Skip to main content

Projecting Forest Dynamics Across Europe: Potentials and Pitfalls of Empirical Mortality Algorithms

Abstract

Mortality is a key process of forest ecosystem dynamics and functioning strongly altering biomass stocks and carbon residence times. Dynamic vegetation models (DVMs) used to predict forest dynamics are typically based on simple, largely data-free (‘theoretical’) mortality algorithms (MAs). To improve DVM projections, the use of empirically based MAs has been suggested, but little is known about their impact on DVM behavior. A systematic comparison of eight MAs (seven inventory-based, one ‘theoretical’) for the pan-European tree species Pinus sylvestris L. was carried out within the DVM ForClim for present and future climate scenarios at three contrasting sites across Europe. Model accuracy was furthermore evaluated with empirical data from young- and old-growth forests. We found strongly diverging mortality patterns among the MAs for present climate. Based on their behavior, we identified two distinct empirical MA groups that were related to their structure (i.e., variables considered), but not to their geographic origin (i.e., the environmental conditions they were calibrated to). Under climate change, MAs based on a competition index produced ecologically inconsistent results, while MAs based on growth showed more plausible and less extreme behaviors. Furthermore, MAs based on growth reached a higher accuracy for projecting young- and old-growth forest dynamics. Our results demonstrate that using empirical MAs in DVMs has a high potential to better predict forest dynamics, but also a risk of yielding implausible results if their structure is inadequate. For DVM applications across large spatiotemporal scales, we thus suggest using MAs based on growth, particularly under future no-analogue climates.

This is a preview of subscription content, access via your institution.

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6

Data Availability

Dynamic vegetation model ForClim available on the ETH Zurich server: https://ites-fe.ethz.ch/openaccess/software/view/1.

References

  • Adams HD, Williams AP, Xu CG, Rauscher SA, Jiang XY, McDowell NG. 2013. Empirical and process-based approaches to climate-induced forest mortality models. Frontiers in Plant Science 4:1–5.

    CAS  Google Scholar 

  • Allen CD, Breshears DD, McDowell NG. 2015. On underestimation of global vulnerability to tree mortality and forest die-off from hotter drought in the Anthropocene. Ecosphere 6:1–55.

    Google Scholar 

  • Allen CD, Macalady AK, Chenchouni H, Bachelet D, McDowell N, Vennetier M, Kitzberger T, Rigling A, Breshears DD, Hogg EH, Gonzalez P, Fensham R, Zhang Z, Castro J, Demidova N, Lim JH, Allard G, Running SW, Semerci A, Cobb N. 2010. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. Forest Ecology and Management 259:660–84.

    Google Scholar 

  • Anderegg WRL, Kane JM, Anderegg LDL. 2013. Consequences of widespread tree mortality triggered by drought and temperature stress. Nature Climate Change 3:30–6.

    Google Scholar 

  • Bennett AC, McDowell NG, Allen CD, Anderson-Teixeira KJ. 2015. Larger trees suffer most during drought in forests worldwide. Nature Plants 1:1–5.

    Google Scholar 

  • Bigler C. 2016. Trade-offs between growth rate, tree size and lifespan of mountain pine (Pinus montana) in the Swiss National Park. PLoS ONE 11:1–18.

    Google Scholar 

  • Bigler C, Braker OU, Bugmann H, Dobbertin M, Rigling A. 2006. Drought as an inciting mortality factor in Scots pine stands of the Valais, Switzerland. Ecosystems 9:330–43.

    Google Scholar 

  • Bigler C, Bugmann H. 2004. Predicting the time of tree death using dendrochronological data. Ecological Applications 14:902–14.

    Google Scholar 

  • Bigler C, Veblen TT. 2009. Increased early growth rates decrease longevities of conifers in subalpine forests. OIKOS 118:1130–8.

    Google Scholar 

  • Bircher N, Cailleret M, Bugmann H. 2015. The agony of choice: different empirical mortality models lead to sharply different future forest dynamics. Ecological Applications 25:1303–18.

