesis 2008] 343 A LGAL –B ACTERIAL C OUPLING IN P ERIPHYTONand bacterial biomass production in a nutrient-limited wetland periphyton mat. Aquatic Microbial Ecology 45: 69–77. S COTT , J. T., R. D. D OYLE , J. A. B ACK , AND S. I. D WORKIN . 2007. The role of N 2 fixation in alleviating N limitation in wetland metaphyton: enzymatic, isotopic, and elemental evidence. Biogeochemistry 84:207–218. S HARMA , K., P. W. I NGLETT , K. R. R EDDY , AND A. V. O GRAM . 2005.Microscopic examination of photoautotrophic and phosphatase-producing organisms in phosphorus-limitedEverglades periphyton mats. Limnology and Oceanography 50:2057–2062. S TERNER , R. W., AND J. J. E LSER . 2002. Ecological stoichiometry: the biology of elements from molecules to the biosphere. Princeton University Press, Princeton, New Jersey. S TEVENSON , R. J. 1996. The stimulation and drag of current. Pages 321–340 in R. J. Stevenson, M. L. Bothwell, and R. L. Lowe (editors). Algal ecology: freshwater benthic ecosystems. Academic Press, San Diego, California. TCEQ (T EXAS C OMMISSION ON E NVIRONMENTAL Q UALITY ). 2007. Surface water quality monitoring. Monitoring andOperations Division, Texas Commission on Environmental Quality, Austin, Texas. (Available from: http://www. tceq.state.tx.us/assets/public/compliance/monops/ water/wdma/dmrg/2007/2007dmrg_complete.pdf) T HOMAZ , S. M., AND R. G. W ETZEL . 1995. [ 3 H]Leucine incorporation methodology to estimate epiphytic bacterial biomass production. Microbial Ecology 29:63–70. T OOLAN , T., J. D. W EHR , AND S. F INDLAY . 1991. Inorganic phosphorus stimulation of bacterioplankton production in a meso-eutrophic lake. Applied and Environmental Microbiology 57:2074–2078. V

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ANNOTE , R. L., G. W. M INSHALL , K. W. C UMMINS , J. R. S EDELL , AND C. E. C USHING . 1980. The river continuum concept. Canadian Journal of Fisheries and Aquatic Sciences 37: 130–137. W ETZEL , R. G. 1993. Microcommunities and microgradients: linking nutrient regeneration, microbial mutualism, and high sustained aquatic primary production. Netherlands Journal of Aquatic Ecology 27:3–9. W ETZEL , R. G., AND G. E. L IKENS . 2000. Limnological analyses. 3 rd edition. Springer, New York. W OTTON , R. S. 2007. Do benthic biologists pay enough attention to aggregates formed in the water column of streams and rivers? Journal of the North American Benthological Society 26:1–11. Received: 27 August 2007 Accepted: 5 February 2008 344 [Volume 27 J. T. S COTT ET AL

.J. N. Am. Benthol. Soc., 2009, 28(1):57–682009 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 Scott 1 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. Lang 2 

### , Ryan S. King 3



### , AND 

### Robert D. Doyle 4 Center for Reservoir and Aquatic Systems Research, Department of Biology, Baylor University, Waco, Texas 76798 USA

Abstract. We explored N 2 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]), N 2 fixation, and APA to evaluate the relative importance of N 2 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, N 2 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, N 2 fixation was high but not different in the control and P treatments and was virtually undetectable on the N treatment. N 2 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 N 2 fixation to periphyton in control and P treatments was 0.93 mg N/m 2 on day 18 and 1.0 mg N/m 2 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 N 2 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 marineenvironments, 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 N 2 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 N 2 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 57importance of N 2 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 N 2 into biologically available NH 4 þ (Voet and Voet 2004). In aquatic environments, N 2 fixation is carried out by certain cyanobacteria, heterotrophic bacteria, and some diatoms with cyanoba cterial endosymbionts (Howarth et al. 1988). Periphyton N 2 fixation and itsrelationship 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 N 2 fixation because N 2 fixation is energetically expensive. However, N 2 fixation can provide a significant amount of N to the entire periphyton community when DIN concentrations arelow. For instance, Grimm and Petrone (1997) demonstrated that fixed N 2 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 N 2 fixation in periphyton from oligotrophic streams. P enrichment increased periphyton N 2 fixation rates by increasing the density of N 2 -fixing organisms within the periphyton community. Higher temperature also increased N 2 fixation by increasing the density of N 2 -fixing organisms and increasing the rate of cell-specific N 2 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 N 2 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 N 2 fixation in streams experiencingstronger 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 naturalnutrient sources (Downing and McCauley 1992). However, N 2 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 N 2 -fixing microbial communities in a freshwater wetland, but similar studies have not been done in streams.

