Gregory R. Koch1, Daniel L. Childers2, Peter A. Stæhr3, René Price4, Stephen E. Davis5, and Evelyn E. Gaiser1

1Department of Biological Sciences, Florida International University Miami, FL, USA 2School of Sustainability, Arizona State University Tempe, AZ, USA

3Department of Marine Ecology, Aarhus University, Roskilde, Denmark 4Department of Earth and Environment, Florida International University, Miami, FL, USA

5Everglades Foundation, Palmetto Bay, FL, USA Results (con’t)

1.  Caraco, N.F., J.J. Cole, and G.E. Likens. 1989. Nature 341(6240): 316-318. 2.  Cole, J.J. and N.F. Caraco. 1998. Limnol. Oceanogr. 43: 647-656. 3.  Davis, S.E., and D.L. Childers. 2007. Estuarine, Coastal, Shelf Science 71: 194-201. 4.  Price, R.M., P.K. Swart, and J.W. Fourqurean. 2006. Hydrobiologia 569: 23-36. 5.  Smith, S. V. 1985. Plant, Cell and Environment 8: 387-398. 6.  Staehr, P.A., D. Bade, C.E. Williamson, M. Van de Bogert, T. Kratz, G.R. Koch, P. Hanson, J. J. Cole. 2010. Limnol.

Oceanogr. Methods 8: 628-644.

Methods We estimated metabolism rates from free-water, diel changes in dissolved oxygen measured at 10-minute increments from floating platforms following methods outlined in Stæhr et al. (2010). We assumed that night respiration was equal to day respiration and that the ponds were fully mixed. We also collected wind speed, water temperature, surface irradiance, and underwater PAR data as part of metabolism calculations or as driving variables. TP and salinity data are from FCE LTER water quality monitoring, discharge data are from USGS monitoring, and rainfall data are from Royal Palm Station through DBHYDRO. ΔO2 / Δt = GPP – R – Fatm

Fatm (g O2 m-2 h-1) = k (O2 meas – O2 sat)

k (cm h-1) = k600(cm h-1) (Sc/600)-0.5

k600 (cm h-1) = 2.07 + 0.215(wind10m)1.7 (Cole and Caraco 1998)

wind10m = windZ (1.4125 (z-0.15)) (Smith 1985)

Sc = 0.0476 (Temp)3 + 3.7818 (Temp)2 - 120.1(Temp) + 1800.6

Abstract / Introduction It is well known that subtropical South Florida experiences wet/dry seasons based on changes in regional precipitation, as opposed to four temperature-based seasons seen at temperate latitudes. While existing wet/dry seasonal demarcatations (Jun-Dec = WET; Jan-May = DRY) are useful for explaining ecologial patterns in freshwater Everglades data, these demarcations may not be appropriate for downstream Everglades estuaries. If inter-annual variation in ecological variables of Everglades estuaries is governed largely by reception of upstream Everglades freshwater, it stands to reason that these estuaries will experience seasons at a different time of the year than the freshwater Everglades. For instance, Everglades estuarine seasons may lag traditional wet/dry seasons and estuarine seasons may not have equal length. In this poster, we examine 14 years of LTER data from estuarine Taylor River and try to assess the timing of seasons based on observed changes in water quality variables. In addition, we also examine ecosystem metabolism data from Taylor River in order to see if estuarine ecosystem function changes seasonally with water quality drivers.

Site Description

Taylor River is an oligotrophic, P-limited estuarine river system characterized by a series of connecting ponds and creeks. FCE LTER stations are situated in the north (TS/Ph 6) and at the mouth (TS/Ph 7) of the river (Fig. 1).

Figure 1. Location of study sites within the pond-and-creek landscape pattern of lower Taylor River and the position of the river within the southern Everglades.

TS/Ph 6

TS/Ph 7

P5 P4 P3

Taylor River

Florida Bay

Taylor Slough

We thank the GLEON network for facilitating international collaborations and the Everglades Foundation and FCE LTER for financial support. Steven Davis, Jennifer Richards, and Rene Price have been invaluable in developing this research project and we thank James Heffernan for helpful discussions. Damon Rondeau, Rafael Travieso, and Adam Hines were invaluable help with buoy construction, deployment, and field work. We also thank Mark Zucker at USGS for assistance with Taylor River discharge data. This material is based upon work supported by the National Science Foundation under Cooperative Agreements #DBI-0620409 and #DEB-99110514.

Results

Figure 2. Left: Monthly mean hydrologic and water quality variables from Taylor River LTER and USGS monitoring stations. Right: Same data in polar plots to examine interannual variability.

GPP Parameter Coefficient p Pond 3 E0 -0.2007 0.0429

r2 = 0.60 Salinity 1.5900 0.2819 (n = 51) Temp ---- ----

TP ---- ---- Pond 4 Salinity 2.4755 0.0593

r2 = 0.79 TP ---- ---- (n = 75) E0 ---- ----

Temp ---- ---- Pond 5 Salinity 5.2995 <0.0001

r2 = 0.76 Temp -4.5343 0.0132 (n = 65) TP ---- ----

E0 ---- ----

Site Label Size (ha) Pond 3 P3 4.69 Pond 4 P4 0.66 Pond 5 P5 0.60

Table 1. Results of multiple autoregression analysis to investigate the contribution of environmental drivers to variation in pond GPP. Only parameters with statistics were included in the final model, selected by the model with lowest AIC.

Figure 3. Weekly mean metabolism rates estimated from free-water diel changes in dissolved oxygen; GPP = Gross Primary Production, R = Ecosystem Respiration, NEP = Net Ecosystem Production.

Figure 4. A conceptual diagram depicting seasonal changes in estuarine hydrology and ecosystem function in Taylor River based on LTER and USGS data, this study, and literature review.

Everglades • No Salt • Low P

Groundwater • High Salt • High P

Florida Bay • High Salt • Low P

P

Season: Wet / Oligohaline August - January

Taylor River • Low Salt • Low P • Refractory C

Everglades • No Salt • Low P

Groundwater • High Salt • High P

P

Season: Dry / Euhaline February - July

Taylor River • High Salt • High P • Labile C

Florida Bay • High Salt • Low P

(Price et al. 2006, Koch et al. In Prep.)

(Caraco et al. 1989, Davis and Childers 2007)

(Price et al. 2006, Koch et al. In Prep.)

(Caraco et al. 1989, Davis and Childers 2007)