Determining Early Warning Thresholds to Detect Tree Mortality Risk in a Southeastern U.S. Bottomland Hardwood Wetland
Abstract
1. Introduction
2. Materials and Methods
2.1. The Study Site
2.2. Flux Measurements
2.3. The Key Drivers of Tree Mortality
2.4. Tree MortalityDetermination
2.5. Data Analysis
3. Results
3.1. Hydrology and Tree Mortality Trends
3.2. Temporal Trends of Carbon Fluxes
3.3. Groundwater Table as a Dominant Driver of Carbon Fluxes
3.4. Tree Mortality
3.5. Predictive Modeling of Tree Mortality
3.6. Thresholds for Early Warning
4. Discussion
4.1. Temporal Trends in Carbon Fluxes
4.2. Hydrological Impacts on Carbon Fluxes
4.3. Groundwater Table as the Key Driver in Carbon Fluxes
4.4. Interannual Tree Mortality
4.5. Tree Species Composition and Mortality
4.6. Key Predictors of Mortality
4.7. Threshold-Based Early Warning System
4.8. Implications for Ecosystem Management
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Intergovernmental Panel on Climate Change (IPCC). Climate Change 2021—The Physical Science Basis; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2023; ISBN 9781009157896. [Google Scholar]
- Kirwan, M.L.; Megonigal, J.P. Tidal wetland stability in the face of human impacts and sea-level rise. Nature 2013, 504, 53–60. [Google Scholar] [CrossRef]
- Krauss, K.W.; Duberstein, J.A.; Doyle, T.W.; Conner, W.H.; Day, R.H.; Inabinette, L.W.; Whitbeck, J.L. Site condition, structure, and growth of baldcypress along tidal/non-tidal salinity gradients. Wetlands 2009, 29, 505–519. [Google Scholar] [CrossRef]
- Herbert, E.R.; Boon, P.; Burgin, A.J.; Neubauer, S.C.; Franklin, R.B.; Ardon, M.; Hopfensperger, K.N.; Lamers, L.P.M.; Gell, P.; Langley, J.A. A global perspective on wetland salinization: Ecological consequences of a growing threat to freshwater wetlands. Ecosphere 2015, 6, 206. [Google Scholar] [CrossRef]
- Langston, A.K.; Kaplan, D.A.; Putz, F.E. A casualty of climate change? Loss of freshwater forest islands on Florida’s Gulf Coast. Glob. Change Biol. 2017, 23, 5383–5397. [Google Scholar] [CrossRef] [PubMed]
- Ardón, M.; Morse, J.L.; Colman, B.P.; Bernhardt, E.S. Drought-induced saltwater incursion leads to increased wetland nitrogen export. Glob. Change Biol. 2013, 19, 2976–2985. [Google Scholar] [CrossRef] [PubMed]
- Martinez, M.; Ardón, M. Drivers of greenhouse gas emissions from standing dead trees in ghost forests. Biogeochemistry 2021, 154, 471–488. [Google Scholar] [CrossRef]
- Schieder, N.W.; Kirwan, M.L. Sea-level driven acceleration in coastal forest retreat. Geology 2019, 47, 1151–1155. [Google Scholar] [CrossRef]
- Donato, D.C.; Kauffman, J.B.; Murdiyarso, D.; Kurnianto, S.; Stidham, M.; Kanninen, M. Mangroves among the most carbon-rich forests in the tropics. Nat. Geosci. 2011, 4, 293–297. [Google Scholar] [CrossRef]
- McLeod, E.; Chmura, G.L.; Bouillon, S.; Salm, R.; Björk, M.; Duarte, C.M.; Lovelock, C.E.; Schlesinger, W.H.; Silliman, B.R. A blueprint for blue carbon: Toward an improved understanding of the role of vegetated coastal habitats in sequestering CO2. Front. Ecol. Environ. 2011, 9, 552–560. [Google Scholar] [CrossRef]
- Dakos, V.; Matthews, B.; Hendry, A.P.; Levine, J.; Loeuille, N.; Norberg, J.; Nosil, P.; Scheffer, M.; De Meester, L. Ecosystem tipping points in an evolving world. Nat. Ecol. Evol. 2019, 3, 355–362. [Google Scholar] [CrossRef]
- Poulter, B.; Goodall, J.L.; Halpin, P.N. Applications of network analysis for adaptive management of artificial drainage systems in landscapes vulnerable to sea level rise. J. Hydrol. 2008, 357, 207–217. [Google Scholar] [CrossRef]
- Kirwan, M.L.; Gedan, K.B. Sea-level driven land conversion and the formation of ghost forests. Nat. Clim. Change 2019, 9, 450–457. [Google Scholar] [CrossRef]
- Blankespoor, B.; Dasgupta, S.; Laplante, B. Sea-Level Rise and Coastal Wetlands. Ambio 2014, 43, 996–1005. [Google Scholar] [CrossRef]
