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Hurricane-Induced Rainfall is a Stronger Predictor of Tropical Forest Damage in Puerto Rico Than Maximum Wind Speeds

  • 1.

    Everham, E. M. & Brokaw, N. V. L. Forest Damage and Recovery from Catastrophic Wind. Bot. Rev. 62, 113–185 (1996).

    • Article
    • Google Scholar
  • 2.

    Mabry, C. M. et al. Typhoon Disturbance and Stand-level Damage Patterns at a Subtropical Forest in Taiwan. Biotropica 30, 238–250 (1998).

    • Article
    • Google Scholar
  • 3.

    Bellingham, P. J. Commentary Cyclone effects on Australian rain forests: An overview. (2008). https://doi.org/10.1111/j.1442-9993.2008.01914.x

  • 4.

    Lin, T. C. et al. Typhoon Disturbance and Forest Dynamics: Lessons from a Northwest Pacific Subtropical Forest. Ecosystems 14, 127–143 (2011).

  • 5.

    Knutson, T. R. et al. Tropical Cyclones and Climate Change. Nat. Geosci. 3, 157–163 (2010).

  • 6.

    Knutson, T. R. et al. Global Projections of Intense Tropical Cyclone Activity for the Late Twenty-First Century from Dynamical Downscaling of CMIP5/RCP4.5 Scenarios. J. Clim. 28, (2015).

  • 7.

    Kossin, J. P. A global slowdown of tropical-cyclone translation speed. Nature 558, 104–107 (2018).

  • 8.

    Knutson, T. R. et al. Dynamical Downscaling Projections of Twenty-First-Century Atlantic Hurricane Activity: CMIP3 and CMIP5 Model-Based Scenarios. J. Clim. 26, 6591–6617 (2013).

  • 9.

    Gutmann, E. D. et al. Changes in Hurricanes from a 13-Yr Convection-Permitting Pseudo–Global Warming Simulation. J. Clim. 31, 3643–3657 (2018).

  • 10.

    Fisk, J. P. et al. The impacts of tropical cyclones on the net carbon balance of eastern US forests (1851-2000). Environ. Res. Lett. 8, (2013).

  • 11.

    Pan, Y. et al. A Large and Persistent Carbon Sink in the World’s Forests. Science (80-.). 333, 988–993 (2011).

  • 12.

    Van Der Werf, G. R. et al. Global fire emissions estimates during 1997-2016. Earth System Science. Data 9, 697–720 (2017).

    • Google Scholar
  • 13.

    Chambers, J. Q. et al. Respiration from a tropical forest ecosystem: Partitioning of sources and low carbon use efficiency. Ecol. Appl. 14, 72–88 (2004).

    • Article
    • Google Scholar
  • 14.

    Uriarte, M. & Papaik, M. Hurricane impacts on dynamics, structure and carbon sequestration potential of forest ecosystems in Southern New England, USA. Tellus A Dyn. Meteorol. Oceanogr. 59, 519–528 (2007).

    • Article
    • Google Scholar
  • 15.

    Arriaga, L. Types and causes of tree mortality in a tropical montane cloud forest of Tamaulipas, Mexico. J. Trop. Ecol. Sep 5, 623–636 (2000).

    • Article
    • Google Scholar
  • 16.

    Bellingham, P. J. & Tanner, E. V. J. The influence of topography on tree growth, mortality, and recruitment in a tropical montane forest. Biotropica. Sep 32, 378–384 (2000).

    • Article
    • Google Scholar
  • 17.

    Boose, E. R., Serrano, M. I. & Foster, D. R. Landscape and regional impacts of hurricanes in Puerto Rico. Ecol. Monogr. 74, 335–352 (2004).

    • Article
    • Google Scholar
  • 18.

    Tanner, E. V. J., Rodriguez-Sanchez, F., Healey, J. R., Holdaway, R. J. & Bellingham, P. J. Long-term hurricane damage effects on tropical forest tree growth and mortality. Ecology 95, 2974–2983 (2014).

