Effects of natural disturbances, salinity and temperature on light-use efficiency and carbon cycling

in the subtropical mangrove forests of Everglades National Park

Jordan G. Barr1, Vic Engel2, Jose D. Fuentes3, Douglas O. Fuller4

1South Florida Natural Resource Center, Everglades National Park, Homestead FL 330302Southeast Ecological Science Center, U.S. Geological Survey, Gainesville FL 326533Department of Meteorology, Pennsylvania State University, University Park PA 168024Department of Geography and Regional Studies, University of Miami, Coral Gables, FL 33124

Outline - LUE in mangrove forests

Mangrove forest characteristicsCoastal carbon budgets and definitionsPartitioning NEE into GPP and RE Modeling light-use efficiency (LUE) and gross primary

productivity (GPP)Disturbances result in spatiotemporal changes mangrove forest

productivityConclusions

Everglades National Park

Riverine mangrove forest dominated by R. mangle, A. germinans, L. racemosa Mangrove forest structure varies across the salinity mixing zone Growth and total biomass reflect [P] gradients and peat depths (up to 6 m) Semi-diurnal tides Distinct wet and dry seasons Avg. precipitation: 160 cm yr-1 Avg. ann. air temperature: 24 C

Everglades National ParkStudy siteHeight (m) b 19.0 ± 0.2Basal area (m2 ha-1) a 39.7 Tree density (ind. ha-1) b 7450Flood frequency (floods yr-1) b ~500Flood duration (h yr-1) b 4622

(52% of time)Total:N (%) a 1.2 ± 0.1P (%) a 0.12 ± 0.01C (%) a 22.2 ± 1.2Bulk density (g cm-3) b 0.212

a Chen and Twilley, 1999b Krauss et al. 2006

Net ecosystem carbon balance (NECB)in riverine mangrove forests

NECB = -NEE + FDIC,DOC,POC (Chapin et al. 2006) NEE == Net ecosystem exchange of CO2

NECB == Net ecosystem carbon balance

Ecosystem respiration

RE response to temperature mirrors that of leaf-level carboxylation and ecosystem gross primary productivity (GPP).

𝑅𝐸=𝐴𝑐𝑡𝑖𝑣𝑎𝑡𝑖𝑜𝑛  𝐷𝑒𝑎𝑐𝑡𝑖𝑣𝑎𝑡𝑖𝑜𝑛

𝐺𝑃𝑃=𝑅𝐸−𝑁𝐸𝐸

RE declines at higher temperatures

Response functions used to estimate daytime RE to derive GPP.

Seasonal C exchange patterns

Daily GPP values reach a plateau during March to November. Lower wintertime GPP values are due to reduced temperature and day length.

Daily LUE values are maximal during September to November as a result of:1) lower PAR (30-50 mol photons m-2 day-1)

compared to levels in April to June, 2) near optimal (27 C) air temperature, and3) reduced salinity levels (10-20 ppt).

Daily PAR values are maximal in May before the onset of the wet season and are minimal during the winter solstice.

Modeling light use efficiency (LUE)

EVI from MODISmEVI = 4.03 ± 0.52

/

𝜀𝑔=𝜀𝑔𝑚𝑎𝑥× 𝑓 𝑇𝐴× 𝑓 𝑃𝐴𝑅× 𝑓 𝑆𝑎𝑙𝑖𝑛𝑖𝑡𝑦

𝐹𝐴𝑃𝐴𝑅=1−𝑒−𝑚𝐸𝑉𝐼×𝐸𝑉𝐼FAPAR

Linear decline in efficiency with increasing PAR and salinity levels.

Efficiency declines both above and below some optimal air temperature

Fraction of PAR absorbed by photosynthetic active vegetation asymptotes to 1 with increasing EVI.

Modeling LUE – advantages of Bayesian framework

The Bayesian modeling framework combines information about the data with any prior (expert) knowledge of the forcing terms.

Compute uncertainties in modeled mean LUE and variance in LUE (not shown)

Model output includes the distribution, and therefore the significance, of all forcing terms (i.e., parameters).

LUE model performance

Our LUE model:2004-2005slope = 0.720, intercept = 0.144, R2 = 0.56

2006-2011slope = 0.483, intercept = 0.249, R2 = 0.45

MODIS

Our model

LUE model conclusions

Mangrove forests exhibited a maximum light-use efficiency (quantum yield) of 3.2±0.2%.

Effect of temperature: 1) Optimal LUE at 28 C, 2) LUE declines to zero for air temperatures < 3 C and > 35 C.

