Area 4: Integration and Scaling Up

Objectives Progress Report Publications Show All  

Integration and Scaling Up

We use a combination of remote sensing, field investigations, and modeling to document and evaluate the consequences of long-term change and disturbance at the landscape scale.

Research Objectives

A) Disturbance-Scape

  • 4A.1 - Assess the effects of wrack perturbations
  • 4A.2 - Assess the effects of creek perturbations
  • 4A.3 - Assess the effects of dieback and other perturbations
  • 4A.4 - Synthesize results into a scaled-up disturbance-scape

B) Landscape Change

  • 4B.1 - Track habitat shifts along the Altamaha River estuary salinity gradient
  • 4B.2 - Conduct synoptic assessments of productivity
  • 4B.3 - Evaluate long-term change in vegetated marsh areas

C) Modeling

  • 4C.1 - Upgrade hydrodynamic models
  • 4C.2 - Enhance soil model
  • 4C.3 - Model plant production
  • 4C.4 - Develop driver-response models

Current Progress Report

Below is an update for each of the Area 4 objectives as reported in the most recent annual report. For a list of all reports click here (Annual Reports).

A) Produce synoptic descriptions of ecosystem properties

  • 4A.1 - Assess the effects of wrack perturbations

      Activities:  We continued monthly drone flights at the Dean Creek site, which are used to guide selection of wrack patches for field sampling. We are currently sampling a suite of variables (plants, invertebrates, porewater, temperature, decomposition) in 16 wrack patches (plus paired controls).

      Significant Results:  We have not seen differences to date in areas affected by wrack in terms of decomposition, soil organic matter, or ammonium concentrations. However, there is a significant decline in Spartina, snail, and crab density; initial observations suggest they take 7-11 months to recover.

  • 4A.2 - Assess the effects of creek perturbations

      Activities:  We plan to use the monthly drone flights (Obj. 4A1) and aerial photos (Obj. 2B2) to evaluate changes in creek configuration and creek slumping over time.

      Significant Results:  Wu et al. (2021) found sharp differences in the effects and recovery trajectories of different variables in response to headward-eroding creeks.

  • 4A.3 - Assess the effects of dieback and other perturbations

      Activities:  We identified several incipient dieback areas (areas where the plants turned prematurely brown) using the drone flights over Dean Creek and have set up plots in two areas where we are sampling the same variables as those being followed in the wrack patches (Obj. 4A1).

      Significant Results:  Hensel et al. (2021) found that hogs maintain large disturbed patches in marshes by feeding on both plants and mussels. This paper, which was in Nature Communications, has had significant news coverage, as it showed that megafauna can reduce the resilience of salt marshes.

  • 4A.4 - Synthesize results into a scaled-up disturbance-scape

      Activities:  This past year we streamlined the workflow for processing drone imagery using PCA, and also collected temperature data to calibrate the drone’s thermal band to aid in identifying wrack patches.

      Significant Results:  Regular drone flights over the airport marsh site (7 flights from Jul-2019 through May-2020) showed that most wrack is found close to the water’s edge, but it persists longer at higher elevations. Although wrack only affected ~5% of the site, wrack patches can be persistent or re-occur in the same spot repeatedly: 1/3 of the wrack-affected pixels were covered in more than 1 image (Fig. 1).

Area 4 Figure 1

Fig. 1. Distribution of wrack cover at the Airport Marsh disturbance site based on repeat drone imagery between Jul-19 and May-20. Colors indicate the number of images in which an area had wrack. Inset map shows close-up of area indicated by white box. Source: T. Lynn, D. Mishra and M. Alber.

B) Landscape Change

  • 4B.1 - Track habitat shifts along the Altamaha River estuary salinity gradient

      Activities:  We collected ground reference data in the tidal fresh forest to improve and validate our habitat classification of Sentinel-2 imagery. We also continue annual bankside surveys along the Altamaha River salinity gradient, as well as monitoring of mixed vegetation on Broughton Island.

      Significant Results:  Hierarchical clustering using ground reference data were used to create a dataset that will be used for classification of Sentinel-2 data. An initial classification using the MLC classifier was quite promising (Fig. 2).

