Models are pivotal for assessing future forest dynamics under the impacts of changing climate and management practices, incorporating representations of tree growth, mortality, and regeneration. Quantitative studies on the importance of mortality submodels are scarce. We evaluated 15 dynamic vegetation models (DVMs) regarding their sensitivity to different formulations of tree mortality under different degrees of climate change. The set of models comprised eight DVMs at the stand scale, three at the landscape scale, and four typically applied at the continental to global scale. Some incorporate empirically derived mortality models, and others are based on experimental data, whereas still others are based on theoretical reasoning. Each DVM was run with at least two alternative mortality submodels. Model behavior was evaluated against empirical time series data, and then, the models were subjected to different scenarios of climate change. Most DVMs matched empirical data quite well, irrespective of the mortality submodel that was used. However, mortality submodels that performed in a very similar manner against past data often led to sharply different trajectories of forest dynamics under future climate change. Most DVMs featured high sensitivity to the mortality submodel, with deviations of basal area and stem numbers on the order of 10-40% per century under current climate and 20-170% under climate change. The sensitivity of a given DVM to scenarios of climate change, however, was typically lower by a factor of two to three. We conclude that (1) mortality is one of the most uncertain processes when it comes to assessing forest response to climate change, and (2) more data and a better process understanding of tree mortality are needed to improve the robustness of simulated future forest dynamics. Our study highlights that comparing several alternative mortality formulations in DVMs provides valuable insights into the effects of process uncertainties on simulated future forest dynamics.

CREAF Campus de Bellaterra Edifici C 08193 Cerdanyola del Vallès Spain

Department de Biologia Evolutiva Ecologia i Ciències Ambientals Universitat de Barcelona Av Diagonal 643 08028 Barcelona Spain

Department of Physical Geography Goethe University Frankfurt Main Germany

Faculty of Forestry Technical University in Zvolen T G Masaryka 24 Zvolen 96053 Slovakia

Forest Ecology ETH Zürich Universitätstrasse 22 8092 Zürich Switzerland

German Centre for Integrative Biodiversity Research Halle Jena Leipzig Leipzig Germany

Helmholtz Centre for Environmental Research UFZ Leipzig Germany

Institute for Environmental Systems Research University of Osnabrück Osnabrück Germany

Institute of Botany The Czech Academy of Sciences Průhonice Czech Republic

Institute of Meteorology and Climate Research Atmospheric Environmental Research Karlsruhe Institute of Technology Garmisch Partenkirchen Germany

Irstea LESSEM Univ Grenoble Alpes 38000 Grenoble France

Los Alamos National Laboratory Los Alamos New Mexico 87544 USA

Member of the Leibniz Association Potsdam Institute for Climate Impact Research Potsdam Germany

Research Unit Forest Dynamics Swiss Federal Institute for Forest Snow and Landscape Research WSL Zürcherstrasse 111 8903 Birmensdorf Switzerland

Senckenberg Biodiversity and Climate Research Centre BiK F Frankfurt Main Germany

Soil and Climate Department Bavarian State Institute of Forestry 85354 Freising Germany

Theoretical Ecology University of Regensburg Universitätsstraße 31 93053 Regensburg Germany

TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany

Unit for Modelling of Climate and Biogeochemical Cycles UR SPHERES University of Liège Liège Belgium

Università degli Studi di Milano DISAA 20123 Milano Italy

University of Natural Resources and Life Sciences Vienna Peter Jordan Straße 82 1190 Wien Austria

USDA Forest Service Northern Research Station Rhinelander Wisconsin USA

Zobrazit více v PubMed

Adams, H. D. , Guardiola‐Claramonte M., Barron‐Gafford G. A., Villegas J. C., Breshears D. D., Zou C. B., Troch P. A., and Huxman T. E.. 2009. Temperature sensitivity of drought‐induced tree mortality portends increased regional die‐off under global‐change‐type drought. Proceedings of the National Academy of Sciences USA 106:7063–7066. PubMed PMC

Allen, C. D. , Breshears D. D., and McDowell N. G.. 2015. On underestimation of global vulnerability to tree mortality and forest die‐off from hotter drought in the Anthropocene. Ecosphere 6:129.