    PubMed  Google Scholar 

  • Boisvenue C, Running SW. 2006. Impacts of climate change on natural forest productivity—evidence since the middle of the 20th century. Global Change Biology 12:862–82.

    Google Scholar 

  • Brang P, Heiri C. 2011. Wenn Waldbrand die Uhr auf Null zurückstellt: der Pfynwald. Brang P, Heiri C, Bugmann H editors. Waldreservate—50 Jahre natürliche Waldentwicklung in der Schweiz. Bern, Stuttgart, Wien: Paul Haupt Verlag, pp 140–149.

  • Bravo-Oviedo A, Sterba H, del Rio M, Bravo F. 2006. Competition-induced mortality for Mediterranean Pinus pinaster Ait. and P-sylvestris L. Forest Ecology and Management 222:88–98.

    Google Scholar 

  • Brazhnik K, Shugart HH. 2016. SIBBORK: A new spatially-explicit gap model for boreal forest. Ecological Modelling 320:182–96.

    Google Scholar 

  • Bugmann H. 1996. A simplified forest model to study species composition along climate gradients. Ecology 77:2055–74.

    Google Scholar 

  • Bugmann H. 2001. A review of forest gap models. Climatic Change 51:259–305.

    Google Scholar 

  • Bugmann H, Hartig F, Hülsmann L, Kollas C, Nadal-Sala D, Vacchiano G, Xu C, Reyer C. 2019. Tree mortality submodels drive long term forest dynamics: an assessment across 15 models from the stand to the global scale. Ecosphere 10:1–22.

    Google Scholar 

  • Cailleret M, Jansen S, Robert EMR, Desoto L, Aakala T, Antos JA, Beikircher B, Bigler C, Bugmann H, Caccianiga M, Cada V, Camarero JJ, Cherubini P, Cochard H, Coyea MR, Cufar K, Das AJ, Davi H, Delzon S, Dorman M, Gea-Izquierdo G, Gillner S, Haavik LJ, Hartmann H, Heres AM, Hultine KR, Janda P, Kane JM, Kharuk VI, Kitzberger T, Klein T, Kramer K, Lens F, Levanic T, Calderon JCL, Lloret F, Lobodo-Vale R, Lombardi F, Rodriguez RL, Makinen H, Mayr S, Meszaros I, Metsaranta JM, Minunno F, Oberhuber W, Papadopoulos A, Peltoniemi M, Petritan AM, Rohner B, Sanguesa-Barreda G, Sarris D, Smith JM, Stan AB, Sterck F, Stojanovic DB, Suarez ML, Svoboda M, Tognetti R, Torres-Ruiz JM, Trotsiuk V, Villalba R, Vodde F, Westwood AR, Wyckoff PH, Zafirov N, Martinez-Vilalta J. 2017. A synthesis of radial growth patterns preceding tree mortality. Global Change Biology 23:1675–90.

    PubMed  Google Scholar 

  • Crecente-Campo F, Soares P, Tome M, Dieguez-Aranda U. 2010. Modelling annual individual-tree growth and mortality of Scots pine with data obtained at irregular measurement intervals and containing missing observations. Forest Ecology and Management 260:1965–74.

    Google Scholar 

  • Das AJ, Stephenson NL, Davis KP. 2016. Why do trees die? Characterizing the drivers of background tree mortality. Ecology 97:2616–27.

    PubMed  Google Scholar 

  • Dobbertin M. 2005. Tree growth as indicator of tree vitality and of tree reaction to environmental stress: A review. European Journal of Forest Research 124:319–33.

    Google Scholar 

  • Eid T, Tuhus E. 2001. Models for individual tree mortality in Norway. Forest Ecology and Management 154:69–84.