Area 4 Figure 2

Fig. 2. Habitat classification of Sentinel-2 imagery of freshwater wetlands in the upstream portion of the Altamaha River estuary (inset). Maximum likelihood classification used all spectral bands plus the soil adjusted vegetation index (SAVI). Overall accuracy: 97.5%. Source: C. Hladik and G. Costomiris.

  • 4B.2 - Conduct synoptic assessments of productivity

      Activities:  We collected ground observations of Juncus biomass, which we are using to calibrate biomass estimates derived from Sentinel-2 imagery. This will complement our existing estimates of Spartina biomass. We are also poised to get scaled-up GPP estimates of the domain from MODIS based on our parameterized Light Use Efficiency model for S. alterniflora.

      Significant Results:  Hawman et al. (2021) evaluated the annual cycle of GPP and light use efficiency measured at the flux tower and found that the cloudiness index and daily maximum tide height were the primary factors that explained deviation in S. alterniflora light use efficiency.

  • 4B.3 - Evaluate long-term change in vegetated marsh area

      Activities:  We continued analyzing soil cores collected along elevation, salinity, and disturbance gradients, to provide insight into how shifts in vegetation and changes in creek morphology affect accretion rates and carbon accumulation.

      Significant Results:  O’Connell et al. (2021) found that belowground biomass is declining in some interior marsh areas over time, perhaps indicating areas of low marsh resiliency to sea-level rise. We also participated in a cross-site effort (Zinnert et al. 2021) that described the state changes expected in coastal wetlands in response to long-term changes in temperature and sea level (Fig. 3).

Area 4 Figure 3

Fig. 3.Response of coastal wetlands to changes in freshwater input and A) temperature, and B) sea level. Conceptual models show how variation in these parameters result in different habitat types in estuarine wetlands. Source: Zinnert et al. 2021.

C) Modeling

  • 4C.1 - Upgrade hydrodynamic models

      Activities:  We have successfully implemented the hydrodynamic, heat flux, and water quality modules of Delft3D in the GCE domain.

      Significant Results:  Output from the Delft3D hydrodynamic model indicate that variability in temperature and salinity are realistically represented (Fig. 4).

Area 4 Figure 4

Fig. 4. 119-hr low-pass filtered salinity (left) and water temperature (right) from the sondes (red) and the hydrodynamic model (black) at sites GCE1 (top), GCE4 (middle), and GCE6 (bottom). A flow proxy of 2% of Ogeechee River discharge used at the (ungauged) head of Sapelo Sound fits the salinity variation at GCE1 well. Source: J. Sheldon and R. Castelao.

  • 4C.2 - Enhance soil model

      Activities:  We are using data collected at the flux tower (Obj. 1A2) to validate and revise our soil temperature model.

      Significant Results:  The soil model simulates radiative forcing and heat propagation in the marsh subsurface.

  • 4C.3 - Model plant production

      Activities:  Our Belowground Ecosystem Resilience Model uses extreme gradient boosting to predict below-ground biomass of S. alterniflora (O’Connell et al. 2021).

      Significant Results:  The BERM model (O’Connell et al. 2021) is based on above-ground proxies and can be scaled with readily available remote sensing data to evaluate spatiotemporal patterns in belowground biomass (Fig. 5).

Area 4 Figure 5

Fig. 5. Above (left) and belowground (right) biomass of Spartina at the GCE flux tower marsh on 6/15/16 (left) and 9/15/16 (right), estimated with our Belowground Ecosystem Resilience Model. Color ramps indicates biomass in g m-2. Source: O’Connell et al. 2021.

  • 4C.4 - Develop driver-response models

      Activities:  We are using empirical mode decomposition and wavelet coherence analysis to investigate patterns in hydrological forcing to the GCE domain and assess the correspondence between them. We have also developed a series of nonlinear driver-response models in which the driver and response obey different mathematical forms.

      Significant Results:  We have analyzed the timeseries of satellite-derived marsh productivity data (Objective 2B4) and flux tower-derived net ecosystem exchange fluxes (Objective 1A2) using entropy-based approaches and methods in dynamical systems analysis to identify causal connections to timeseries reflecting environmental forcing (Fig. 6).