- Spencer, T.; Schuerch, M.; Nicholls, R.J.; Hinkel, J.; Lincke, D.; Vafeidis, A.T.; Reef, R.; McFadden, L.; Brown, S. Global coastal wetland change under sea-level rise and related stresses: The DIVA Wetland Change Model. Glob. Planet. Change 2016, 139, 15–30. [Google Scholar] [CrossRef]
- Schuerch, M.; Spencer, T.; Temmerman, S.; Kirwan, M.L.; Wolff, C.; Lincke, D.; McOwen, C.J.; Pickering, M.D.; Reef, R.; Vafeidis, A.T.; et al. Future response of global coastal wetlands to sea-level rise. Nature 2018, 561, 231–234. [Google Scholar] [CrossRef] [PubMed]
- Lovelock, C.E.; Reef, R. Variable Impacts of Climate Change on Blue Carbon. One Earth 2020, 3, 195–211. [Google Scholar] [CrossRef]
- Yu, M.; Rivera-Ocasio, E.; Heartsill-Scalley, T.; Davila-Casanova, D.; Rios-López, N.; Gao, Q. Landscape-Level Consequences of Rising Sea-Level on Coastal Wetlands: Saltwater Intrusion Drives Displacement and Mortality in the Twenty-First Century. Wetlands 2019, 39, 1343–1355. [Google Scholar] [CrossRef]
- Noormets, A. AmeriFlux BASE US-NC4 NC_AlligatorRiver (Dataset), version 5-5; AmeriFlux AMP: Berkeley, CA, USA, 2022. [Google Scholar]
- Mitra, B.; Miao, G.; Minick, K.; McNulty, S.G.; Sun, G.; Gavazzi, M.; King, J.S.; Noormets, A. Disentangling the Effects of Temperature, Moisture, and Substrate Availability on Soil CO2 Efflux. J. Geophys. Res. Biogeosci. 2019, 124, 2060–2075. [Google Scholar] [CrossRef]
- Miao, G.; Noormets, A.; Domec, J.C.; Fuentes, M.; Trettin, C.C.; Sun, G.; McNulty, S.G.; King, J.S. Hydrology and microtopography control carbon dynamics in wetlands: Implications in partitioning ecosystem respiration in a coastal plain forested wetland. Agric. For. Meteorol. 2017, 247, 343–355. [Google Scholar] [CrossRef]
- Vickers, D.; Mahrt, L. Quality control and flux sampling problems for tower and aircraft data. J. Atmos. Ocean. Technol. 1997, 14, 512–526. [Google Scholar] [CrossRef]
- Wilczak, J.M.; Oncley, S.P.; Stage, S.A. Sonic anemometer tilt correction algoriths. Bound.-Layer Meteorol. 2001, 99, 127–150. [Google Scholar] [CrossRef]
- Webb, K.; Pearman, G.I.; Leuning, R. Correction of flux measurements for density effects due to heat and water vapour transfer. Q. J. R. Meteorol. Soc. 1980, 106, 85–100. [Google Scholar] [CrossRef]
- Ibrom, A.; Dellwik, E.; Flyvbjerg, H.; Jensen, N.O.; Pilegaard, K. Strong low-pass filtering effects on water vapour flux measurements with closed-path eddy correlation systems. Agric. For. Meteorol. 2007, 147, 140–156. [Google Scholar] [CrossRef]
- Moncrieff, J.; Clement, R.; Finnigan, J.; Meyers, T. Averaging, detrending, and filtering of eddy covariance time series. In Handbook of Micrometeorology: A Guide for Surfarce Flux Measurement; Lee, X., Massman, W., Law, B., Eds.; Springer: Dordrecht, The Netherlands, 2004; ISBN 1402022654. [Google Scholar]
- Mauder, T.; Foken, T. Impact of post-field data processing on eddy covariance flux estimates and energy balance closure. Meteorol. Z. 2006, 15, 597–609. [Google Scholar] [CrossRef]
- Moreno-Martínez, Á.; Izquierdo-Verdiguier, E.; Maneta, M.P.; Camps-Valls, G.; Robinson, N.; Muñoz-Marí, J.; Sedano, F.; Clinton, N.; Running, S.W. Multispectral high resolution sensor fusion for smoothing and gap-filling in the cloud. Remote Sens. Environ. 2020, 247, 111901. [Google Scholar] [CrossRef] [PubMed]
- Martínez-Ferrer, L.; Moreno-Martínez, Á.; Campos-Taberner, M.; García-Haro, F.J.; Muñoz-Marí, J.; Running, S.W.; Kimball, J.; Clinton, N.; Camps-Valls, G. Quantifying uncertainty in high resolution biophysical variable retrieval with machine learning. Remote Sens. Environ. 2022, 280, 113199. [Google Scholar] [CrossRef]
- Woodall, C.W.; Monleon, V.J. Sampling Protocol, Estimation, and Analysis Procedures for the Down Woody Materials Indicator of the FIA Program; Gen. Tech. Rep. NRS-22; US Department of Agriculture, Forest Service, Northern Research Station: Newtown Square, PA, USA, 2008; 68p.