Co-occurrence of high PAR (>50 mol PAR m-2 day-1) and high salinity (>30 ppt) contributed to reduced LUE during May and June.4.7% reduction in LUE per 10 ppt increase in salinity10.1% reduction in PAR per 10 mol photons m-2 day-1

increase in PAR (photo-saturation in mangrove forests)Effect of EVI on FAPAR was significant, but contributed to a

modest 20% to 30% seasonal variability in LUE.

Net ecosystem carbon balance (NECB)in riverine mangrove forests

SR mangrove forests represent outliers in terms of forest-atmosphere C exchange with -NEE of 956±175 g C m-2 per year

Mean C burial rates are 12432 gC m-2 per year (95% C.I., Breithaupt et al., 2012).

Over hundreds of years, NECB C burial rates, and NECB represents only 13% of -NEE.

Therefore, most (87% of NEE) may be exported to the estuary as DIC, DOC, and POC.

𝑁𝐸𝐶𝐵=(∑ −𝑁𝐸𝐸 )−𝐹𝐷𝐼𝐶 ,𝐷𝑂𝐶 ,𝑃𝑂𝐶

-NEE

ConclusionsMangrove forests in the Everglades sequester 956±175 g C m-2 per year.

Much of this C (80% to 90%) may eventually be exported to the estuary and coastal ocean as organic (DOC, POC) and inorganic (DIC) carbon.

Ecosystem respiration (RE) in mangrove forests obeys a temporally dynamic non-linear function of air temperature. Quantifying these patterns was essential for estimating seasonal patterns in GPP.

Air temperature, daily PAR, salinity, and EVI represent significant controls on GPP. Our Bayesian modeling framework provided uncertainty estimates of both LUE and individual forcing terms.

EVI values (in the slides that follow), determined from Landsat 5 TM, were both spatially and temporally variable during 1985 to the present. These trends will prove essential in understanding mangrove forest productivity in response to disturbances (i.e. hurricanes and cold periods), climate change, and upstream water management practices.

1985.11.02

Spatiotemporal changes in EVI following disturbances(Red is high (~1), blue is 0)

2004.01.23

2005.11.09

Defoliation following Hurricane Wilma

2006.03.01

Regrowth has notYet occurred

2009.10.19

Recovery of mangrovesAdjacent to the coast

2010.02.08

Mangrove foresttransgression

Jan 2010 cold periodDestroys inland mangroves

2011.11.10

Damage persists2 years later

Acknowledgements

J.D.F. and V.E. acknowledge support from the DOE (grant DE-FC02-06ER64298) to participate in this research. This research is also based upon work supported by the National Science Foundation through the Florida Coastal Everglades Long-Term Ecological Research program under grant number DBI-0620409.

Extra slides…

Parameter Description Mean SD 2.50% Median 97.50%e0 Optimum light-use

efficiency (mmol C (mol photons)-1)

31.8 2.2 27.7 31.6 36.7

mEVI Curvature of FAPAR response to EVI (dimensionless)

4.03 0.52 3.11 3.99 5.21

Tmin Temperature minimum (C) 2.6 0.6 1.4 2.7 3.7

Tmax Temperature maximum (C) 33.5 0.6 32.4 33.5 34.8

Topt Temperature optimum (C) 27.8 0.3 27.2 27.8 28.5msal Salinity forcing

(dimensionless) 0.0047 0.0022 0.0000 0.0048 0.0084

mPAR PAR-saturation forcing 0.0101 0.0004 0.0092 0.0102 0.0110

FiguresAnnual GPP vs RE

The DEM's for the state of Florida were developed by staff at NOAA's Coastal Services Center, Charleston, SC. The DEMs were derived from a variety of lidar data, which can be obtained via the Digital Coast website (csc.noaa.com/digitalcoast). The data are broken down by NOAA National Weather Service Weather Forecast Office boundaries, as follows:Miami (MFL) MFL_1_GCS_NAVDm.img Collier, Monroe (excluding Keys)

The DEMs are provided in geographic coordinates (NAD83) and vertically referenced to NAVD88. Vertical units for all data are meters.

Portions of some DEMs may have data voids due to the fact no lidar data was available for the area at the time of development. Check the Digital Coast for our latest lidar holdings.

Any questions/concerns regarding the DEMs or derived products used in the Sea Level Rise and Coastal impacts Viewer, please contact:Doug Marcy, doug.marcy@noaa.gov, 843-740-1334

Magenta: -0.45 mBlue: -0.1 mCyan: +0.1 mGreen: 0.65 mYellow: 1.0 mRed: 1.4 m

EC flux tower