Allen, C. D. , et al. 2010. A global overview of drought and heat‐induced tree mortality reveals emerging climate change risks for forests. Forest Ecology and Management 259:660–684.

Anderegg, W. R. L. , et al. 2016. When a tree dies in the forest: scaling climate‐driven tree mortality to ecosystem water and carbon fluxes. Ecosystems 19:1133–1147.

Augustynczik, A. L. D. , Hartig F., Minunno F., Kahle H. P., Diaconu D., Hanewinkel M., and Yousefpour R.. 2017. Productivity of Fagus sylvatica under climate change ‐ A Bayesian analysis of risk and uncertainty using the model 3‐PG. Forest Ecology and Management 401:192–206.

Bigler, C. , Bräker O. U., Bugmann H., Dobbertin M., and Rigling A.. 2006. Drought as an inciting mortality factor in Scots pine stands of the Valais, Switzerland. Ecosystems 9:330–343.

Bigler, C. , and Veblen T. T.. 2009. Increased early growth rates decrease longevities of conifers in subalpine forests. Oikos 118:1130–1138.

Bircher, N. 2015. To die or not to die: forest dynamics in Switzerland under climate change. Thesis No. 22775. ETH Zürich, Zürich, Switzerland.

Bircher, N. , Cailleret M., and Bugmann H.. 2015. The agony of choice: different empirical mortality models lead to sharply different future forest dynamics. Ecological Applications 25:1303–1318. PubMed

Bohn, F. J. , Frank K., and Huth A.. 2014. Of climate and its resulting tree growth: simulating the productivity of temperate forests. Ecological Modelling 278:9–17.

Bondeau, A. , et al. 2007. Modelling the role of agriculture for the 20th century global terrestrial carbon balance. Global Change Biology 13:679–706.

Botkin, D. B. , Janak J. F., and Wallis J. R.. 1972. Some ecological consequences of a computer model of forest growth. Journal of Ecology 60:849–872.

Brienen, R. J. W. , et al. 2015. Long‐term decline of the Amazon carbon sink. Nature 519:344–348. PubMed

Bugmann, H. 1996. A simplified forest model to study species composition along climate gradients. Ecology 77:2055–2074.

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

Bugmann, H. , and Bigler C.. 2011. Will the CO2 fertilization effect in forests be offset by reduced tree longevity? Oecologia 165:533–544. PubMed

Bugmann, H. K. M. , Yan Xiaodong S. M. T., Martin P., Lindner M., Desanker P. V., and Cumming S. G.. 1996. A comparison of forest gap models: model structure and behaviour. Climatic Change 34:289–313.

Clark, J. S. , Beckage B., Camill P., Cleveland B., HilleRisLambers J., Lichter J., McLachlan J., Mohan J., and Wyckoff P.. 1999. Interpreting recruitment limitation in forests. American Journal of Botany 86:1–16. PubMed

Cramer, W. , et al. 2001. Global response of terrestrial ecosystem structure and function to CO2 and climate change: results from six dynamic global vegetation models. Global Change Biology 7:357–373.

Dai, A. G. 2011. Drought under global warming: a review. Wiley Interdisciplinary Reviews‐Climate Change 2:45–65.

Dury, M. , Hambuckers A., Warnant P., Henrot A., Favre E., Ouberdous M., and Francois L.. 2011. Responses of European forest ecosystems to 21st century climate: assessing changes in interannual variability and fire intensity. Iforest‐Biogeosciences and Forestry 4:82–99.

Elkin, C. , Gutierrez A. G., Leuzinger S., Manusch C., Temperli C., Rasche L., and Bugmann H.. 2013. A 2°C warmer world is not safe for ecosystem services in the European Alps. Global Change Biology 19:1827–1840. PubMed

Fabrika, M. 2005. Simulátor biodynamiky lesa SIBYLA, koncepcia, konštrukcia a programové riešenie. Zvolen, Slovakia, Habilitačná práca, Technická univerzita vo Zvolene.

Fischer, R. , et al. 2016. Lessons learned from applying a forest gap model to understand ecosystem and carbon dynamics of complex tropical forests. Ecological Modelling 326:124–133.