- Lewis, R.R. Ecological engineering for successful management and restoration of mangrove forests. Ecol. Eng. 2005, 24, 403–418. [Google Scholar] [CrossRef]
- Sheil, D.; May, R. Mortality and Recruitment Rate Evaluations in Heterogeneous Tropical Forests. J. Ecol. 1996, 84, 91–100. [Google Scholar] [CrossRef]
- Kendall, M.G. Rank Correlation Methods, 4th ed.; Charles Griffin: London, UK, 1975. [Google Scholar]
- Rauf, A.U.; Rafi, M.S.; Ali, I.; Muhammad, U.W. Temperature trend detection in upper Indus basin by using Mann-Kendall test. Adv. Sci. Technol. Eng. Syst. 2016, 1, 5–13. [Google Scholar] [CrossRef]
- Yue, S.; Pilon, P.; Cavadias, G. Power of the Mann-Kendall and Spearman’s rho tests for detecting monotonic trends in hydrological series. J. Hydrol. 2002, 259, 254–271. [Google Scholar] [CrossRef]
- Barton, K. Package MuMIn: Multi-Model Inference (version 1.46.0). R Package 2022. The Comprehensive R Archive Network (CRAN). Available online: https://CRAN.R-project.org/package=MuMIn (accessed on 15 January 2025).
- Robin, X.; Turck, N.; Hainard, A.; Tiberti, N.; Lisacek, F.; Sanchez, J.-C.; Mueller, M. pROC: An open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinform. 2011, 8, 12–77. [Google Scholar] [CrossRef]
- Wickham, H. ggplot2: Elegant Graphics for Data Analysis; Springer: New York, NY, USA, 2016; ISBN 978-3-319-24277-4. [Google Scholar]
- Sievert, C. Interactive Web-Based Data Visualization with R, Plotly, and Shiny; Chapman and Hall/CRC: Boca Raton, FL, USA, 2020; ISBN 9781138331457. [Google Scholar]
- Wickham, H.; Averick, M.; Bryan, J.; Chang, W.; McGowan, L.; François, R.; Grolemund, G.; Hayes, A.; Henry, L.; Hester, J.; et al. Welcome to the Tidyverse. J. Open Source Softw. 2019, 4, 1686. [Google Scholar] [CrossRef]
- Wickham, H. Reshaping Data with the reshape Package. J. Stat. Softw. 2007, 12, 1–20. [Google Scholar]
- R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2023. [Google Scholar]
- Lai, D.Y.F.; Roulet, N.T.; Moore, T.R. The spatial and temporal relationships between CO2 and CH4 exchange in a temperate ombrotrophic bog. Atmos. Environ. 2014, 89, 249–259. [Google Scholar] [CrossRef]
- Knox, S.H.; Sturtevant, C.; Matthes, J.H.; Koteen, L.; Verfaillie, J.; Baldocchi, D. Agricultural peatland restoration: Effects of land-use change on greenhouse gas (CO2 and CH4) fluxes in the Sacramento-San Joaquin Delta. Glob. Change Biol. 2015, 21, 750–765. [Google Scholar] [CrossRef] [PubMed]
- Morin, T.; Bohrer, R.; Frasson, L.; Naor-Azreli, L.; Mesi, S.; Stefanik, K.; Schafer, K. Environmental drivers of methane fluxes from an urban temperate wetland park. J. Geophys. Res. Biogeosci. 2014, 119, 2188–2208. [Google Scholar] [CrossRef]
- Chimner, R.A.; Cooper, D.J. Influence of water table levels on CO2 emissions in a Colorado subalpine fen: An in situ microcosm study. Soil Biol. Biochem. 2003, 35, 345–351. [Google Scholar] [CrossRef]
