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An evaluation of multi-species empirical tree mortality algorithms for dynamic vegetation modelling

  • 1.

    Allen, C. D., Breshears, D. D. & McDowell, N. G. On underestimation of global vulnerability to tree mortality and forest die-off from hotter drought in the Anthropocene. Ecosphere 6, 1–55. https://doi.org/10.1890/Es15-00203.1 (2015).

    Article 

    Google Scholar 

  • 2.

    Allen, C. D. et al. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. For. Ecol. Manag. 259, 660–684. https://doi.org/10.1016/j.foreco.2009.09.001 (2010).

    Article 

    Google Scholar 

  • 3.

    Anderegg, W. R. L., Kane, J. M. & Anderegg, L. D. L. Consequences of widespread tree mortality triggered by drought and temperature stress. Nat Clim Change 3, 30–36 (2013).

    ADS 
    Article 

    Google Scholar 

  • 4.

    Taccoen, A. et al. Background mortality drivers of European tree species: climate change matters. Proc R Soc B-Biol Sci 286, 1–10. https://doi.org/10.1098/rspb.2019.0386 (2019).

    Article 

    Google Scholar 

  • 5.

    Hartmann, H. et al. Research frontiers for improving our understanding of drought-induced tree and forest mortality. New Phytol. 218, 15–28. https://doi.org/10.1111/nph.15048 (2018).

    Article 
    PubMed 

    Google Scholar 

  • 6.

    Trugman, A. T., Anderegg, L. D. L., Anderegg, W. R. L., Das, A. J. & Stephenson, N. L. Why is tree drought mortality so hard to predict? Trends Ecol. Evol., 1–13. https://doi.org/10.1016/j.tree.2021.02. (2021).

  • 7.

    McDowell, N. G. et al. Evaluating theories of drought-induced vegetation mortality using a multimodel-experiment framework. New Phytol. 200, 304–321. https://doi.org/10.1111/nph.12465 (2013).

    CAS 
    Article 
    PubMed 

    Google Scholar 

  • 8.

    Keane, R. E. et al. Tree mortality in gap models: application to climate change. Clim. Change 51, 509–540. https://doi.org/10.1023/A:1012539409854 (2001).

    Article 

    Google Scholar 

  • 9.

    Bircher, N., Cailleret, M. & Bugmann, H. The agony of choice: different empirical mortality models lead to sharply different future forest dynamics. Ecol. Appl. 25, 1303–1318. https://doi.org/10.1890/14-1462.1 (2015).

    Article 
    PubMed 

    Google Scholar 

  • 10.

    Bugmann, H. et al. Tree mortality submodels drive long term forest dynamics: an assessment across 15 models from the stand to the global scale. Ecosphere 10, 1–22. https://doi.org/10.1002/ecs2.2616 (2019).

    Article 

    Google Scholar 

  • 11.

    Friend, A. D. et al. Carbon residence time dominates uncertainty in terrestrial vegetation responses to future climate and atmospheric CO2. Proc Natl Acad Sci USA 111, 3280–3285. https://doi.org/10.1073/pnas.1222477110 (2014).

    ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 

  • 12.

    Lines, E. R., Coomes, D. A. & Purves, D. W. Influences of forest structure, climate and species composition on tree mortality across the Eastern US. PLoS ONE 5, 1–12. https://doi.org/10.1371/journal.pone.0013212 (2010).

    CAS 
    Article 

    Google Scholar 

  • 13.

    Purves, D. & Pacala, S. Predictive models of forest dynamics. Science 320, 1452–1453. https://doi.org/10.1126/science.1155359 (2008).

    ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 

  • 14.

    Cailleret, M., Bircher, N., Hartig, F., Hülsmann, L. & Bugmann, H. Bayesian calibration of a growth-dependent tree mortality model to simulate the dynamics of European temperate forests. Ecol. Appl. 30, 1–17. https://doi.org/10.1002/eap.2021 (2020).

    Article 

    Google Scholar 

  • 15.

    Rowland, L., Martinez-Vilalta, J. & Mencuccini, M. Hard times for high expectations from hydraulics: predicting drought-induced forest mortality at landscape scales remains a challenge. New Phytol. 230, 1685–1687. https://doi.org/10.1111/nph.17317 (2021).