Friend, A. D. , et al. 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 111:3280–3285. PubMed PMC

Grimm, V. , Revilla E., Berger U., Jeltsch F., Mooij W. M., Railsback S. F., Thulke H. H., Weiner J., Wiegand T., and DeAngelis D. L.. 2005. Pattern‐oriented modeling of agent‐based complex systems: lessons from ecology. Science 310:987–991. PubMed

Hartig, F. , Dyke J., Hickler T., Higgins S. I., O'Hara R. B., Scheiter S., and Huth A.. 2012. Connecting dynamic vegetation models to data ‐ an inverse perspective. Journal of Biogeography 39:2240–2252.

He, H. S. 2008. Forest landscape models: definitions, characterization, and classification. Forest Ecology and Management 254:484–498.

Heiri, C. , Bugmann H., Tinner W., Heiri O., and Lischke H.. 2006. A model‐based reconstruction of Holocene treeline dynamics in the Central Swiss Alps. Journal of Ecology 94:206–216.

Henne, P. D. , Elkin C. M., Reineking B., Bugmann H., and Tinner W.. 2011. Did soil development limit spruce (Picea abies) expansion in the Central Alps during the Holocene? Testing a palaeobotanical hypothesis with a dynamic landscape model. Journal of Biogeography 38:933–949.

Hickler, T. , et al. 2012. Projecting the future distribution of European potential natural vegetation zones with a generalized, tree species‐based dynamic vegetation model. Global Ecology and Biogeography 21:50–63.

Hlásny, T. , Barcza Z., Barka I., Merganičová K., Sedmák R., Kern A., Pajtík J., Balázs B., Fabrika M., and Churkina G.. 2014. Future carbon cycle in mountain spruce forests of Central Europe: modelling framework and ecological inferences. Forest Ecology and Management 328:55–68.

Hülsmann, L. , Bugmann H., and 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.

Hülsmann, L. , Bugmann H., Cailleret M., and Brang P.. 2018. How to kill a tree – Empirical mortality models for eighteen species and their performance in a dynamic forest model. Ecological Applications 28:522–540. PubMed

IPCC . 2013. Climate Change 2013: the physical science basis. Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, UK.

Irauschek, F. , Rammer W., and Lexer M. J.. 2017. Can current management maintain forest landscape multifunctionality in the Eastern Alps in Austria under climate change? Regional Environmental Change 17:33–48.

Keane, R. E. , Austin M., Field C., Huth A., Lexer M. J., Peters D., Solomon A., and Wyckoff P.. 2001. Tree mortality in gap models: application to climate change. Climatic Change 51:509–540.

Keane, R. E. , McKenzie D., Falk D. A., Smithwick E. A. H., Miller C., and Kellogg L.‐K. B.. 2015. Representing climate, disturbance, and vegetation interactions in landscape models. Ecological Modelling 309–310:33–47.

Lasch‐Born, P. , Suckow F., Gutsch M., Reyer C., Hauf Y., Murawski A., and Pilz T.. 2015. Forests under climate change: potential risks and opportunities. Meteorologische Zeitschrift 24:157–172.

Lexer, M. J. , and Hönninger K.. 2001. A modified 3D‐patch model for spatially explicit simulation of vegetation composition in heterogeneous landscapes. Forest Ecology and Management 144:43–65.

Lischke, H. , Lotter A., and Fischlin A.. 1999. Untangling a post‐glacial pollen record with forest model simulations and independent climate data. Ecological Modelling 150:1–21.

Liu, J. , and Ashton P. S.. 1995. Individual‐based simulation models for forest succession and management. Forest Ecology and Management 73:157–175.

Lloret, F. , Escudero A., Iriondo J. M., Martinez‐Vilalta J., and Valladares F.. 2012. Extreme climatic events and vegetation: the role of stabilizing processes. Global Change Biology 18:797–805.

Manusch, C. , Bugmann H., Heiri C., and Wolf A.. 2012. Tree mortality in dynamic vegetation models – A key feature for accurately simulating forest properties. Ecological Modelling 243:101–111.

Martinez‐Vilalta, J. , and Lloret F.. 2016. Drought‐induced vegetation shifts in terrestrial ecosystems: the key role of regeneration dynamics. Global and Planetary Change 144:94–108.