Nutrient-diffusing substrata (NDS) are useful tools foridentifying 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 ofperiphyton 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 withNDS. However, few studies have coupled NDS experiments with more-complex measurements of periphyton community function such as N 2 fixation and APA.

We measured periphyton N 2 fixation, APA, and biomass accumulation (as AFDM and CHLA) to understand the importance of N 2 fixation in alleviating N limitation to stream periphyton. We worked in a stream-like outflow of a constructed freshwater marsh. N 2 fixation and APA must be measured in enclosed containers. Therefore, we used the micro-NDS design

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.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 N 2 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 N 2 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 N 2 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 N 2 fixation and APA would be more sensitive than AFDM and CHLA to changes in nutrient availability. Our goals were to evaluate N 2 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 m 3 /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- l m 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 NaNO 3 ), or P enrichment (0.065 M P from Na 3 PO 4 12H 2 O). We randomly placed NDS in a rack (60 3 90 cm) made from wire netting framed with plastic-coated plant stakes. We used a 2 nd 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), N 2 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 todetermine 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, N 2 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), NO 2 þ NO 3 -N (NO x -N), and PO 4 -P levels using colorimetric methods (APHA 1992) on a Lachat t 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 HOBO t Water Temp Pro temperature logger (OnsetComputer 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

W:/xml/jnabs/jnbs_2801/jnbs-28-01-06.3d Wednesday, 12 November 2008 10:35 am Allen Press, Inc. Page 59 2009] 59 P ERIPHYTON N 2 F IXATION AND APA(control, N, and P) on days 1, 2, 3, 6, 12, 18, 24, and 29after 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 NO x -N and PO 4 -P concentrations.

Estimation of N 2 fixation, APA, AFDM, and CHLA We kept periphyton intact on FGDs from NDS for measurement of N 2 fixation and APA before removal for biomass estimation (AFDM, CHLA). We measured N 2 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 N 2 -fixation assays. We conducted N 2 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 N 2 fixation assays or 30 mL of 1.2% Tris buffer for APA assays.

N

2 fixation.— We estimated N 2 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 l mol m

 2 s  1 ) for 2 to 4 h. We incubated 2 samples from each treatment in the dark to estimate the importance of N 2 fixation by heterotrophs. In addition, we incubated streamwater samples under lights to account for any background N 2 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 toestablish equilibrium between water and vapor phases. We withdrew 1 l L 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 70 8 C. We used 10-ppm ethylene standards to calibrate the instrument daily. To estimate N 2 fixation, we assumed that the production of 3 l mol ethylene was equivalent to the fixation of 1 l mol N 2 (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 ofmethylumbelliferone (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- l g/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 N 2 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 fiberfilters (GFFs) for AFDM and CHLA analyses, respectively. We estimated AFDM on prewashed, precombusted, and preweighed GFFs. We dried AFDM samples at 100 8 C for 1 h, weighed them to estimate dry mass, combusted them for 1 h at 500 8 C, 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 BeckmanDU 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, NO x -N, and PO 4 -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, N 2 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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.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 log 10 ( 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 NO x -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 PO 4 -P concentrations did not differ between upstream anddownstream samples (Table 1). Mean water temperature was 25.5 8 C (range: 23–28 8 C) during the study period.

Mean agar concentrations of NO x -N and PO 4 -Pdeclined exponentially during the study period. Day29 NO x -N and PO 4 -P remained within

; 40% and 75% of day-0 concentrations, respectively. NO x -N concentrations of the control and P treatment were

; 0. PO 4 -P concentrations of the control and N treatment also were low, but the PO 4 -P concentrations tended to be slightly higher in the N treatment than in the control. The 1 st derivatives of exponential-loss equations predicted the release rates for NO x -N and PO 4 -P of the N and P treatments, respectively (Fig. 1A, B). The predicted release rate of NO x -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 NO x -N cm

 2 d

 1 on day 6, 1.2 mg NO x -N cm

 2 d

 1 on day 18, and 0.57 mg NO x -N cm

 2 d

 1 on day 29. The release rate of PO 4 -P from the P treatment did not exhibit a rapid initial decline (Fig. 1B). NDS released 0.41, 0.26, and 0.18 mg PO 4 -P cm

 2 d

 1 on days 6, 18, and 29, respectively.