    Article 
    PubMed 

    Google Scholar 

  • 16.

    Cailleret, M. et al. A synthesis of radial growth patterns preceding tree mortality. Glob. Change Biol. 23, 1675–1690. https://doi.org/10.1111/gcb.13535 (2017).

    ADS 
    Article 

    Google Scholar 

  • 17.

    Bigler, C. & Bugmann, H. Growth-dependent tree mortality models based on tree rings. Can. J. For. Res. 33, 210–221. https://doi.org/10.1139/X02-180 (2003).

    Article 

    Google Scholar 

  • 18.

    Hülsmann, L., Bugmann, H., Cailleret, M. & Brang, P. How to kill a tree: empirical mortality models for 18 species and their performance in a dynamic forest model. Ecol. Appl. 28, 522–540. https://doi.org/10.1002/eap.1668 (2018).

    Article 
    PubMed 

    Google Scholar 

  • 19.

    Weiskittel, A. R., Hann, D. W., Kershaw, J. A. & Vanclay, J. K. in Forest Growth and Yield Modeling Ch. 8, 139–155 (Wiley, 2011).

  • 20.

    Holzwarth, F., Kahl, A., Bauhus, J. & Wirth, C. Many ways to die – partitioning tree mortality dynamics in a near-natural mixed deciduous forest. J. Ecol. 101, 220–230. https://doi.org/10.1111/1365-2745.12015 (2013).

    Article 

    Google Scholar 

  • 21.

    Dobbertin, M. Tree growth as indicator of tree vitality and of tree reaction to environmental stress: a review. Eur. J. For. Res. 124, 319–333. https://doi.org/10.1007/s10342-005-0085-3 (2005).

    Article 

    Google Scholar 

  • 22.

    Thrippleton, T., Hülsmann, L., Cailleret, M. & Bugmann, H. Projecting forest dynamics across Europe: potentials and pitfalls of empirical mortality algorithms. Ecosystems 23, 188–203. https://doi.org/10.1007/s10021-019-00397-3 (2020).

    Article 

    Google Scholar 

  • 23.

    Adams, H. D. et al. Empirical and process-based approaches to climate-induced forest mortality models. Front Plant Sci 4, 1–5. https://doi.org/10.3389/fpls.2013.00438 (2013).

    ADS 
    CAS 
    Article 

    Google Scholar 

  • 24.

    Archambeau, J. et al. Similar patterns of background mortality across Europe are mostly driven by drought in European beech and a combination of drought and competition in Scots pine. Agric. For. Meteorol. 280, 1–12. https://doi.org/10.1016/j.agrformet.2019.107772 (2020).

    Article 

    Google Scholar 

  • 25.

    Luo, Y. & Chen, H. Y. H. Competition, species interaction and ageing control tree mortality in boreal forests. J. Ecol. 99, 1470–1480. https://doi.org/10.1111/j.1365-2745.2011.01882.x (2011).

    Article 

    Google Scholar 

  • 26.

    Brzeziecki, B. & Kienast, F. Classifying the life-history strategies of trees on the basis of the grimian model. For. Ecol. Manage. 69, 167–187. https://doi.org/10.1016/0378-1127(94)90227-5 (1994).

    Article 

    Google Scholar 

  • 27.

    Valladares, F. & Niinemets, U. Shade tolerance, a key plant feature of complex nature and consequences. Annu. Rev. Ecol. Evol. Syst. 39, 237–257. https://doi.org/10.1146/annurev.ecolsys.39.110707.173506 (2008).

    Article 

    Google Scholar 

  • 28.

    Kobe, R. K. & Coates, K. D. Models of sapling mortality as a function of growth to characterize interspecific variation in shade tolerance of eight tree species of northwestern British Columbia. Can. J. For. Res. 27, 227–236. https://doi.org/10.1139/x96-182 (1997).

    Article 

    Google Scholar 

  • 29.