- Mitsch, W.J.; Bernal, B.; Nahlik, A.M.; Mander, Ü.; Zhang, L.; Anderson, C.J.; Jørgensen, S.E.; Brix, H. Wetlands, carbon, and climate change. Landsc. Ecol. 2013, 28, 583–597. [Google Scholar] [CrossRef]
- Davidson, E.A.; Janssens, I.A. Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature 2006, 440, 165–173. [Google Scholar] [CrossRef]
- Chang, K.Y.; Riley, W.J.; Knox, S.H.; Jackson, R.B.; McNicol, G.; Poulter, B.; Aurela, M.; Baldocchi, D.; Bansal, S.; Bohrer, G.; et al. Substantial hysteresis in emergent temperature sensitivity of global wetland CH4 emissions. Nat. Commun. 2021, 12, 2266. [Google Scholar] [CrossRef]
- Zhu, K.; Jia, W.; Mei, Y.; Wu, S.; Huang, P. Shift from flooding to drying enhances the respiration of soil aggregates by changing microbial community composition and keystone taxa. Front. Microbiol. 2023, 14, 1167353. [Google Scholar] [CrossRef] [PubMed]
- Ding, J.; McDowell, N.; Fang, Y.; Ward, N.; Kirwan, M.L.; Regier, P.; Megonigal, P.; Zhang, P.; Zhang, H.; Wang, W.; et al. Modeling the mechanisms of conifer mortality under seawater exposure. New Phytol. 2023, 239, 1679–1691. [Google Scholar] [CrossRef] [PubMed]
- Sacatelli, R.; Kaplan, M.; Carleton, G.; Lathrop, R.G. Coastal Forest Dieback in the Northeast USA: Potential Mechanisms and Management Responses. Sustainability 2023, 15, 6346. [Google Scholar] [CrossRef]
- Bridgham, S.D.; Megonigal, J.P.; Keller, J.K.; Bliss, N.B.; Trettin, C. The carbon balance of North American wetlands. Wetlands 2006, 26, 889–916. [Google Scholar] [CrossRef]
- Laiho, R. Decomposition in peatlands: Reconciling seemingly contrasting results on the impacts of lowered water levels. Soil Biol. Biochem. 2006, 38, 2011–2024. [Google Scholar] [CrossRef]
- Bubier, J.; Crill, P.; Mosedale, A.; Frolking, S.; Linder, E. Peatland responses to varying interannual moisture conditions as measured by automatic CO2 chambers. Glob. Biogeochem. Cycles 2003, 17, 1066. [Google Scholar] [CrossRef]
- Freeman, C.; Ostle, N.; Kang, H. An enzymatic latch on global carbon store. Nature 2001, 409, 149. [Google Scholar] [CrossRef]
- Lasslop, G.; Reichstein, M.; Papale, D.; Richardson, A.; Arneth, A.; Barr, A.; Stoy, P.; Wohlfahrt, G. Separation of net ecosystem exchange into assimilation and respiration using a light response curve approach: Critical issues and global evaluation. Glob. Change Biol. 2010, 16, 187–208. [Google Scholar] [CrossRef]
- Baldocchi, D. “Breathing” of the terrestrial biosphere: Lessons learned from a global network of carbon dioxide flux measurement systems. Aust. J. Bot. 2008, 56, 1–26. [Google Scholar] [CrossRef]
- Fan, Y.; Miguez-Macho, G.; Jobbágy, E.G.; Jackson, R.B.; Otero-Casal, C. Hydrologic regulation of plant rooting depth. Proc. Natl. Acad. Sci. USA 2017, 114, 10572–10577. [Google Scholar] [CrossRef]
- Kozlowski, T.T. Responses of woody plants to flooding and salinity. Tree Physiol. 1997, 17, 490. [Google Scholar] [CrossRef]
