Area 4: Integration and Scaling Up

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 processed monthly drone flights to identify wrack patches at both the Dean Creek and Belle Marsh sites and completed field sampling of wrack and paired control areas at Dean Creek (plants, invertebrates, porewater, temperature, decomposition).

  Significant Results:  Lynn et al. (2023) found that most wrack patches are mobile (~1 month in any one spot) and accumulate most frequently at high tide lines. Interestingly, wrack was closer to water after higher tides (Fig. 1).

  

  Fig. 1. Relationship between the maximum tide height of the previous 30 days and median distance to water of wrack patches. Source: Lynn et al. 2023.

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

  Activities:  We tracked slump block dynamics in monthly drone images of Dean Creek (Obj 4A1), conducted field, sidescan sonar and LIDAR surveys of slump blocks, and began monthly field measurements of slump block elevation. We also conducted predation assays behind slump blocks (Fig.2).

  Significant Results:  Yang et al. (subm.) found that slump blocks are actively moving and either submerge or reconnect to the bank (Fig 3), and that block loss accounted for 66% of channel widening observed in the study area over one year.

Fig. 2. Creek slump blocks identified from aerial photos of Belle Marsh on the Duplin R. Note that slumps identified on the creek edge in 2013 had disappeared by 2018 as the creek widened. Source: Yang et al. (in prep).

Fig. 3. Classification of slump blocks in drone imagery. Top row: Formation of a new slump block, which was first observed in 5/20. Row2: An example of a persistent block, which was consistently observed between 9/20 and 3/21. Row 3: Changes in a slump block that submerged between 10/20 and 12/10. Row 4: A block that reconnected to the marsh platform. Source: Yang et al. in review

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

  Activities:  We continued field measurements in dieback areas, where we sample the same variables as in the wrack patches (Obj 4A1).

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

  Activities:  We are combining field and drone data to scale up the effects of wrack disturbance to the landscape scale. We are also using biomass data calibrated to the drone imagery to analyze recovery.

  Significant Results:  Repeat drone imagery allowed us to identify “disturbance” wrack, which remains in place for > x months (Fig. 4). Initial results show that wrack patches that were in place for longer periods of time resulted in a higher percentage of plant biomass loss.

Fig. 4. Area of wrack in monthly drone flights at the Dean Creek site, classified as ephemeral (present < 2 mo) or persistent (> 2 mo). Persistent wrack comprised 30% of the total. Source: M. Pierce and T. Lynn.

B) Landscape Change

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

  Activities:  We continue annual bankside surveys of Spartina and sentinel trees along the Altamaha River salinity gradient and monitoring of mixed vegetation sites. This year we compared random forest and a neural network approach to classify tidal marsh habitat along the Altamaha River and conducted a change analysis from 2017-2022. We also performed a community analysis of Altamaha tidal fresh forest vegetation based on hierarchical clustering of extensive ground data collected in 2021.

  Significant Results:  Pudil (2023) found evidence for fine-scale variation in species composition of Altamaha marsh plants, particularly on Broughton Island (Fig. 5). Costomiris (2022) identified 6 distinct freshwater forest communities and found that plot-level species composition was significantly associated with longitude (a proxy for distance upstream).

Fig. 5. Random forest classification of vegetation at the Broughton Island site. Source: C. Hladik.

• 4B.2 - Conduct synoptic assessments of productivity

  Activities:  We partitioned net ecosystem exchange (NEE) measured at the flux tower (Obj 1A2) into gross primary production and ecosystem respiration. We are also scaling up our belowground biomass model, which estimates Spartina above and belowground biomass, foliar N, leaf area index, and chlorophyll (Obj 4C3). We continued to process a new atmospheric-correction Landsat TM and OLI product that is improving our ability to compare Spartina and Juncus biomass estimates from 1984-present for the entire Georgia coast.

  Significant Results:  GPP estimates at the flux tower show strong differences between tall Spartina at the creekbanks and short Spartina found in the marsh interior (Fig. 6).

Fig. 6. a) Interannual comparison of gross primary production (GPP) at “marsh edge” areas, composed of tall form S. alterniflora associated with tidal creek edges, and “marsh interior”, composed of short form S. alterniflora found on the marsh platform. b) Map displaying the average annual flux footprint climatologies for fluxes measured from each canopy zone: “mash edge” (black) and “marsh interior” (white). Source: Hawman et al., in review.

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

  Activities:  We are working with colleagues in SC to optimize the UVVR (unvegetated:vegetated marsh ratio) for southeastern marshes, which provides information on vulnerability.