    Wyckoff, P. H. & Clark, J. S. The relationship between growth and mortality for seven co-occurring tree species in the southern Appalachian Mountains. J. Ecol. 90, 604–615. https://doi.org/10.1046/j.1365-2745.2002.00691.x (2002).

    Article 

    Google Scholar 

  • 30.

    Anderegg, L. D. L. & HilleRisLambers, J. Drought stress limits the geographic ranges of two tree species via different physiological mechanisms. Glob. Change Biol. 22, 1029–1045. https://doi.org/10.1111/gcb.13148 (2016).

    ADS 
    Article 

    Google Scholar 

  • 31.

    Clark, J. S. et al. The impacts of increasing drought on forest dynamics, structure, and biodiversity in the United States. Glob. Change Biol. 22, 2329–2352. https://doi.org/10.1111/gcb.13160 (2016).

    ADS 
    Article 

    Google Scholar 

  • 32.

    Etzold, S. et al. One century of forest monitoring data in Switzerland reveals species- and site-specific trends of climate-induced tree mortality. Front Plant Sci 10, 1–19. https://doi.org/10.3389/fpls.2019.00307 (2019).

    Article 

    Google Scholar 

  • 33.

    Schuldt, B. et al. A first assessment of the impact of the extreme 2018 summer drought on Central European forests. Basic Appl. Ecol. 45, 86–103. https://doi.org/10.1016/j.baae.2020.04.003 (2020).

    Article 

    Google Scholar 

  • 34.

    Vanoni, M., Cailleret, M., Hülsmann, L., Bugmann, H. & Bigler, C. How do tree mortality models from combined tree-ring and inventory data affect projections of forest succession?. For. Ecol. Manage. 433, 606–617. https://doi.org/10.1016/j.foreco.2018.11.042 (2019).

    Article 

    Google Scholar 

  • 35.

    Huber, N., Bugmann, H. & Lafond, V. Capturing ecological processes in dynamic forest models: why there is no silver bullet to cope with complexity. Ecosphere 11, 1–34. https://doi.org/10.1002/ecs2.3109 (2020).

    Article 

    Google Scholar 

  • 36.

    Bugmann, H. A simplified forest model to study species composition along climate gradients. Ecology 77, 2055–2074. https://doi.org/10.2307/2265700 (1996).

    Article 

    Google Scholar 

  • 37.

    Hülsmann, L., Bugmann, H. & Brang, P. How to predict tree death from inventory data – lessons from a systematic assessment of European tree mortality models. Can. J. For. Res. 47, 890–900. https://doi.org/10.1139/cjfr-2016-0224 (2017).

    Article 

    Google Scholar 

  • 38.

    Eid, T. & Tuhus, E. Models for individual tree mortality in Norway. For. Ecol. Manag. 154, 69–84. https://doi.org/10.1016/S0378-1127(00)00634-4 (2001).

    Article 

    Google Scholar 

  • 39.

    Monserud, R. A. & Sterba, H. Modeling individual tree mortality for Austrian forest species. For. Ecol. Manag. 113, 109–123. https://doi.org/10.1016/S0378-1127(98)00419-8 (1999).

    Article 

    Google Scholar 

  • 40.

    Dursky, J. Modellierung der Absterbeprozesse in Rein- und Mischbeständen aus Fichte und Buche. Allg. Forst- u. Jagdztg. 168, 131–134 (1997).

    Google Scholar 

  • 41.

    Trasobares, A., Pukkala, T. & Muna, J. Growth and yield model for uneven-aged mixtures of Pinus sylvestris L. and Pinus nigra Arn. in Catalonia, north-east Spain. Ann. For. Sci. 61, 9–24, doi:https://doi.org/10.1051/forset:2003080 (2004).

  • 42.

    Crecente-Campo, F., Soares, P., Tome, M. & Dieguez-Aranda, U. Modelling annual individual-tree growth and mortality of Scots pine with data obtained at irregular measurement intervals and containing missing observations. For. Ecol. Manage. 260, 1965–1974. https://doi.org/10.1016/j.foreco.2010.08.044 (2010).

    Article 

    Google Scholar 

  • 43.