- Williams, K.; Ewel, K.C.; Stumpf, R.P.; Putz, F.E.; Workman, T.W. Sea-level rise and coastal forest retreat on the west coast of Florida, USA. Ecology 1999, 80, 2045–2063. [Google Scholar] [CrossRef]
- Allen, C.D.; Macalady, A.K.; Chenchouni, H.; Bachelet, D.; McDowell, N.; Vennetier, M.; Kitzberger, T.; Rigling, A.; Breshears, D.D.; Hogg, E.H.; et al. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. For. Ecol. Manag. 2010, 259, 660–684. [Google Scholar] [CrossRef]
- Conner, W.; Doyle, T.; Krauss, K. Ecology of Tidal Freshwater Forested Wetlands of the Southeastern United States; Springer: Dordrecht, The Netherlands, 2007; ISBN 9781402050947. [Google Scholar]
- Mccarron, J.K.; Mcleod, K.W.; Conner, W.H. Flood and salinity stress of wetland woody species, Buttonbush (Cephalanthus occidentalis) and Swamp tupelo (Nyssa sylvatica vail. biflora). Wetlands 1998, 8, 165–175. [Google Scholar] [CrossRef]
- White, E.; Kaplan, D. Restore or retreat? saltwater intrusion and water management in coastal wetlands. Ecosyst. Health Sustain. 2017, 3, e01258. [Google Scholar] [CrossRef]
- Elsayed, S.M.; Oumeraci, H. Modelling and mitigation of storm-induced saltwater intrusion: Improvement of the resilience of coastal aquifers against marine floods by subsurface drainage. Environ. Model. Softw. 2018, 100, 252–277. [Google Scholar] [CrossRef]
- Yu, X.; Yang, J.; Graf, T.; Koneshloo, M.; O’Neal, M.A.; Michael, H.A. Impact of topography on groundwater salinization due to ocean surge inundation. Water Resour. Res. 2016, 52, 5794–5812. [Google Scholar] [CrossRef]
- Liu, F.; Liu, H.; Adalibieke, W.; Peng, Z.; Liang, B.; Feng, S.; Shi, L.; Zhu, X. Decline in stability of forest productivity in the tropics as determined by canopy water content. iScience 2023, 26, 107211. [Google Scholar] [CrossRef] [PubMed]
- Doyle, T.W.; Krauss, K.W.; Conner, W.H.; From, A.S. Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise. For. Ecol. Manag. 2010, 259, 770–777. [Google Scholar] [CrossRef]
- Conner, W.; McLeod, K.; McCarron, J. Flooding and salinity effects on growth and survival of four common. Wetl. Ecol. Manag. 1997, 5, 99–109. [Google Scholar] [CrossRef]
- Scheffer, M.; Bascompte, J.; Brock, W.A.; Brovkin, V.; Carpenter, S.R.; Dakos, V.; Held, H.; Van Nes, E.H.; Rietkerk, M.; Sugihara, G. Early-warning signals for critical transitions. Nature 2009, 461, 53–59. [Google Scholar] [CrossRef]
- Lovelock, C.E.; Feller, I.C.; Reef, R.; Hickey, S.; Ball, M.C. Mangrove dieback during fluctuating sea levels. Sci. Rep. 2017, 7, 1680. [Google Scholar] [CrossRef] [PubMed]
- Doyle, T.W.; Keeland, B.D.; Gorham, L.E.; Johnson, D.J.; Louisiana, S.; Doyle, T.W.; Keeland, B.D.; Gorham, L.E.; Johnson, D.J. Structural Impact of Hurricane Andrew on the Forested Wetlands of the Atchafalaya Basin in South Louisiana. J. Coast. Res. 1992, 21 (Special Issue No. 21), 354–364. Available online: http://www.jstor.org/stable/25736020 (accessed on 30 May 2025).