Area 4 Figure 6

Fig. 6. Implementation of Convergent Cross Mapping to identify environmental factors that determine net CO2 fluxes measured at the flux tower. The time series length denotes the number of data points inputted to the method. The prediction skill gives the correlation between the predicted and observed values of the variables. A causal impact is inferred when the prediction skill increases with increasing time series length. Source: K. Bice and C. Meile.

Area 4 Publications from GCE-IV

Wu, F., Ortals, C., Ruiz, J., Farrell, W.R., McNichol, S.M., Angelini, C., Spivak, A.C., Alber, M., Tong, C. and Pennings, S.C. 2022. Disturbance is complicated: headward-eroding saltmarsh creeks produce multiple responses and recovery trajectories. Limnology & Oceanography. 67:S86-S100. (DOI: 10.1002/lno.11867)

Burns, C., Alexander, C.R. Jr. and Alber, M. 2021. Assessing long-term trends in lateral salt-marsh shoreline change along a U.S. East Coast latitudinal gradient. Journal of Coastal Research. 37(2):291-301. (DOI: 10.2112/JCOASTRES-D-19-00043.1)

O'Connell, J.L., Mishra, D., Alber, M. and Byrd, K.B. 2021. BERM: A belowground ecosystem resilience model for estimating Spartina alterniflora belowground biomass. New Phytologist. (DOI: 10.1111/nph.17607)

Zinnert, J.C., Nippert, J.B., Rudgers, J.A., Pennings, S.C., Gonzalez, G., Alber, M., Baer, S.G., Blair, J.M., Burd, A.B., Collins, S.L., Craft, C.B., Di Iorio, D., Dodds, W.K., Groffman, P.M., Herbert, E., Hladik, C.M., Li, F., Litvak, M., Newsome, S., O'Donnell, J., Pockman, W.T., Schalles, J.F. and Young, D.R. 2021. State Changes: Insights from the U.S. Long Term Ecological Research Network. Ecosphere. (DOI: 10.1002/ecs2.3433)

Burns, C., Alber, M. and Alexander, C.R. Jr. 2020. Historical Changes in the Vegetated Area of Salt Marshes. Estuaries and Coasts. (DOI: https://doi.org/10.1007/s12237-020-00781-6)

Crotty, S.M., Ortals, C., Pettengill, T.M., Shi, L., Olabarrieta, M., Joyce, M.A., Altieri, A.H., Morrison, E., Bianchi, T.S., Craft, C.B., Bertness, M.D. and Angelini, C. 2020. Sea-level rise and the emergence of a keystone grazer alter the geomorphic evolution and ecology of southeast US salt marshes. PNAS. 117:17891-17902. (DOI: https://doi.org/10.1073/pnas.1917869117)

Feagin, R.A., Forbrich, I., Huff, T.P., Barr, J.G., Ruiz-Plancarte, J., Fuentes, J.D., Najjar, R., Vargas, R., Vazquez-Lule, A.L., Windham-Myers, L., Kroeger, K.D., Ward, E.J., Moore, G.W., Leclerc, M.Y., Krauss, K.W., Stagg, C.L., Alber, M., Knox, S.H., Schafer, K.V.R., Bianchi, T.S., Hutchings, J.A., Nahrawi, H.B., Noormets, A., Mitra, B., Jaimes, A., Hinson, A.L., Bergamaschi, B. and King, J.S. 2020. Tidal wetland Gross Primary Production across the continental United States, 2000-2019. Globa

Langston, A., Alexander, C.R. Jr., Alber, M. and Kirwan, M. 2020. Beyond 2100: Elevation capital disguises salt marsh vulnerability to sea-level rise in Georgia, USA. Estuarine, Coastal and Shelf Science.

