Using hybrid modelling to predict basal area and evaluate effects of climate change on growth of Norway spruce and Scots pine stands

References

• Allen CD, Macalady AK, Chenchouni H, Bachelet D, McDowell N, Vennetier M, Kitzberger T, Rigling A, Breshears DD, Hogg EH, et al. 2010. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. For Ecol Manag. 259:660–684. doi:https://doi.org/10.1016/j.foreco.2009.09.001.
   Web of Science ®Google Scholar
• Allen RG, Pereira LS, Raes D, Smith M. 1998. Crop evapotranspiration - Guidelines for computing crop water requirements. FAO Irrig drain. 56:1–300.
   Google Scholar
• Alley WM. 1984. On the Treatment of Evapotranspiration, Soil-Moisture Accounting, and Aquifer Recharge in Monthly Water-Balance Models. Water Resour Res. 20:1137–1149. doi:https://doi.org/10.1029/WR020i008p01137.
   Web of Science ®Google Scholar
• Andersson S, Wiklert P. 1972. Markfysikaliska undersökningar i odlad jord. J Agric Land Improv. XXIII:53–143.
   Google Scholar
• Baldwin VC, Burkhart HE, Westfall JA, Peterson KD. 2001. Linking growth and yield and process models to estimate impact of environmental changes on growth of loblolly pine. For Sci. 47:77–82.
   Google Scholar
• Battaglia M, Sands PJ, Candy SG. 1999. Hybrid growth model to predict height and volume growth in young Eucalyptus globulus plantations. For Ecol Manag. 120:193–201. doi:https://doi.org/10.1016/S0378-1127(98)00548-9.
   Web of Science ®Google Scholar
• Belyazid S, Zanchi G. 2019. Water limitation can negate the effect of higher temperatures on forest carbon sequestration. Eur J For Res. 138:287–297. doi:https://doi.org/10.1007/s10342-019-01168-4.
   Web of Science ®Google Scholar
• Bergh J, Linder S, Bergstrom J. 2005. Potential production of Norway spruce in Sweden. For Ecol Manag. 204:1–10. doi:https://doi.org/10.1016/j.foreco.2004.07.075.
   Web of Science ®Google Scholar
• Bergh J, Linder S, Lundmark T, Elfving B. 1999. The effect of water and nutrient availability on the productivity of Norway spruce in northern and southern Sweden. For Ecol Manag. 119:51–62. doi:https://doi.org/10.1016/S0378-1127(98)00509-X.
   Web of Science ®Google Scholar
• Bergh J, McMurtrie RE, Linder S. 1998. Climatic factors controlling the productivity of Norway spruce: A model-based analysis. For Ecol Manag. 110:127–139. doi:https://doi.org/10.1016/S0378-1127(98)00280-1.
   Web of Science ®Google Scholar
• Boisvenue C, Running SW. 2006. Impacts of climate change on natural forest productivity - evidence since the middle of the 20th century. Glb Chg Bio. 12:862–882. doi:https://doi.org/10.1111/j.1365-2486.2006.01134.x.
   Web of Science ®Google Scholar
• Cannell MGR. 1989. Physiological Basis of Wood Production: a Review. Scand J For Res. 4:459–490. doi:https://doi.org/10.1080/02827588909382582.
   Web of Science ®Google Scholar
• Cattiaux J, Douville H, Peings Y. 2013. European temperatures in CMIP5: origins of present-day biases and future uncertainties. Clim Dyn. 41:2889–2907. doi:https://doi.org/10.1007/s00382-013-1731-y.
   Web of Science ®Google Scholar
• Clapp RB, Hornberger GM. 1978. Empirical Equations for Some Soil Hydraulic-Properties. Water Resour Res. 14:601–604. doi:https://doi.org/10.1029/WR014i004p00601.
   Web of Science ®Google Scholar
• Clutter JL. 1963. Compatible Growth and Yield Models for Loblolly Pine. For Sci. 9:354–371. doi:https://doi.org/10.1093/forestscience/9.3.354.
   Google Scholar
• Coumou D, Rahmstorf S. 2012. A decade of weather extremes. Nat Clim Change. 2:491–496. doi:https://doi.org/10.1038/Nclimate1452.
   Web of Science ®Google Scholar
• Destouni G, Verrot L. 2014. Screening long-term variability and change of soil moisture in a changing climate. J Hydrol. 516:131–139. doi:https://doi.org/10.1016/j.jhydrol.2014.01.059.
   Web of Science ®Google Scholar
• Dunne KA, Willmott CJ. 1996. Global distribution of plant-extractable water capacity of soil. Int J Climatol. 16:841–859. doi:https://doi.org/10.1002/(SICI)1097-0088(199608)16:8%3C841::AID-JOC60%3E3.0.CO;2-8.
   Web of Science ®Google Scholar
• Dzierzon H, Mason EG. 2006. Towards a nationwide growth and yield model for radiata pine plantations in New Zealand. Can J For Res.-Revue Canadienne De Recherche Forestiere. 36:2533–2543. doi:https://doi.org/10.1139/X06-214.
   Web of Science ®Google Scholar
• Eklund A, Axén Mårtensson J, Bergström S, Björck E, Dahné J, Lindström L, Nordborg D, Olsson J, Simonsson L, Sjökvist E. 2015. Sveriges framtida klimat- Underlag till Dricksvattenutredningen. Norrköping, Sweden: SMHI Klimatologi.