    • Article
    • Google Scholar
  • 19.

    Mitchell, S. J. Wind as a natural disturbance agent in forests: A synthesis. Forestry 86, 147–157 (2013).

    • Article
    • Google Scholar
  • 20.

    Kamimura, K., Kitagawa, K., Saito, S. & Mizunaga, H. Root anchorage of hinoki (Chamaecyparis obtuse (Sieb. Et Zucc.) Endl.) under the combined loading of wind and rapidly supplied water on soil: analyses based on tree-pulling experiments. Eur. J. For. Res. 131, 219–227 (2012).

    • Article
    • Google Scholar
  • 21.

    Lugo, A. E. Visible and invisible effects of hurricanes on forest ecosystems: An international review. Austral Ecol. 33, 368–398 (2008).

    • Article
    • Google Scholar
  • 22.

    Xi, W. Synergistic effects of tropical cyclones on forest ecosystems: a global synthesis. Journal of Forestry Research 26, 1–21 (2015).

  • 23.

    Bellingham, P. J. et al. Hurricanes need not causes high mortality: the effects of Hurricane Gilbert on forest in Jamaica. J. Trop. Ecol. 8, 217–223 (1992).

    • Article
    • Google Scholar
  • 24.

    Imbert, D., Labbe, P. & Rousteau, A. Hurrican Damage and Forest Structure in Guadeloupe, French West Indies. J. Trop. Ecol. 12, 663–680 (1996).

    • Article
    • Google Scholar
  • 25.

    Chambers, J. Q. et al. The steady-state mosaic of disturbance and succession across an old-growth Central Amazon forest landscape. Proc. Natl. Acad. Sci. 110, 3949–3954 (2013).

  • 26.

    Grimbacher, P. S., Catterall, C. P. & Stork, N. E. Do edge effects increase the susceptibility of rainforest fragments to structural damage resulting from a severe tropical cyclone? Austral Ecol. 33, 525–531 (2008).

    • Article
    • Google Scholar
  • 27.

    McMahon, S. M., Arellano, G. & Davies, S. J. The importance and challenges of detecting changes in forest mortality rates. Ecosphere 10, e02615 (2019).

    • Article
    • Google Scholar
  • 28.

    Drusch, M. et al. Sentinel-2: ESA’s Optical High-Resolution Mission for GMES Operational Services. Remote Sens. Environ. 120, 25–36 (2012).

  • 29.

    Chambers, J. Q. et al. Hurricane Katrina’s carbon footprint on U.S. Gulf Coast forests. Science (80-.). 318, 1107 (2007).

  • 30.

    Rifai, S. W. et al. Landscape-scale consequences of differential tree mortality from catastrophic wind disturbance in the Amazon. Ecol. Appl. 26, 2225–2237 (2016).

  • 31.

    Schwartz, N. B. et al. Fragmentation increases wind disturbance impacts on forest structure and carbon stocks in a western Amazonian landscape. Ecol. Appl. 27, 1901–1915 (2017).

  • 32.

    Negrón-Juárez, R. I. et al. Widespread Amazon forest tree mortality from a single cross-basin squall line event. Geophys. Res. Lett. 37, 1–5 (2010).

    • Article
    • Google Scholar
  • 33.

    Breiman, L. E. O. Random Forests. 5–32 (2001).

  • 34.

    Ishwaran, H. & Kogalur, U. B. randomForestSRC: Random Forests for Survival, Regression, and Classificatio. (RF-SRC), R Packag. version 2.7.0., https://doi.org/10.1001/jamapsychiatry.2013.1944 (2018).

  • 35.

    Ehrlinger, J. ggRandomForests: Random Forests for Regression (2014).

  • 36.

    Walker, L. R. Tree Damage and Recovery From Hurricane Hugo in Luquillo Experimental Forest, Puerto Rico. Biotropica 23, 379 (1991).

