LONG-TERM PHYTOPLANKTON DYNAMICS IN UPPER KLAMATH LAKE WITH CONSIDERATIONS OF TROPHIC STRUCTURE AND CLIMATE CHANGE

Nadia Gillett, Yangdong Pan, John Rueter, Angela Strecker, Debbie Blackmore, Heejun Chang, and Gene Foster* Center for Lakes and Reservoirs Portland State University, Portland, OR *Oregon Department of Environmental Quality, Portland, OR

NALMS 2014

Average Depth: 2.4 m Elevation: 1261.6 m Surface area: 249 km2 (over 283.3 km2 with Agency Lake) Land cover: forest (72.9%) and range land (13.3%)

Upper Klamath Lake, Oregon

Ecosystem restoration in Upper Klamath Lake (UKL)

•  Removal of six dams on the Klamath River •  Six restoration technologies have been proposed for the lake: •  dredging •  alum/oxygenation •  massive filtration to remove algae •  treatment wetlands and floating islands •  Increase in natural wetlands •  distributed upland landscape improvements

Three general questions • What is the spatio-temporal structure of algae in the lake?

•  Is there any evidence for competition or succession between APFA and MSAE

• What is the effect of the weather/climate

Dataset •  Water quality, phytoplankton, zooplankton (Klamath Basin Tribes) •  Climate dataset (NALDAS-2, PRISM) •  Years 1990-1997 12 sites (n=705)

Species  name   Division  %  

samples   Mean   Median   Min   Max  Aphanizomenon  flos-­‐aquae   Cyanophyta   79   65.85   85.45   0.06   100  Rhodomonas  minuta   Cryptophyta   74   0.82   0.21   0   28.57  Cryptomonas  ovata   Cryptophyta   52   21.74   11.66   0.26   96.12  Stephanodiscus  hantzschii   Diatoms   39   7.95   1.11   0   87.05  Oscillatoria  geminata   Cyanophyta   35   8.42   5.33   0.16   62.26  Chlamydomonas  sp   Chlorophyta   35   8.2   2.61   0.02   77.36  Asterionella  formosa   Diatoms   28   11.48   3.46   0.01   98.57  

• No temporal or spatial patterns in the structure of UKL phytoplankton

•  Important environmental variables in structuring UKL phytoplankton (ordination)

Microcystis aeruginosa

Aphanizomenon flos-aquae

www.plingfactory.de

www.cit.vfu.cz

Two hypotheses for APFA/MSAE •  APFA and MSAE may compete

•  Implication – decreasing APFA could lead to increases in MSAE

•  APFA blooms may provide combined nitrogen for a follow-on bloom of MSAE •  Implication – decreasing APFA could lead to decreases in MSAE

1 outlier (0% MSAE, 100% APFA) excluded after diagnostics check

Linear regression

Aphanizomenon < 68%biovolume Aphanizomenon

> 68%biovolume

Microcystis % biovolume n=number of samples

Microcystis biovolume (%) increases

All samples n=1546

Environmental variables and Aphanizomenon used as predictors of Microcystis abundance

SRP<59 SRP>59

Chlorophyll<16.5 Chlorophyll>16.5

Aphanizomenon > 40.5%biovolume

Aphanizomenon <40.5%biovolume

Notice different months on x-axis

Species presence only

Climate change in the Pacific Northwest

•  Increases in air temperature • Higher number of extreme heat days • Heavier precipitation

•  Predicted to increase in the frequency, severity, and duration of algal blooms

Barr et al. (2010), OCCRI (2010), Pearl and Huisman (2008)

Calm, wind speed <3 m/s Turbid, wind speed >3 m/s Cold, max temperature <20oC Warm, max temperature >20oC

April-October (n=1386)

Climate conditions

• Climate changes that may also impact MSAE over APFA • Models can’t be created because so few samples with MSAE

• Higher temperatures help both • Wind speed – higher wind speed might favor MSAE

• Downscaling climate to lake scale, might help us see if climate change predicts more windy days or overnight stratification (that seems to be detrimental to APFA)

Summary

• Water quality variables are important factors in the structure of UKL phytoplankton

• Management activities that reduce Aphanizomenon and expected climate changes may lead to a shift in lake ecology in which a noxious but non-toxic strain of Aphanizomenon could be replaced by a toxic strain of Microcystis

• Need finer scale weather simulations (wind, temperature and lake stratification) to look for patterns in algae.

Next steps

•  Add remaining phytoplankton data •  Create a conceptual ecosystem model • Develop a regional climate model and simulate lake responses that can be used for lake management

Acknowledgements •  Jake Kann (Aquatic Ecosystem Sciences) •  Eli Asarian (Riverbend Sciences) •  Nicole Alfafara, Kit Rouhe, Mike Psaris (Portland

State University)

Algae bloom on surface of Upper Klamath Lake with Mt. McLoughlin in background. (Photograph taken by Dean Snyder, U.S. Geological Survey, Klamath Falls, Oregon, Oct. 27, 2007)

Components of the project

•  Retrospective analysis of multiple datasets (Klamath Basin Tribes, USGS, PacifiCorp) to identify factors that can be used to predict harmful algal blooms

•  Creation of a conceptual ecosystem model • Down-scale the larger climate model and simulations so that it can be used for lake management

Aphanizomenon flos-aquae (%) as a response variable (Classification And Regression Tree analysis)

TN TP

Temp

TP Wind Temp

Aphanizomenon biovolume (%) increases

Summers (June-Oct) n=1094

Microcystis % biovolume n=number of samples

NH>386 NH<386

Aphanizomenon < 65%biovolume

Aphanizomenon > 65%biovolume

Environmental variables and Aphanizomenon used as predictors of Microcystis abundance

APFA < 38% APFA > 38%

Microcystis biovolume (%) increases