    Google Scholar 

  • Fisher RA, Koven CD, Anderegg WRL, Christoffersen BO, Dietze MC, Farrior CE, Holm JA, Hurtt GC, Knox RG, Lawrence PJ, Lichstein JW, Longo M, Matheny AM, Medvigy D, Muller-Landau HC, Powell TL, Serbin SP, Sato H, Shuman JK, Smith B, Trugman AT, Viskari T, Verbeeck H, Weng ES, Xu CG, Xu XT, Zhang T, Moorcroft PR. 2018. Vegetation demographics in earth system models: A review of progress and priorities. Global Change Biology 24:35–54.

    PubMed  Google Scholar 

  • Franklin JF, Shugart HH, Harmon ME. 1987. Tree death as an ecological process. BioScience 37:550–6.

    Google Scholar 

  • Friend AD, Lucht W, Rademacher TT, Keribin R, Betts R, Cadule P, Ciais P, Clark DB, Dankers R, Falloon PD, Ito A, Kahana R, Kleidon A, Lomas MR, Nishina K, Ostberg S, Pavlick R, Peylin P, Schaphoff S, Vuichard N, Warszawski L, Wiltshire A, Woodward FI. 2014. Carbon residence time dominates uncertainty in terrestrial vegetation responses to future climate and atmospheric CO2. Proceedings of the National Academy of Sciences of the United States of America 111:3280–5.

    CAS  PubMed  Google Scholar 

  • Galbraith D, Levy PE, Sitch S, Huntingford C, Cox P, Williams M, Meir P. 2010. Multiple mechanisms of Amazonian forest biomass losses in three dynamic global vegetation models under climate change. New Phytologist 187:647–65.

    PubMed  Google Scholar 

  • Galiano L, Martinez-Vilalta J, Sabate S, Lloret F. 2012. Determinants of drought effects on crown condition and their relationship with depletion of carbon reserves in a Mediterranean holm oak forest. Tree Physiology 32:478–89.

    PubMed  Google Scholar 

  • Greenwood S, Ruiz-Benito P, Martinez-Vilalta J, Lloret F, Kitzberger T, Allen CD, Fensham R, Laughlin DC, Kattge J, Bonisch G, Kraft NJB, Jump AS. 2017. Tree mortality across biomes is promoted by drought intensity, lower wood density and higher specific leaf area. Ecology Letters 20:539–53.

    PubMed  Google Scholar 

  • Gutierrez AG, Snell RS, Bugmann H. 2016. Using a dynamic forest model to predict tree species distributions. Global Ecology and Biogeography 25:347–58.

    Google Scholar 

  • Hartmann H, Moura CF, Anderegg WRL, Ruehr NK, Salmon Y, Allen CD, Arndt SK, Breshears DD, Davi H, Galbraith D, Ruthrof KX, Wunder J, Adams HD, Bloemen J, Cailleret M, Cobb R, Gessler A, Grams TEE, Jansen S, Kautz M, Lloret F, O’Brien M. 2018. Research frontiers for improving our understanding of drought-induced tree and forest mortality. New Phytologist 218:15–28.

    PubMed  Google Scholar 

  • Hawkes C. 2000. Woody plant mortality algorithms: description, problems and progress. Ecological Modelling 126:225–48.

    Google Scholar 

  • Holgen P, Mattsson L, Li CZ. 2000. Recreation values of boreal forest stand types and landscapes resulting from different silvicultural systems: An economic analysis. Journal of Environmental Management 60:173–80.

    Google Scholar 

  • Huber N. 2019. Towards robust projections of future forest dynamics: Why there is no silver bullet to cope with complexity. PhD Thesis. D-USYS. ETH Zurich. p. 294.

  • Huber N, Bugmann H, Lafond V. 2018. Global sensitivity analysis of a dynamic vegetation model: Model sensitivity depends on successional time, climate and competitive interactions. Ecological Modelling 368:377–90.

    Google Scholar 

  • Hülsmann L, Bugmann H, Brang P. 2017. How to predict tree death from inventory data—lessons from a systematic assessment of European tree mortality models. Canadian Journal of Forest Research 47:890–900.