    • Article
    • Google Scholar
  • 37.

    Walker, L. R. Timing of post-hurricane tree mortality in Puerto Rico. J. Trop. Ecol. 11, 315–320 (1995).

    • Article
    • Google Scholar
  • 38.

    Bellingham, P. J., Tanner, E. V. J. & Healey, J. R. Sprouting of Trees in Jamaican Montane Forests, after a Hurricane. J. Ecol. 82, 747–758 (1994).

    • Article
    • Google Scholar
  • 39.

    Lin, T. C. et al. Influence of typhoon disturbances on the understory light regime and stand dynamics of a subtropical rain forest in northeastern Taiwan. J. For. Res. 8, 139–145 (2003).

  • 40.

    Lugo, A. E. & Scatena, F. N. Background and Catastrophic Tree Mortality in Tropical Moist, Wet, and Rain Forests. Biotropica 28, 585 (1996).

    • Article
    • Google Scholar
  • 41.

    Uriarte, M., Thompson, J. & Zimmerman, J. K. Hurricane María tripled stem breaks and doubled tree mortality relative to other major storms. Nat. Commun. 10, 1362 (2019).

  • 42.

    Paine, R. T., Tegner, M. J. & Johnson, E. A. Compounded Perturbations Yield Ecological Surprises. Ecosystems 1, 535–545 (1998).

    • Article
    • Google Scholar
  • 43.

    Uriarte, M. et al. Natural disturbance and human land use as determinants of tropical forest dynamics: Results from a forest simulator. Ecol. Monogr. 79, 423–443 (2009).

    • Article
    • Google Scholar
  • 44.

    Clark, J. S. et al. Ecological forecasts: an emerging imperative. Science 293, 657–60 (2001).

  • 45.

    Zimmerman, J. K. et al. Responses of Tree Species to Hurricane Winds in Subtropical Wet Forest in Puerto-Rico – Implications for Tropical Tree Life- Histories. J. Ecol. 82, 911–922 (1994).

    • Article
    • Google Scholar
  • 46.

    Wang, F. & Xu, Y. J. Comparison of remote sensing change detection techniques for assessing hurricane damage to forests. Environ. Monit. Assess. 162, 311–326 (2010).

  • 47.

    Van Beusekom, A. E., Álvarez-Berríos, N. L., Gould, W. A., Quiñones, M. & González, G. Hurricane Maria in the U.S. Caribbean: Disturbance forces, variation of effects, and implications for future storms. Remote Sens. 10, 1–14 (2018).

    • Google Scholar
  • 48.

    Zhang, X., Wang, Y., Jiang, H. & Wang, X. Remote-sensing assessment of forest damage by Typhoon Saomai and its related factors at landscape scale. Int. J. Remote Sens. 34, 7874–7886 (2013).

  • 49.

    Hu, T. & Smith, R. B. The impact of Hurricane Maria on the vegetation of Dominica and Puerto Rico using multispectral remote sensing. Remote Sens. 10, (2018).

  • 50.

    Hoque, M. A. A., Phinn, S. & Roelfsema, C. A systematic review of tropical cyclone disaster management research using remote sensing and spatial analysis. Ocean Coast. Manag. 146, 109–120 (2017).

    • Article
    • Google Scholar
  • 51.

    Batke, S. P., Jocque, M. & Kelly, D. L. Modelling hurricane exposure and wind speed on a mesoclimate scale: A case study from Cusuco NP, Honduras. PLoS One 9, (2014).

  • 52.

    Batke, S. P. & Kelly, D. L. Tree damage and microclimate of forest canopies along a hurricane-impact gradient in Cusuco National Park, Honduras. J. Trop. Ecol. 30, 457–467 (2014).

    • Article
    • Google Scholar
  • 53.

    Foster, D. & Boose, E. Patterns of Forest Damage Resulting from Catastrophic Wind in Central New England, USA. Br. Ecol. Soc. 80, 79–98 (1992).