    Palahi, M., Pukkala, T., Miina, J. & Montero, G. Individual-tree growth and mortality models for Scots pine (Pinus sylvestris L.) in north-east Spain. Ann. For. Sci. 60, 1–10, https://doi.org/10.1051/forest:2002068 (2003).

  • 44.

    Bravo-Oviedo, A., Sterba, H., del Rio, M. & Bravo, F. Competition-induced mortality for Mediterranean Pinus pinaster Ait. and P-sylvestris L. For. Ecol. Manag. 222, 88–98, doi:https://doi.org/10.1016/j.foreco.2005.10.016 (2006).

  • 45.

    Fridman, J. & Ståhl, G. A three-step approach for modelling tree mortality in Swedish forests. Scand. J. For. Res. 16, 455–466. https://doi.org/10.1080/02827580152632856 (2001).

    Article 

    Google Scholar 

  • 46.

    Wunder, J. et al. Growth-mortality relationships as indicators of life-history strategies: a comparison of nine tree species in unmanaged European forests. Oikos 117, 815–828. https://doi.org/10.1111/j.0030-1299.2008.16371.x (2008).

    Article 

    Google Scholar 

  • 47.

    Das, A., Battles, J., Stephenson, N. L. & van Mantgem, P. J. The contribution of competition to tree mortality in old-growth coniferous forests. For. Ecol. Manage. 261, 1203–1213. https://doi.org/10.1016/j.foreco.2010.12.035 (2011).

    Article 

    Google Scholar 

  • 48.

    Bigler, C. & Bugmann, H. Predicting the time of tree death using dendrochronological data. Ecol. Appl. 14, 902–914. https://doi.org/10.1890/03-5011 (2004).

    Article 

    Google Scholar 

  • 49.

    Larocque, G. R., Archambault, L. & Delisle, C. Development of the gap model ZELIG-CFS to predict the dynamics of North American mixed forest types with complex structures. Ecol. Model. 222, 2570–2583. https://doi.org/10.1016/j.ecolmodel.2010.08.035 (2011).

    Article 

    Google Scholar 

  • 50.

    Timofeeva, G. et al. Long-term effects of drought on tree-ring growth and carbon isotope variability in Scots pine in a dry environment. Tree Physiol. 37, 1028–1041. https://doi.org/10.1093/treephys/tpx041 (2017).

    CAS 
    Article 
    PubMed 

    Google Scholar 

  • 51.

    Neumann, M., Mues, V., Moreno, A., Hasenauer, H. & Seidl, R. Climate variability drives recent tree mortality in Europe. Glob. Change Biol. 23, 4788–4797. https://doi.org/10.1111/gcb.13724 (2017).

    ADS 
    Article 

    Google Scholar 

  • 52.

    Levesque, M. et al. Drought response of five conifer species under contrasting water availability suggests high vulnerability of Norway spruce and European larch. Glob. Change Biol. 19, 3184–3199. https://doi.org/10.1111/gcb.12268 (2013).

    ADS 
    Article 

    Google Scholar 

  • 53.

    Rigling, A. et al. Driving factors of a vegetation shift from Scots pine to pubescent oak in dry Alpine forests. Glob. Change Biol. 19, 229–240. https://doi.org/10.1111/gcb.12038 (2013).

    ADS 
    Article 

    Google Scholar 

  • 54.

    Eyvindson, K., Repo, A. & Mönkkönen, M. Mitigating forest biodiversity and ecosystem service losses in the era of bio-based economy. Forest Policy Econ 92, 119–127. https://doi.org/10.1016/j.forpol.2018.04.009 (2018).

    Article 

    Google Scholar 

  • 55.

    Mina, M. et al. Future ecosystem services from European mountain forests under climate change. J. Appl. Ecol. 54, 389–401. https://doi.org/10.1111/1365-2664.12772 (2017).

    Article 

    Google Scholar 

  • 56.

    Thom, D., Rammer, W. & Seidl, R. The impact of future forest dynamics on climate: interactive effects of changing vegetation and disturbance regimes. Ecol. Monogr. 87, 665–684. https://doi.org/10.1002/ecm.1272 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 57.