    Google Scholar 

  • Hülsmann L, Bugmann H, Cailleret M, Brang P. 2018. How to kill a tree: empirical mortality models for 18 species and their performance in a dynamic forest model. Ecological Applications 28:522–40.

    PubMed  Google Scholar 

  • Keane RE, Austin M, Field C, Huth A, Lexer MJ, Peters D, Solomon A, Wyckoff P. 2001. Tree mortality in gap models: Application to climate change. Climatic Change 51:509–40.

    Google Scholar 

  • Kullman L. 1997. Tree-limit stress and disturbance—A 25-year survey of geoecological change in the scandes mountains of Sweden. Geografiska Annaler Series a-Physical Geography 79a: 139–165.

    Google Scholar 

  • Leibundgut H. 1993. Europäische Urwälder - Wegweiser zur naturnahen Waldwirtschaft. Bern, Stuttgart: Paul Haupt Verlag.

    Google Scholar 

  • Lembcke G, Knapp E, Dittmar O. 2000. Ertragstafel für die Kiefer (Pinus sylvestris L.) im norddeutschen Tiefland. Eberswalde, Germany: Landesforstanstalt Eberswalde.

  • Lutz JA, Halpern CB. 2006. Tree mortality during early forest development: A long-term study of rates, causes, and consequences. Ecological Monographs 76:257–75.

    Google Scholar 

  • Manusch C, Bugmann H, Heiri C, Wolf A. 2012. Tree mortality in dynamic vegetation models—A key feature for accurately simulating forest properties. Ecological Modelling 243:101–11.

    Google Scholar 

  • Mason WL, Connolly T, Pommerening A, Edwards C. 2007. Spatial structure of semi-natural and plantation stands of Scots pine (Pinus sylvestris L.) in northern Scotland. Forestry 80:564–83.

    Google Scholar 

  • Mátyás C, Ackzell L, Samuel CJA. 2004. EUFORGEN technical guidelines for genetic conservation and use for Scots pine (Pinus sylvestris). Rome, Italy: International Plant Genetic Resources Institute. p p6.

    Google Scholar 

  • McDowell NG, Fisher RA, Xu CG, Domec JC, Holtta T, Mackay DS, Sperry JS, Boutz A, Dickman L, Gehres N, Limousin JM, Macalady A, Martinez-Vilalta J, Mencuccini M, Plaut JA, Ogee J, Pangle RE, Rasse DP, Ryan MG, Sevanto S, Waring RH, Williams AP, Yepez EA, Pockman WT. 2013. Evaluating theories of drought-induced vegetation mortality using a multimodel-experiment framework. New Phytologist 200:304–21.

    CAS  PubMed  Google Scholar 

  • Mina M, Bugmann H, Klopcic M, Cailleret M. 2017. Accurate modeling of harvesting is key for projecting future forest dynamics: a case study in the Slovenian mountains. Regional Environmental Change 17:49–64.

    Google Scholar 

  • Monserud RA. 1976. Simulation of forest tree mortality. Forest Science 22:438–44.

    Google Scholar 

  • Monserud RA, Sterba H. 1999. Modeling individual tree mortality for Austrian forest species. Forest Ecology and Management 113:109–23.

    Google Scholar 

  • Niinemets U. 2010. Responses of forest trees to single and multiple environmental stresses from seedlings to mature plants: Past stress history, stress interactions, tolerance and acclimation. Forest Ecology and Management 260:1623–39.

    Google Scholar 

  • Palahi M, Pukkala T, Miina J, Montero G. 2003. Individual-tree growth and mortality models for Scots pine (Pinus sylvestris L.) in north-east Spain. Annals of Forest Science 60:1–10.

    Google Scholar 

  • Pretzsch H. 2006. Species-specific allometric scaling under self-thinning: evidence from long-term plots in forest stands. Oecologia 146:572–83.