McDowell, N. G. , Beerling D. J., Breshears D. D., Fisher R. A., Raffa K. F., and Stitt M.. 2011. The interdependence of mechanisms underlying climate‐driven vegetation mortality. Trends in Ecology & Evolution 26:523–532. PubMed

McDowell, N. G. , et al. 2013. Evaluating theories of drought‐induced vegetation mortality using a multimodel‐experiment framework. New Phytologist 200:304–321. PubMed

McDowell, N. G. , et al. 2016. Multi‐scale predictions of massive conifer mortality due to chronic temperature rise. Nature Climate Change 6:1048–1048.

Mette, T. 2014. Modelling Patagonian Lenga‐forest dynamics (Nothofagus pumilio) in Chile. Final report, DFG project ME 3568/2‐1, Bonn, Germany.

Mette, T. , Albrecht A., Ammer C., Biber P., Kohnle U., and Pretzsch H.. 2009. Evaluation of the forest growth simulator SILVA on dominant trees in mature mixed Silver fir–Norway spruce stands in South‐West Germany. Ecological Modelling 220:1670–1680.

Monserud, R. A. , and Sterba H.. 1999. Modeling individual tree mortality for Austrian forest species. Forest Ecology and Management 113:109–123.

Moorcroft, P. R. , Hurtt G. C., and Pacala S. W.. 2001. A method for scaling vegetation dynamics: the ecosystem demography model (ED). Ecological Monographs 71:557–585.

Morales, P. , et al. 2005. Comparing and evaluating process‐based ecosystem model predictions of carbon and water fluxes in major European forest biomes. Global Change Biology 11:2211–2233. PubMed

Nadal‐Sala, D. , Keenan T. F., Sabaté S., and Gracia C.. 2017. Forest eco‐physiological models: water use and carbon sequestration. Pages 81–102 in Bravo F., LeMay V., and Jandl R., editors. Managing forest ecosystems: the challenge of climate change. Springer, Berlin, Germany.

Neumann, M. , Nues V., Moreno A., Hasenauer H., and Seidl R.. 2017. Climate variability drives recent tree mortality in Europe. Global Change Biology 23:4788–4797. PubMed PMC

Peters, G. P. , Andrew R. M., Boden T., Canadell J. G., Ciais P., Le Quere C., Marland G., Raupach M. R., and Wilson C.. 2013. The challenge to keep global warming below 2°C. Nature Climate Change 3:4–6.

Piao, S. L. , et al. 2013. Evaluation of terrestrial carbon cycle models for their response to climate variability and to CO2 trends. Global Change Biology 19:2117–2132. PubMed

Pretzsch, H. 2009. Forest dynamics, growth and yield. Springer, Berlin, Germany.

Pretzsch, H. , Biber P., and Dursky J.. 2002. The single tree‐based stand simulator SILVA: construction, application and evaluation. Forest Ecology and Management 162:3–21.

R Core Team . 2017. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.

Rasche, L. , Fahse L., and Bugmann H.. 2013. Key factors affecting the future provision of tree‐based forest ecosystem goods and services. Climatic Change 118:579–593.

Reyer, C. , Lasch‐Born P., Suckow F., Gutsch M., Murawski A., and Pilz T.. 2014. Projections of regional changes in forest net primary productivity for different tree species in Europe driven by climate change and carbon dioxide. Annals of Forest Science 71:211–225.

Reyer, C. P. O. , et al. 2015. Forest resilience and tipping points at different spatio‐temporal scales: approaches and challenges. Journal of Ecology 103:5–15.

Schaphoff, S. , Heyder U., Ostberg S., Gerten D., Heinke J., and Lucht W.. 2013. Contribution of permafrost soils to the global carbon budget. Environmental Research Letters 8:014026.

Scheffer, M. 2010. Complex systems: foreseeing tipping points. Nature 467:411–412. PubMed

Scheffer, M. , Carpenter S., Foley J. A., Folke C., and Walker B.. 2001. Catastrophic shifts in ecosystems. Nature 413:591–596. PubMed

Scheller, R. M. , and Mladenoff D. J.. 2004. A forest growth and biomass module for a landscape simulation model, LANDIS: design, validation, and application. Ecological Modelling 180:211–229.