    • Google Scholar
  • 54.

    Xi, W. & Peet, R. K. Hurricane effects on the piedmont forests: Patterns and implications. Ecol. Restor. 26, 295–298 (2008).

    • Article
    • Google Scholar
  • 55.

    Luke, D., McLaren, K. & Wilson, B. Modeling Hurricane Exposure in a Caribbean Lower Montane Tropical Wet Forest: The Effects of Frequent, Intermediate Disturbances and Topography on Forest Structural Dynamics and Composition. Ecosystems 19, 1178–1195 (2016).

    • Article
    • Google Scholar
  • 56.

    Xi, W. Forest response to natural disturbance: Change in structure and diversity on a North Carolina Piedmont forest in response to catastrophic wind events. 368 (2005).

  • 57.

    Xi, W., Peet, R. K., Decoster, J. K. & Urban, D. L. Tree damage risk factors associated with large, infrequent wind disturbances of Carolina forests. Forestry 81, 317–334 (2008).

    • Article
    • Google Scholar
  • 58.

    Scatena, F. N. & Lugo, A. E. Geomorphology, disturbance, and the vegetation and soils of two subtropical wet steep land watersheds in Puerto Rico. Geomorphology 13, 199–213 (1995).

  • 59.

    Pasch, R. J., Penny, A. B. & Berg, R. Hurricane Maria. National Hurricane Center Tropical Cyclone Report (2019). AL142016

  • 60.

    Keellings, D. & Hernández Ayala, J. J. Extreme Rainfall Associated With Hurricane Maria Over Puerto Rico and Its Connections to Climate Variability and Change. Geophys. Res. Lett. 46, 2964–2973 (2019).

  • 61.

    Bessette-Kirton, E. K. et al. Map Data Showing Concentration of Landslides Caused by Hurricane Maria in Puerto Rico. (2017).

  • 62.

    Larsen, M. C. & Simon, A. A rainfall intensity-duration threshold for landslides in a humid- tropical environment, Puerto Rico. Geogr. Ann. Ser. A 75 A, 13–23 (1993).

    • Article
    • Google Scholar
  • 63.

    Miller, G. L. & Lugo, A. E. Guide to the ecological systems of Puerto Rico. Gen. Tech. Rep. IITF-GTR-35 1–436 (2009).

  • 64.

    Daly, C., Helmer, E. H. & Quinones, M. Mapping the climate of Puerto Rico, Vieques and Culebra. Int. J. Climatol. 23, 1359–1381 (2003).

    • Article
    • Google Scholar
  • 65.

    Kennaway, T. & Helmer, E. The Forest Types and Ages Cleared for Land Development in Puerto Rico. GIScience Remote Sens. 44, 356–382 (2007).

    • Article
    • Google Scholar
  • 66.

    Helmer, E. H., Brandeis, T. J., Lugo, A. E. & Kennaway, T. Factors influencing spatial pattern in tropical forest clearance and stand age: Implications for carbon storage and species diversity. J. Geophys. Res. Biogeosciences 113, 1–14 (2008).

    • Article
    • Google Scholar
  • 67.

    Hansen, M. C. et al. High-Resolution Global Maps of. Science (80-.). 342, 850–853 (2013).

  • 68.

    Schwartz, N. B., Budsock, A. M. & Uriarte, M. Fragmentation, forest structure, and topography modulate impacts of drought in a tropical forest landscape. Ecology 100, (2019).

  • 69.

    Muscarella, R., Uriarte, M., Erickson, D. L. & Swenson, N. G. Climate and Biodiversity Effects on Standing Biomass in Puerto Rican Forests. Caribb. Nat. 199–217 (2016).

  • 70.

    Barone, J. A., Thomlinson, J., Cordero, P. A. & Zimmerman, J. K. Metacommunity structure of tropical forest along an elevation gradient in Puerto Rico. J. Trop. Ecol. 24, 525–534 (2008).