    PubMed  Google Scholar 

  • Rödig E, Cuntz M, Rammig A, Fischer R, Taubert F, Huth A. 2018. The importance of forest structure for carbon fluxes of the Amazon rainforest. Environmental Research Letters 13:1–11.

    Google Scholar 

  • Scheller RM, Kretchun AM, Loudermilk EL, Hurteau MD, Weisberg PJ, Skinner C. 2018. Interactions among fuel management, species composition, bark beetles, and climate change and the potential effects on forests of the Lake Tahoe Basin. Ecosystems 21:643–56.

    CAS  Google Scholar 

  • Seidl R, Thom D, Kautz M, Martin-Benito D, Peltoniemi M, Vacchiano G, Wild J, Ascoli D, Petr M, Honkaniemi J, Lexer MJ, Trotsiuk V, Mairota P, Svoboda M, Fabrika M, Nagel TA, Reyer CPO. 2017. Forest disturbances under climate change. Nature Climate Change 7:395–402.

    PubMed  PubMed Central  Google Scholar 

  • Shao GF, Bugmann H, Yan XD. 2001. A comparative analysis of the structure and behavior of three gap models at sites in northeastern China. Climatic Change 51:389–413.

    Google Scholar 

  • Shugart HH, Wang B, Fischer R, Ma JY, Fang J, Yan XD, Huth A, Armstrong AH. 2018. Gap models and their individual-based relatives in the assessment of the consequences of global change. Environmental Research Letters 13:1–17.

    Google Scholar 

  • Shuman JK, Shugart HH, O’Halloran TL. 2011. Sensitivity of Siberian larch forests to climate change. Global Change Biology 17:2370–84.

    Google Scholar 

  • Sterba H. 1995. Forest decline and increasing increments—a simulation study. Forestry 68:153–63.

    Google Scholar 

  • Temperli C, Veblen TT, Hart SJ, Kulakowski D, Tepley AJ. 2015. Interactions among spruce beetle disturbance, climate change and forest dynamics captured by a forest landscape model. Ecosphere 6:1–20.

    Google Scholar 

  • Thom D, Rammer W, Seidl R. 2017. The impact of future forest dynamics on climate: interactive effects of changing vegetation and disturbance regimes. Ecological Monographs 87:665–84.

    PubMed  PubMed Central  Google Scholar 

  • Thurner M, Beer C, Carvalhais N, Forkel M, Santoro M, Tum M, Schmullius C. 2016. Large-scale variation in boreal and temperate forest carbon turnover rate related to climate. Geophysical Research Letters 43:4576–85.

    CAS  Google Scholar 

  • Thurner M, Beer C, Ciais P, Friend AD, Ito A, Kleidon A, Lomas MR, Shaun QG, Rademacher TT, Schaphoff S, Tum M, Wiltshire A, Carvalhais N. 2017. Evaluation of climate-related carbon turnover processes in global vegetation models for boreal and temperate forests. Global Change Biology 23:3076–91.

    PubMed  PubMed Central  Google Scholar 

  • Trasobares A, Pukkala T, Muna J. 2004. Growth and yield model for uneven-aged mixtures of Pinus sylvestris L. and Pinus nigra Arn. in Catalonia, north-east Spain. Annals of Forest Science 61:9–24.

    Google Scholar 

  • Vanoni M, Bugmann H, Notzli M, Bigler C. 2016. Drought and frost contribute to abrupt growth decreases before tree mortality in nine temperate tree species. Forest Ecology and Management 382:51–63.

    Google Scholar 

  • Vanoni M, Cailleret M, Hülsmann L, Bugmann H, Bigler C. 2019. How do tree mortality models from combined tree-ring and inventory data affect projections of forest succession? Forest Ecology and Management 433:606–17.

    Google Scholar 

  • Weiskittel AR, Hann DW, Kershaw JA, Vanclay JK. 2011. Mortality. Forest growth and yield modeling. Oxford, UK: Wiley. pp 139–55.