Schumacher, S. , Reineking B., Sibold J., and Bugmann H.. 2006. Modeling the impact of climate and vegetation on fire regimes in mountain landscapes. Landscape Ecology 21:539–554.

Seidl, R. , and Rammer W.. 2017. Climate change amplifies the interactions between wind and bark beetle disturbances in forest landscapes. Landscape Ecology 32:1485–1498. PubMed PMC

Seidl, R. , Rammer W., and Lexer M. J.. 2011. Adaptation options to reduce climate change vulnerability of sustainable forest management in the Austrian Alps. Canadian Journal of Forest Research 41:694–706.

Seidl, R. , Rammer W., Scheller R. M., and Spies T. A.. 2012. An individual‐based process model to simulate landscape‐scale forest ecosystem dynamics. Ecological Modelling 231:87–100.

Seidl, R. , Schelhaas M. J., Rammer W., and Verkerk P. J.. 2014. Increasing forest disturbances in Europe and their impact on carbon storage. Nature Climate Change 4:806–810. PubMed PMC

Seidl, R. , et al. 2017. Forest disturbances under climate change. Nature Climate Change 7:395–402. PubMed PMC

Settele, J. , et al. 2014. Terrestrial and inland water systems. Pages 271–359 in Field C. B., et al., editors. Climate Change 2014: impacts, adaptation, and vulnerability. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, UK: and New York, New York, USA.

Shugart, H. H. 1984. A theory of forest dynamics. The ecological implications of forest succession models. Springer, New York, New York, USA.

Sitch, S. , et al. 2008. Evaluation of the terrestrial carbon cycle, future plant geography and climate‐carbon cycle feedbacks using five Dynamic Global Vegetation Models (DGVMs). Global Change Biology 14:2015–2039.

Smith, B. , Warlind D., Arneth A., Hickler T., Leadley P., Siltberg J., and Zaehle S.. 2014. Implications of incorporating N cycling and N limitations on primary production in an individual‐based dynamic vegetation model. Biogeosciences 11:2027–2054.

Solomon, A. M. 1986. Transient response of forests to CO2‐induced climate change: simulation modeling experiments in eastern North America. Oecologia 68:567–579. PubMed

Steinkamp, J. , and Hickler T.. 2015. Is drought‐induced forest dieback globally increasing? Journal of Ecology 103:31–43.

Temperli, C. , Bugmann H., and Elkin C.. 2013a. Cross‐scale interactions among bark beetles, climate change, and wind disturbances: a landscape modeling approach. Ecological Monographs 83:383–402.

Temperli, C. , Zell J., Bugmann H., and Elkin C.. 2013b. Sensitivity of ecosystem goods and services projections of a forest landscape model to initialization data. Landscape Ecology 28:1337–1352.

Warnant, P. , Francois L., Strivay D., and Gerard J. C.. 1994. CARAIB ‐ a global model of terrestrial biological productivity. Global Biogeochemical Cycles 8:255–270.

Warszawski, L. , Frieler K., Huber V., Piontek F., Serdeczny O., and Schewe J.. 2014. The Inter‐Sectoral Impact Model Intercomparison Project (ISI‐MIP): project framework. Proceedings of the National Academy of Sciences of the United States of America 111:3228–3232. PubMed PMC

Williams, A. P. , et al. 2013. Temperature as a potent driver of regional forest drought stress and tree mortality. Nature Climate Change 3:292–297.

Wykoff, W. R. , Crookston N. L., and Stage A. R.. 1982. User's guide to the Stand Prognosis Model. Gen. Tech. Rep. INT‐133. U.S. Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station, Ogden, Utah, USA.

Yoda, K. , Kira T., Ogawa H., and Hozumi K.. 1963. Self‐thinning in over‐crowded pure stands under cultivated and natural conditions (Intraspecific competition among higher plants XI). Journal of the Institute of Polytechnics, Osaka City University, Series D 14:107–129.

Tackling unresolved questions in forest ecology: The past and future role of simulation models

Limited capacity of tree growth to mitigate the global greenhouse effect under predicted warming