- Jayakaran, A.D.; Williams, T.M.; Ssegane, H.; Amatya, D.M.; Song, B.; Trettin, C.C. Hurricane impacts on a pair of coastal forested watersheds: Implications of selective hurricane damage to forest structure and streamflow dynamics. Hydrol. Earth Syst. Sci. 2014, 18, 1151–1164. [Google Scholar] [CrossRef]
- Chapman, E.L.; Chambers, J.Q.; Ribbeck, K.F.; Baker, D.B.; Tobler, M.A.; Zeng, H.; White, D.A. Hurricane Katrina impacts on forest trees of Louisiana’s Pearl River basin. For. Ecol. Manag. 2008, 256, 883–889. [Google Scholar] [CrossRef]
- Ouyang, Y.; Grace, J.M.; Parajuli, P.B.; Caldwell, P.V. Impacts of Multiple Hurricanes and Tropical Storms on Watershed Hydrological Processes in the Florida Panhandle. Climate 2022, 10, 42. [Google Scholar] [CrossRef]
- Zhou, X.; Lei, X.; Peng, C.; Wang, W.; Zhou, C.; Liu, C.; Liu, Z. Correcting the overestimate of forest biomass carbon on the national scale. Methods Ecol. Evol. 2016, 7, 447–455. [Google Scholar] [CrossRef]
- Anderegg, W.R.L.; Trugman, A.T.; Badgley, G.; Anderson, C.M.; Bartuska, A.; Ciais, P.; Cullenward, D.; Field, C.B.; Freeman, J.; Goetz, S.J.; et al. Climate-driven risks to the climate mitigation potential of forests. Science 2020, 368, 8247. [Google Scholar] [CrossRef] [PubMed]
- Adnan, S.; Ullah, K.; Ahmed, R. Variability in meteorological parameters and their impact on evapotranspiration in a humid zone of Pakistan. Meteorol. Appl. 2020, 27, e1859. [Google Scholar] [CrossRef]
- Fisher, J.B.; Melton, F.; Middleton, E.; Hain, C.; Anderson, M.; Allen, R.; McCabe, M.F.; Hook, S.; Baldocchi, D.; Townsend, P.A.; et al. The future of evapotranspiration: Global requirements for ecosystem functioning, carbon and climate feedbacks, agricultural management, and water resources. Water Resour. Res. 2017, 53, 2618–2626. [Google Scholar] [CrossRef]
- Aguilos, M.; Carter, C.; Middlebrough, B.; Bulluck, J.; Webb, J.; Brannum, K.; Watts, J.O.; Lobeira, M.; Sun, G.; Mcnulty, S.; et al. Hydrologic Perturbation Is a Key Driver of Tree Mortality in Bottomland Hardwood Wetland Forests of North Carolina, USA. Forests 2025, 16, 39. [Google Scholar] [CrossRef]
- Mclaughlin, D.L.; Kaplan, D.A.; Cohen, M.J. Managing forests for increased regional water yield in the southeastern U.S. coastal plain. J. Am. Water Resour. Assoc. 2013, 49, 953–965. [Google Scholar] [CrossRef]
- Megonigal, J.P.; Brewer, P.E.; Knee, K.L. Radon as a natural tracer of gas transport through trees. New Phytol. 2020, 225, 1470–1475. [Google Scholar] [CrossRef]
- Lafleur, P.M.; Roulet, N.T.; Admiral, S.W. Annual cycle of CO2 exchange at a bog peatland. J. Geophys. Res. Atmos. 2001, 106, 3071–3081. [Google Scholar] [CrossRef]
- Sulman, B.N.; Roman, D.T.; Yi, K.; Wang, L.; Phillips, R.P.; Novick, K.A. High atmospheric demand for water can limit forest carbon uptake and transpiration as severely as dry soil. Geophys. Res. Lett. 2016, 43, 9686–9695. [Google Scholar] [CrossRef]
- Mitsch, W.J.; Bernal, B.; Hernandez, M.E. Ecosystem services of wetlands. Int. J. Biodivers. Sci. Ecosyst. Serv. Manag. 2015, 11, 1–4. [Google Scholar] [CrossRef]
- Ratajczak, Z.; Carpenter, S.R.; Ives, A.R.; Kucharik, C.J.; Ramiadantsoa, T.; Stegner, M.A.; Williams, J.W.; Zhang, J.; Turner, M.G. Abrupt Change in Ecological Systems: Inference and Diagnosis. Trends Ecol. Evol. 2018, 33, 513–526. [Google Scholar] [CrossRef] [PubMed]