Effects of N and P enrichment on periphyton accrual

Periphyton AFDM increased significantly during the study period ( F 7,129 1⁄4 92.88, p , 0.0001; Fig. 2). However, area-normalized AFDM did not differ significantly among nutrient treatments ( F 2,129 1⁄4 1.30, p 1⁄4 0.2751), nor was the nutrient 3 date interaction significant ( F 14,129 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 cm

 2 d

 1 between days 1 and 6 and was 0.2197 mg cm

 2 d

 1 between days 24 and 29. Areanormalized CHLA varied significantly among nutrient treatments over the study period ( F 4,97 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.

N

2 fixation and APA following nutrient enrichment

N 2 fixation varied significantly among nutrient treatments during the study period, regardless of T

ABLE 1. Mean ( 6 1 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, NO x -N 1⁄4 NO 2 þ NO 3 -N. n 1⁄4 5.

Analyte Concentration ( l g/L)

Paired t 1-tailed p Upstream Downstream

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 NO x -N 49.6 6 3.2 62.8 6 4.5 2.46 0.03 PO 4 -P 24.5 6 0.4 29.9 6 3.1 1.70 0.08

F IG . 1. N (A) and P (B) release rates from nutrient diffusing substrates.

W:/xml/jnabs/jnbs_2801/jnbs-28-01-06.3d Wednesday, 12 November 2008 10:35 am Allen Press, Inc. Page 61 2009] 61 P ERIPHYTON N 2 F IXATION AND APAnormalization method ( p , 0.001; Fig. 4A–C). N 2 fixation did not differ significantly among nutrient treatments on day 6. However, N 2 fixation was significantly greater in control and P than in N treatments on days 18 and 29. N 2 fixation did not differ significantly between control and P treatments (Fig. 4A–C). Biomass (CHLA)-normalized N 2 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 N 2 fixation, but the difference between days was not significant

(Fig. 4B). N 2 fixation in dark incubations was consistently . 0 in control and P treatments (Table 2). N 2 fixation in dark incubations accounted for 4.0 to 73.3% of N 2 fixation in light incubations, except in the F IG . 2. Mean ( 6 1 SE) periphyton biomass measured as area-normalized ash-free dry mass (AFDM) on control, N, and P nutrient diffusing substrate treatments over 29 d.

F IG . 3. Mean ( 6 1 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). F IG . 4. Mean ( 6 1 SE) N 2 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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.control treatment on day 6 when N 2 fixation was 7 3 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 ( F 2,36 1⁄4 57.17–91.67, p , 0.0001; Fig. 5A–C). APA varied significantly among nutrient treatments over the study period ( F 4,36 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 theirusefulness in long-term studies (Corkum 1996, Rugenski et al. 2008). Most NDS studies are limited to theperiod 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 ourstudy, 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 NO x -N cm

 2 d

 1 , 0.18 mg PO 4 -P cm

 2 d

 1 ) to inhibit periphyton N 2 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 T

ABLE 2. Mean periphyton N 2 fixation rates ( 6 1 SE) in dark incubations. n 1⁄4 2.

Treatment Day Dark N 2 fixation ( l gNm

 2 h

 1 ) % of mean light N 2 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

F IG . 5. Mean ( 6 1 SE) alkaline phosphatase activity (APA), expressed as mass phosphatase (P ase ) 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).

W:/xml/jnabs/jnbs_2801/jnbs-28-01-06.3d Wednesday, 12 November 2008 10:35 am Allen Press, Inc. Page 63 2009] 63 P ERIPHYTON N 2 F IXATION AND APAdid 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/cm 2 . 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 l g/cm 2 and periphyton P probably ranged from 3 to 6 l g/cm 2 . Thus, the N and P diffusion rates were sufficient to replace periphyton N 10 to 20 3 and periphyton P 30 to 60 3 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. NO x -N concentrations were slightly higher in water collected 0.5 m downstream than in water collected immediately upstream of the rack. The slightly higher NO x -N concentrations supplied to downstream NDS might have dampened periphytonresponse to nutrient treatments in downstream samples, but the concentrations of NO x -N in downstreamwater were certainly lower than the exposure concentrations of NO x -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 inthe 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).