  Significant Results:  Mariotti et al (2023) found that despite substantial progradation and erosion since 1850, there has been very little net change in total marsh area along GA's coast.

C) Modeling

• 4C.1 - Upgrade hydrodynamic models

  Activities:  We are using passive tracers in Delft3D to quantify residence time in the estuary and its sensitivity to variations in forcing. We are also upgrading to a newer version of the model with a flexible mesh.

  Significant Results:  Model-predicted residence time is being used to help explain observed changes in DOC content in the estuary following the passage of storms.

• 4C.2 - Enhance soil model

  Activities:  We continue work on a mechanistic model of marsh soil temperature and are using observations from 4 flux towers along the east coast to produce a soil temperature model based on remote sensing and gridded climate data.

  Significant Results:  Our remote sensing model can predict tidal marsh soil temperature with an average error of 1.5°C, which captures elevation-related temperature variation (Fig. 7).

Fig. 7. Spatial prediction of hourly soil temperature using a remote sensing and climate data-based machine learning model. Predictions have a 10 m spatial resolution. White dashed line is a transect showing b) predicted temperature variation across elevation during a low tide and high tide period. Source: D. Mishrak and R. Sharma.

• 4C.3 - Model plant production

  Activities:  We took quarterly measurements of plant characteristics at 6 sites to use as training data for our belowground biomass model (BERM).

  Significant Results:  We have found that flooding and elevation are important predictors of Spartina belowground biomass and that spatial changes in these variables are more important than temporal changes over the course of a year. In addition, Jung and Burd (subm.) found that Spartina allocates different proportions of its productivity to belowground production depending on its height form.

• 4C.4 - Develop driver-response models

  Activities:  We used wavelet coherence and empirical dynamic modeling to examine causal connections between environmental drivers and ecosystem properties.

  Significant Results:  Bice et al. (2023) used empirical dynamic modeling to show that temperature, river discharge, drought, sea level, and river nutrient concentrations were causally connected to salt marsh biomass. A similar analysis of the flux tower timeseries data identified radiation and soil temperature as causal variables for NEE (Obj 1A2).

Area 4 Publications from GCE-IV

Costomiris, G., Hladik, C.M. and Craft, C.B. 2024. Multivariate Analysis of the Community Composition of Tidal Freshwater Forests on the Altamaha River, Georgia. Special Issue: Coastal Forest Dynamics and Coastline Erosion—Series II. Forests. 15(1). (DOI: 10.3390/f15010200)

Lehmann, M.K., Gurlin, D., Pahlevan, N., Binding, C., Fichot, C., Gitelson, A., Mishra, D., Schalles, J.F., Simis, S., Smith, B. and Spyrakos, E. 2023. GLORIA - A globally representative hyperspectral in situ dataset for optical sensing of water quality. Nature - Scientific Data. 10:1130958, 6 April 2023(100 (2023)):13 p. (DOI: doi.org/10.1038/s41597-023-01973-y)

Robinson, M., Alexander, C.R. Jr. and Venherm, C. 2022. Shallow Water Estuarine Mapping in High-Tide-Range Environments: A Case Study from Georgia, USA. Special Issue: Shallow Water Mapping. Estuaries and Coasts. 45:980-999. (DOI: https://doi.org/10.1007/s12237-021-01032-y)

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)

Schaeffer, B., Neely, M., Spinosa, A., Serafy, E., Odermatt, D., Weathers, K., Barracchini, T., Bouffard, D., Carvalho, L., Comny, R., De Keukelaere, P., Hunter, P., Jamet, C., Joehnk, K., Johnston, J., Knudby, A., Minaudo, C., Pahlevan, N., Rose, K., Schalles, J.F. and Tzortziou, M. 2021. Integrating inland and coastal water quality data for actionable knowledge. Special Issue: Big Earth Data and Remote Sensing in Coastal Environments. Remote Sensing. 13; 23 July 2021(15):24 p. (DOI: doi.org/10

Wu, F., Pennings, S.C., Ortals, C., Ruiz, J., Farrell, W.R., McNichol, S.M., Angelini, C., Spivak, A.C., Alber, M. and Tong, C. 2021. Disturbance is complicated: headward-eroding saltmarsh creeks produce multiple responses and recovery trajectories. Limnology & Oceanography. 67:S86-S100. (DOI: 10.1002/lno.11867)

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)

Burns, C., Alexander, C.R. Jr. and Alber, M. 2020. 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)

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.