O'Connell, J.L., Alber, M. and Pennings, S.C. 2020. Microspatial differences in soil temperature cause phenology change on par with long-term climate warming in salt marshes. Ecosystems. 23:498–510. (DOI: https://doi.org/10.1007/s10021-019-00418-1)

Spivak, A.C., Sanderman, J., Bowen, J.L., Canuel, E.A. and Hopkinson, C.S. 2019. Global-change controls on soil-carbon accumulation and loss in coastal vegetated ecosystems. Nature Geoscience. 12:685–692. (DOI: https://doi.org/10.1038/s41561-019-0435-2)

Miklesh, D.M. and Meile, C. 2018. Controls on porewater salinity in a Southeastern salt marsh. PeerJ. 6:e5911. (DOI: 10.7717/peerj.5911)

Vu, H. and Pennings, S.C. 2018. Predators mediate above- vs belowground herbivory in a salt marsh crab. Ecosphere. 9(2):e02107. (DOI: 10.1002/ecs2.2107)

Vu, H., Wieski, K. and Pennings, S.C. 2017. Ecosystem engineers drive creek formation in salt marshes. Ecology. 98(1):162-174.

Wang, Y., Castelao, R. and Di Iorio, D. 2017. Salinity Variability and Water Exchange in Interconnected Estuaries. Estuaries and Coasts. (DOI: 10.1007/s12237-016-0195-9)

Angelini, C., Griffin, J., van de Koppel, J., Derksen-Hooijberg, M., Lamers, L., Smolders, A.J.P., van der Heide, T. and Silliman, B.R. 2016. A keystone mutualism underpins resilience of a coastal ecosystem to drought. Nature Communications. 7:12473. (DOI: 10.1038/ncomms12473)

O'Donnell, J. and Schalles, J.F. 2016. Examination of Abiotic Drivers and Their Influence on Spartina alterniflora Biomass over a Twenty-Eight Year Period Using Landsat 5 TM Satellite Imagery of the Central Georgia Coast. Special Issue: Remote Sensing in Coastal Environments. Remote Sensing. 8(6):22. (DOI: 10.3390/rs8060477)

McFarlin, C.R., Bishop, T.D., Hester, M. and Alber, M. 2015. Context-dependent effects of the loss of Spartina alterniflora on salt marsh invertebrate communities. Estuarine, Coastal and Shelf Science. 163:218-230. (DOI: 10.1016/j.ecss.2015.05.045)

Hladik, C.M., Schalles, J.F. and Alber, M. 2013. Salt marsh elevation and habitat mapping using hyperspectral and LIDAR data. Remote Sensing of the Environment. 139:318 - 330. (DOI: 10.1016/j.rse.2013.08.003)

Pennings, S.C. and Silliman, B.R. 2005. Linking biogeography and community ecology: latitudinal variation in plant-herbivore interaction strength. Ecology. 86:2310-2319.

McKnight, C.J. 2016. A modelling study of horizontal transport and residence time in the Duplin River estuary, Sapelo Island GA. M.S. Thesis. University of Georgia, Athens, GA.

Schalles, J.F. and Peffer, C. Presentation: Regulatory, Legal, and Ethical Considerations for Drone Operations - The view from coastal Georgia . Regulatory Legal and Ethical Considerations for Drone Operations. Drones in the Coastal Zone - U.S. Southeast and Caribbean Regional Workshop, October 22, 2020, Virtual (web-based).

Schalles, J.F. Presentation: High resolution salt marsh vegetation biomass mapping with an Altum 6 band camera and Matrice 210 drone. Introduction to Using Drones in the Coastal Zone. Drones in the Coastal Zone - U.S. Southeast and Caribbean Regional Workshop, October 14, 2020, Virtual (web-based).

O'Connell, J.L., Alber, M., Mishra, D. and Byrd, K. 2020. Presentation: Structural heterogeneity in above vs belowground biomass pools differ for Spartina alterniflora monocultures, with consequences for forecasting ecosystem resiliency. Ecological Society of America.

Sheldon, J.E. and Alber, M. 2019. An Examination of High Salinity Events in the Altamaha River Estuary. In: Review prepared for GA DNR-CRD.

 
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This material is based upon work supported by the National Science Foundation under grants OCE-9982133, OCE-0620959, OCE-1237140 and OCE-1832178. Any opinions, findings, conclusions, or recommendations expressed in the material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.