N

2 fixation, APA, and biomass on NDS

N

2 fixation.— Periphyton N 2 fixation ranged from 44 to 86 l gNm

 2 h

 1 in control and P treatments on days 18 and 29. This range is 4 to 8 3 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

l gNm

 2 h

 1 (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 N 2 -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 N 2 fixation would be highest in control and P treatments and that N enrichment would suppress N 2 fixation by periphyton. N 2 fixation rates were lower in N than in control treatments, but P enrichment did not stimulate N 2 fixation in our system. Mean N 2 fixation was always lower on P treatments than on controls, although this difference was never statistically significant. That P enrichment did not stimulate N 2 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 N 2 fixation potential. However, we lack taxonomic data to support this hypothesis. Marcarelli and Wurtsbaugh (2006) found that P enrichment increased the number of N 2 -fixing taxa in an oligotrophic stream, but other studies have reported that P enrichment decreased the rate of N 2 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 N 2 fixation might have decreased in response to P enrichment. Sundareshwar et al. (2003) reported reduced heterotrophic N 2 fixation in estuarine sediments after P enrichment, but found that glucose alone and glucose þ P additions stimulated N 2 fixation. We did not replicate dark N 2 fixation measurements sufficiently for statistical comparisons, but N 2 fixation in the dark was 4 to 73% of N 2 fixation in the light on days 18 and 29. Thus, heterotrophic N 2 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 N 2 fixation was 0.93 mg N m

 2 d

 1 on day 18 and 1.0 mg N m

 2 d

 1 on day 29 (mean of control and P treatment N 2 fixation rates integrated over a 14:10 h day:night cycle). The rates of N diffusion in the N treatment suppressed N 2 fixation on days 18 (12.0 g N

m  2 d

 1 ) and 29 (5.7 g N m

 2 d

 1 ) and supplied 6 3 10 3
to 12 3 10 3 times more N to the N-enriched periphyton than was supplied by N 2 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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.inhibited N 2 fixation. However, agar N concentrationscan be modified to manipulate diffusion rates (Rugenski et al. 2008), and future studies might be able to clarify the relationships between diffusion rates of NO 3 – or NH 4 þ from NDS and up- or down-regulation of N 2 fixation by periphyton.

N 2 fixation rates in streams generally are much lower than instream denitrification and stream DIN uptake rates (Marcarelli et al. 2008). This conclusion suggests that N 2 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 time

 1 . If we assume that periphyton N in our study probably ranged from 30 to 60 l g N/cm 2 on NDS, then N-specific uptake from N 2 fixation on control and P treatments in our study was 1.5 3 10

 3 /d to 3.1 3 10

 3 /d on day 18 and 1.7 3 10

 3 /d to 3.4 3 10

 3 /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 10

 4 /d to 10

 1 /d, a range that brackets our N 2 fixation estimates. Therefore, the importance of N 2 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 wouldinactivate 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 noton 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 l g 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 N 2 fixation and APA were greatly influenced by periphyton biomass. AFDM and CHLA increased substantially between days 18 and 29. Area-normalized N 2 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 N 2 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 N 2 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 N 2 fixation and APA would be more sensitive than periphyton biomass to differences in nutrient enrichment. N 2 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 fordifferential 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 distinguishwhether 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] 65 P ERIPHYTON N 2 F IXATION AND APAtrophs. We do not know whether only a subset of autotrophs, mostly heterotrophs, or a combination ofboth 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 Nand P exchange between trophic levels. Thus, periphyton communities experiencing differential nutrient limitation between trophic levels might be moreproductive 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 ofprimary importance in oligotrophic ecosystems (Vadeboncoeur et al. 2002). Results of our study suggest that N 2 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 N 2 fixation and APA are sensitiveindicators 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 N 2 fixation and stimulated APA, and Penrichment inhibited APA. Therefore, microbial metabolism was related to both N and P availability. Furthermore, the simultaneous activity of both N 2 fixation and APA and their respective responses tonutrient 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 ifdifferential 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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