    Blattert, C., Lemm, R., Thees, O., Lexer, M. J. & Hanewinkel, M. Management of ecosystem services in mountain forests: review of indicators and value functions for model based multi-criteria decision analysis. Ecol Indic 79, 391–409. https://doi.org/10.1016/j.ecolind.2017.04.025 (2017).

    Article 

    Google Scholar 

  • 58.

    Haeler, E. et al. Saproxylic species are linked to the amount and isolation of dead wood across spatial scales in a beech forest. Landscape Ecol. 36, 89–104. https://doi.org/10.1007/s10980-020-01115-4 (2021).

    Article 

    Google Scholar 

  • 59.

    Das, A. J., Stephenson, N. L. & Davis, K. P. Why do trees die? Characterizing the drivers of background tree mortality. Ecology 97, 2616–2627. https://doi.org/10.1002/ecy.1497 (2016).

    Article 
    PubMed 

    Google Scholar 

  • 60.

    Franklin, J. F., Shugart, H. H. & Harmon, M. E. Tree death as an ecological process. Bioscience 37, 550–556. https://doi.org/10.2307/1310665 (1987).

    Article 

    Google Scholar 

  • 61.

    Huber, N., Bugmann, H. & Lafond, V. Global sensitivity analysis of a dynamic vegetation model: model sensitivity depends on successional time, climate and competitive interactions. Ecol. Model. 368, 377–390. https://doi.org/10.1016/j.ecolmodel.2017.12.013 (2018).

    Article 

    Google Scholar 

  • 62.

    Portier, J. et al. “Latent reserves”: a hidden treasure in National Forest Inventories. J. Ecol. 109, 369–383. https://doi.org/10.1111/1365-2745.13487 (2021).

    Article 

    Google Scholar 

  • 63.

    Kunstler, G. et al. Demographic performance of European tree species at their hot and cold climatic edges. J. Ecol. 109, 1041–1054. https://doi.org/10.1111/1365-2745.13533 (2021).

    Article 

    Google Scholar 

  • 64.

    Gutierrez, A. G., Snell, R. S. & Bugmann, H. Using a dynamic forest model to predict tree species distributions. Glob. Ecol. Biogeogr. 25, 347–358. https://doi.org/10.1111/geb.12421 (2016).

    Article 

    Google Scholar 

  • 65.

    Botkin, D. B., Janak, J. F. & Wallis, J. R. Some ecological consequences of a computer model of forest growth. J. Ecol. 60, 849–872. https://doi.org/10.2307/2258570 (1972).

    Article 

    Google Scholar 

  • 66.

    Bugmann, H. A review of forest gap models. Clim. Change 51, 259–305. https://doi.org/10.1023/A:1012525626267 (2001).

    Article 

    Google Scholar 

  • 67.

    Watt, A. S. Pattern and process in the plant community. J. Ecol. 35, 1–22. https://doi.org/10.2307/2256497 (1947).

    Article 

    Google Scholar 

  • 68.

    Shugart, H. H. & Smith, T. M. A review of forest patch models and their application to global change research. Clim. Change 34, 131–153. https://doi.org/10.1007/BF00224626 (1996).

    ADS 
    Article 

    Google Scholar 

  • 69.

    Monserud, R. A. Simulation of forest tree mortality. Forest Science 22, 438–444. https://doi.org/10.1093/forestscience/22.4.438 (1976).

    Article 

    Google Scholar 

  • 70.

    IPCC. Climate Change 2014: Impacts, adaptation, and vulnerability, Pt A: global and sectoral aspects. Climate Change 2014: Impacts, Adaptation, and Vulnerability, Pt A: Global and Sectoral Aspects, 1-1131, doi:https://doi.org/10.1017/CBO9781107415379 (2014).

  • 71.

    Manusch, C., Bugmann, H., Heiri, C. & Wolf, A. Tree mortality in dynamic vegetation models: a key feature for accurately simulating forest properties. Ecol. Model. 243, 101–111. https://doi.org/10.1016/j.ecolmodel.2012.06.008 (2012).

    Article 

    Google Scholar 

  • 72.

    R: A language and environment for statistical computing (R Foundation for Statistical Computing, Vienna, Austria, 2020).


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