    • Article
    • Google Scholar
  • 71.

    Thompson, J. et al. Land Use History, Environment, and Tree Composition in a Tropical Forest. Ecol. Appl. 12, 1344–1363 (2002).

    • Article
    • Google Scholar
  • 72.

    TIGER/Line Shapefile, 2016, Series Information for the Current Census Tract State-based Shapefile. (2016).

  • 73.

    CRIM, P. Hydrography Revision, 2001-04 from CRIM Basemap, scale 1:5000, Aguas Buenas, PR Quadrangle. (2006).

  • 74.

    Gorelick, N. et al. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2016).

  • 75.

    Adams, J. B. & Gillespie, A. R. Remote sensing of landscapes with spectral images: A physical modeling approach. Remote Sensing of Landscapes with Spectral Images: A Physical Modeling Approach https://doi.org/10.1017/CBO9780511617195 (2006).

  • 76.

    Canham, C. D., Thompson, J., Zimmerman, J. K. & Uriarte, M. Variation in susceptibility to hurricane damage as a function of storm intensity in puerto Rican tree species. Biotropica 42, 87–94 (2010).

    • Article
    • Google Scholar
  • 77.

    Scatena, F. N., Silver, W., Siccama, T., Johnson, A. & Sanchez, M. J. Biomass and Nutrient Content of the Bisley Experimental Watersheds, Luquillo Experimental Forest, Puerto Rico, Before and After Hurricane Hugo, 1989. Biotropica 25, 15–27 (1993).

    • Article
    • Google Scholar
  • 78.

    Carswell, W. J. The 3D Elevation Program: summary for Puerto Rico. https://doi.org/10.3133/fs20133097 (2016).

    • Article
    • Google Scholar
  • 79.

    Brown, S. Estimating biomass and biomass change of tropical forests: a primer. (Food and Agriculture Organization of the United Nations, 1986).

  • 80.

    Metcalf, E. J. C., Clark, J. S. & Clark, D. A. Tree growth inference and prediction when the point of measurement changes: modelling around buttresses in tropical forests. J. Trop. Ecol. 25, 1–12 (2009).

    • Article
    • Google Scholar
  • 81.

    Cushman, K. C., Muller-Landau, H. C., Condit, R. S. & Hubbell, S. P. Improving estimates of biomass change in buttressed trees using tree taper models. Methods Ecol. Evol. 5, 573–582 (2014).

    • Article
    • Google Scholar
  • 82.

    Mark D. Powell, Sam H. Houston, Luis R. Amat, Nirva Morisseau-Leroy, The HRD real-time hurricane wind analysis system. Journal of Wind Engineering and Industrial Aerodynamics 77–78, 53–64 (1998).

    • Article
    • Google Scholar
  • 83.

    Emery R. Boose, David R. Foster, Marcheterre Fluet. Hurricane Impacts to Tropical and Temperate Forest Landscapes. Ecological Monographs 64(4), 369–400 (1994).

    • Article
    • Google Scholar
  • 84.

    Reed, J. C. & Bush, C. A. About the geologic map in the National Atlas of the United States of America. (U.S. Geological Survey, 2007).

  • 85.

    Hijmans, R. J. et al. Raster: raster: Geographic data analysis and modeling. R package, version (2011).

  • 86.

    Cutler, R. et al. Random Forests for Classification in Ecology. 88, 2783–2792 (2007).

  • 87.

    Friedman, J. H. & Popescu, B. E. Predictive learning via rule ensembles. Ann. Appl. Stat. 2, 916–954 (2008).

  • 88.

    Fokkema, M. & Christoffersen, B. Prediction Rule Ensembles Version. 0.7.1, (2019).

  • 89.

    Molnar, C. iml: An R package for Interpretable Machine Learning. J. Open Source Softw. 3, 786 (2018).


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