    Google Scholar 

  • Williams JW, Jackson ST. 2007. Novel climates, no-analog communities, and ecological surprises. Frontiers in Ecology and the Environment 5:475–82.

    Google Scholar 

  • Wood CM, Bunce RGH. 2016. Ecological survey of the native pinewoods of Scotland 1971. Earth System Science Data 8:177–89.

    Google Scholar 

  • Wunder J, Brzeziecki B, Zybura H, Reineking B, Bigler C, Bugmann H. 2008. Growth-mortality relationships as indicators of life-history strategies: a comparison of nine tree species in unmanaged European forests. OIKOS 117:815–28.

    Google Scholar 

  • Wyckoff PH, Clark JS. 2002. The relationship between growth and mortality for seven co-occurring tree species in the southern Appalachian Mountains. Journal of Ecology 90:604–15.

    Google Scholar 

  • Yang H, Piao SL, Zeng ZZ, Ciais P, Yin Y, Friedlingstein P, Sitch S, Ahlstrom A, Guimberteau M, Huntingford C, Levis S, Levy PE, Huang MT, Li Y, Li XR, Lomas MR, Peylin P, Poulter B, Viovy N, Zaehle S, Zeng N, Zhao F, Wang L. 2015. Multicriteria evaluation of discharge simulation in dynamic global vegetation models. Journal of Geophysical Research-Atmospheres 120:7488–505.

    Google Scholar 

  • Yates KL, Bouchet PJ, Caley MJ, Mengersen K, Randin CF, Parnell S, Fielding AH, Bamford AJ, Ban S, Barbosa A, Dormann CF, Elith J, Embling CB, Ervin GN, Fisher R, Gould S, Graf RF, Gregr EJ, Halpin PN, Heikkinen RK, Heinanen S, Jones AR, Krishnakumar PK, Lauria V, Lozano-Montes H, Mannocci L, Mellin C, Mesgaran MB, Moreno-Amat E, Mormede S, Novaczek E, Oppel S, Crespo GO, Peterson AT, Rapacciuolo G, Roberts JJ, Ross RE, Scales KL, Schoeman D, Snelgrove P, Sundblad G, Thuiller W, Torres LG, Verbruggen H, Wang L, Wenger S, Whittingham MJ, Zharikov Y, Zurell D, Sequeira AMM. 2018. Outstanding Challenges in the transferability of ecological models. Trends in Ecology & Evolution 33:790–802.

    Google Scholar 

Download references

Acknowledgements

This work was funded by Swiss National Science Foundation project ‘Advanced Tree MOrtality MOdeling’ (ATMO^2, Project Number 163250). LH received funding by the Bavarian Ministry of Science and the Arts in the context of the Bavarian Climate Research Network (bayklif). We are grateful for the support by Dominic Michel in all IT-related questions. We furthermore thank Peter Brang, Jonas Stillhard and the Swiss Forest Reserve Project, a joint endeavor of the Swiss Federal Research Institute WSL, the Swiss Federal Institute of Technology (ETH) Zurich and the Swiss Federal Office of the Environment, for providing access to the Pfynwald data. We also acknowledge the helpful comments of two anonymous reviewers.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Timothy Thrippleton.

Additional information

Author’s Contribution

TT and HB designed the study, TT conducted the simulation experiments and data analysis, TT, LH, MC and HB wrote the paper.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 2841 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Thrippleton, T., Hülsmann, L., Cailleret, M. et al. Projecting Forest Dynamics Across Europe: Potentials and Pitfalls of Empirical Mortality Algorithms. Ecosystems 23, 188–203 (2020). https://doi.org/10.1007/s10021-019-00397-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10021-019-00397-3

Keywords

  • dynamic vegetation model
  • tree mortality
  • mortality models
  • forest dynamics
  • climate change
  • environmental gradient
  • Pinus sylvestris