Barr et al Ecosummit presentation Oct 2012, Columbus OH


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Remote sensing of mangrove forest productivity
Ecosummit, Columbus OH, Oct 2012

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  • Not all mangroves are created equally.
  • Mangrove ecosystem respiration is dynamic in that it reaches a peak followed by deactivation and the response to air temperature changes with time. These features must be considered when deriving GPP estimates. GPP estimates will then be used to constrain ecosystem models of mangrove productivity.
  • Seasonal C exchange patterns1) Seasonal trends in GPP and LUE controlled by daily PAR levels2) Lower GPP and LUE result from lower wintertime temperature3) Changes in GPP could alter the delicate balance of NECB and maintenance of NECB necessary for soils to accrete C at the pace of SLR.
  • Modeling LUE – LUE is essential for quantifying GPP and therefore annual NEE. The magnitude of NEE in turn controls partitioning of C into NECB (as burial or soil accretion) and total export as DIC, DOC, and POC.In summary, long-term sustainability of mangrove forests along the coasts depends on sufficient productivity for maintaining positive NECB and soil levels keeping pace with rising sea level.
  • Photo-saturation is currently not included in most satellite-based models of productivity. Yet, photo-saturation is an important process in mangrove forests.Salinity is important but modest control on LUE. This shows the resilience of mangroves to seasonally variable salinity levels (at least up to seawater levels).
  • If NEE changes as a result of altered productivity (as expressed in GPP), then the partitioning as C burial could change.Rates of C burial are critical to surface elevations keeping pace with sea level.
  • Barr et al Ecosummit presentation Oct 2012, Columbus OH

    1. 1. Jordan G. Barr1, Vic Engel2, Jose D. Fuentes3, Douglas O. Fuller4 1South Florida Natural Resource Center, Everglades National Park, Homestead FL 33030 2Southeast Ecological Science Center, U.S. Geological Survey, Gainesville FL 32653 3Department of Meteorology, Pennsylvania State University, University Park PA 16802 4Department of Geography and Regional Studies, University of Miami, Coral Gables, FL 33124
    2. 2. Outline - LUE in mangrove forests Mangrove forest characteristics Coastal carbon budgets and definitions Partitioning NEE into GPP and RE Modeling light-use efficiency (LUE) and gross primary productivity (GPP) Disturbances result in spatiotemporal changes mangrove forest productivity Conclusions
    3. 3. 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
    4. 4. Everglades National Park Study site Height (m) b 19.0 0.2 Basal area (m2 ha-1) a 39.7 Tree density (ind. ha-1)b 7450 Flood frequency (floods yr-1)b ~500 Flood duration (h yr-1)b 4622 (52% of time) Total: N (%) a 1.2 0.1 P (%) a 0.12 0.01 C (%) a 22.2 1.2 Bulk density (g cm-3)b 0.212 a Chen and Twilley, 1999 b Krauss et al. 2006
    5. 5. 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
    6. 6. Ecosystem respiration  RE response to temperature mirrors that of leaf-level carboxylation and ecosystem gross primary productivity (GPP).  RE declines at higher temperatures  Response functions used to estimate daytime RE to derive GPP.
    7. 7. 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, and 3) 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.
    8. 8. Modeling light use efficiency (LUE) EVI from MODIS mEVI = 4.03 ± 0.52 FAPAR 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.
    9. 9. 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).
    10. 10. LUE model performance Our LUE model: 2004-2005 slope = 0.720, intercept = 0.144, R2 = 0.56 2006-2011 slope = 0.483, intercept = 0.249, R2 = 0.45 MODIS Our model
    11. 11. 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 salinity 10.1% 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.
    12. 12. 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 12432 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. -NEE
    13. 13. Conclusions Mangrove 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.
    14. 14. 1985.11.02 Spatiotemporal changes in EVI following disturbances (Red is high (~1), blue is 0)
    15. 15. 2004.01.23
    16. 16. 2005.11.09 Defoliation following Hurricane Wilma
    17. 17. 2006.03.01 Regrowth has not Yet occurred
    18. 18. 2009.10.19 Recovery of mangroves Adjacent to the coast
    19. 19. 2010.02.08 Mangrove forest transgression Jan 2010 cold period Destroys inland mangroves
    20. 20. 2011.11.10 Damage persists 2 years later
    21. 21. 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.
    22. 22. 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 28.5 msal 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
    23. 23. Figures Annual GPP vs RE
    24. 24. 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 ( 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,, 843-740-1334
    25. 25. Magenta: -0.45 m Blue: -0.1 m Cyan: +0.1 m Green: 0.65 m Yellow: 1.0 m Red: 1.4 m
    26. 26. EC flux tower