- Turetsky, M.R.; Abbott, B.W.; Jones, M.C.; Anthony, K.W.; Olefeldt, D.; Schuur, E.A.G.; Grosse, G.; Kuhry, P.; Hugelius, G.; Koven, C.; et al. Carbon release through abrupt permafrost thaw. Nat. Geosci. 2020, 13, 138–143. [Google Scholar] [CrossRef]
- Boulton, C.A.; Lenton, T.M.; Boers, N. Pronounced loss of Amazon rainforest resilience since the early 2000s. Nat. Clim. Change 2022, 12, 271–278. [Google Scholar] [CrossRef]
- Seidl, R.; Thom, D.; Kautz, M.; Martin-Benito, D.; Peltoniemi, M.; Vacchiano, G.; Wild, J.; Ascoli, D.; Petr, M.; Honkaniemi, J.; et al. Forest disturbances under climate change. Nat. Clim. Change 2017, 7, 395–402. [Google Scholar] [CrossRef]
Mortality Rate (%) | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | |
Standing dead | 1.09 | 1.10 | 1.10 | 2.87 | 5.23 | 5.97 | 10.69 | 11.31 | 14.95 | 18.30 | 19.01 |
Downed dead | 0.55 | 0.82 | 1.38 | 4.55 | 4.95 | 7.41 | 24.54 | 26.92 | 28.57 | 29.81 | 31.61 |
Total mortality | 1.64 | 1.91 | 2.46 | 7.29 | 9.94 | 12.97 | 33.00 | 35.67 | 40.01 | 43.70 | 45.82 |
General Species Composition | Scientific Name | Total Tree Density (TPH) | Population Percentage (%) | Tree Mortality in 2019 (per Species in TPH) | Live Trees in 2019 (TPH) | Mortality Rate (%) in 2019 | ||
---|---|---|---|---|---|---|---|---|
Standing Dead | Downed Dead | Total Dead | ||||||
American holly | Ilex opaca | 10 | 3 | 1 | 0 | 1 | 9 | 11 |
Bald cypress | Taxodium distichum | 34 | 9 | 5 | 5 | 10 | 24 | 35 |
Black gum | Nyssa sylvatica | 140 | 38 | 13 | 36 | 49 | 91 | 43 |
Loblolly pine | Pinus taeda | 40 | 11 | 9 | 11 | 20 | 20 | 69 |
Red maple | Acer rubrum | 50 | 14 | 8 | 8 | 16 | 34 | 39 |
Swamp bay | Persea palustris | 57 | 15 | 8 | 20 | 28 | 29 | 68 |
Sweet gum | Liquidambar styraciflua | 39 | 11 | 5 | 7 | 12 | 27 | 37 |
Response Variable | Predictor Variables | Multiple R2 | p-Value | F-Value | AIC |
---|---|---|---|---|---|
Mortality ~ | GWT | 0.75 | 0.002 | 17.99 | 84.07 |
GWT+LAI | 0.89 | 0.002 | 20.52 | 75.33 | |
GWT+LAI+NEE | 0.93 | 0.0006 | 35.55 | 71.48 | |
GWT+LAI+NEE+Rn | 0.98 | 0.0001 | 124.01 | 57.84 |
Variable | Threshold Value | 95% Confidence Interval | |
---|---|---|---|
Lower Limits | Upper Limits | ||
GWT | 2.23 | 1.37 | 3.68 |
LAI | 2.99 | 2.90 | 3.30 |
NEE | 1.27 | −1.11 | 5.64 |
Rn | 167.54 | 163.23 | 213.92 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Aguilos, M.; Zhang, J.; Belgado, M.L.; Sun, G.; McNulty, S.; King, J. Determining Early Warning Thresholds to Detect Tree Mortality Risk in a Southeastern U.S. Bottomland Hardwood Wetland. Forests 2025, 16, 1255. https://doi.org/10.3390/f16081255
Aguilos M, Zhang J, Belgado ML, Sun G, McNulty S, King J. Determining Early Warning Thresholds to Detect Tree Mortality Risk in a Southeastern U.S. Bottomland Hardwood Wetland. Forests. 2025; 16(8):1255. https://doi.org/10.3390/f16081255
Chicago/Turabian StyleAguilos, Maricar, Jiayin Zhang, Miko Lorenzo Belgado, Ge Sun, Steve McNulty, and John King. 2025. "Determining Early Warning Thresholds to Detect Tree Mortality Risk in a Southeastern U.S. Bottomland Hardwood Wetland" Forests 16, no. 8: 1255. https://doi.org/10.3390/f16081255
APA StyleAguilos, M., Zhang, J., Belgado, M. L., Sun, G., McNulty, S., & King, J. (2025). Determining Early Warning Thresholds to Detect Tree Mortality Risk in a Southeastern U.S. Bottomland Hardwood Wetland. Forests, 16(8), 1255. https://doi.org/10.3390/f16081255