   Google Scholar
• Elfving B. 2010. Growth modelling in the Heureka system [Online]. Swedish University of Agricultural Sciences, Faculty of Forestry. Available: http://heurekaslu.org/wiki/Heureka_prognossystem_(Elfving_rapportutkast).pdf [Accessed 8 August 2019].
   Google Scholar
• Elfving B, Kiviste A. 1997. Construction of site index equations for Pinus sylvestris L. using permanent plot data in Sweden. For Ecol Manag. 98:125–134. doi:https://doi.org/10.1016/S0378-1127(97)00077-7.
   Web of Science ®Google Scholar
• Fahlvik N, Elfving B, Wikstrom P. 2014. Evaluation of growth models used in the Swedish Forest Planning System Heureka. Silva Fenn. 48. doi:https://doi.org/10.14214/sf.1013.
   Web of Science ®Google Scholar
• Felton A, Lofroth T, Angelstam P, Gustafsson L, Hjalten J, Felton AM, Simonsson P, Dahlberg A, Lindbladh M, Svensson J, et al. 2020. Keeping pace with forestry: Multi-scale conservation in a changing production forest matrix. Ambio. 49:1050–1064. doi:https://doi.org/10.1007/s13280-019-01248-0.
   PubMed Web of Science ®Google Scholar
• Fridman J, Holm S, Nilsson M, Nilsson P, Ringvall AH, Stahl G. 2014. Adapting National Forest Inventories to changing requirements - the case of the Swedish National Forest Inventory at the turn of the 20th century. Silva Fenn. 48. doi:https://doi.org/10.14214/sf.1095.
   Web of Science ®Google Scholar
• Grossiord C, Buckley TN, Cernusak LA, Novick KA, Poulter B, Siegwolf RTW, Sperry JS, McDowell NG. 2020. Plant responses to rising vapor pressure deficit. New Phytol. 226:1550–1566. doi:https://doi.org/10.1111/nph.16485.
   PubMed Web of Science ®Google Scholar
• Henning JG, Burk TE. 2004. Improving growth and yield estimates with a process model derived growth index. Canadian Journal of Forest Research-Revue Canadienne De Recherche Forestiere. 34:1274–1282. doi:https://doi.org/10.1139/X04-021.
   Web of Science ®Google Scholar
• IPCC. 2021. Climate Change 2021: The Physical Science Basis. Cambridge, United Kingdom and New York, USA: Cambridge University Press.
   Google Scholar
• Jackson RB, Canadell J, Ehleringer JR, Mooney HA, Sala OE, Schulze ED. 1996. A global analysis of root distributions for terrestrial biomes. Oecologia. 108:389–411. doi:https://doi.org/10.1007/Bf00333714.
   PubMed Web of Science ®Google Scholar
• Jacob D, Petersen J, Eggert B, Alias A, Christensen OB, Bouwer LM, Braun A, Colette A, Deque M, Georgievski G, et al. 2014. EURO-CORDEX: new high-resolution climate change projections for European impact research. Reg Environ Change. 14:563–578. doi:https://doi.org/10.1007/s10113-013-0499-2.
   Web of Science ®Google Scholar
• Jaramillo F, Prieto C, Lyon SW, Destouni G. 2013. Multimethod assessment of evapotranspiration shifts due to non-irrigated agricultural development in Sweden. J Hydrol. 484:55–62. doi:https://doi.org/10.1016/j.jhydrol.2013.01.010.
   Web of Science ®Google Scholar
• Johnsen K, Samuelson L, Teskey R, McNulty S, Fox T. 2001. Process models as tools in forestry research and management. For Sci. 47:2–8.
   Google Scholar
• Kolari P, Lappalainen HK, Hanninen H, Hari P. 2007. Relationship between temperature and the seasonal course of photosynthesis in Scots pine at northern timberline and in southern boreal zone. Tellus Ser B: Chem. Phys. Meteorol. 59:542–552. doi:https://doi.org/10.1111/j.1600-0889.2007.00262.x.
   Web of Science ®Google Scholar
• Landsberg J, Makela A, Sievanen R, Kukkola M. 2005. Analysis of biomass accumulation and stem size distributions over long periods in managed stands of Pinus sylvestris in Finland using the 3-PG model. Tree Physiol. 25:781–792. doi:https://doi.org/10.1093/treephys/25.7.781.
   PubMed Web of Science ®Google Scholar
• Landsberg J, Sands P. 2011. Physiological ecology of forest production, 1st ed, Terrestrial Ecology Series. San Diego, USA: Elsevier.
   Google Scholar
• Landsberg JJ, Waring RH. 1997. A generalised model of forest productivity using simplified concepts of radiation-use efficiency, carbon balance and partitioning. For Ecol Manag. 95:209–228. doi:https://doi.org/10.1016/S0378-1127(97)00026-1.
   Web of Science ®Google Scholar
• Lee SH. 1998. Modelling growth and yield of Douglas-fir using different interval lengths in the South Island of New Zealand. Doctor of Philosophy in forestry, University of Canterbury.
   Google Scholar
• Martin-Benito D, Pederson N. 2015. Convergence in drought stress, but a divergence of climatic drivers across a latitudinal gradient in a temperate broadleaf forest. J Biogeogr. 42:925–937. doi:https://doi.org/10.1111/jbi.12462.
   Web of Science ®Google Scholar
• Mason EG, Methol R, Cochrane H. 2011. Hybrid mensurational and physiological modelling of growth and yield of Pinus radiata D. Don. using potentially useable radiation sums. Forestry. 84:99–108. doi:https://doi.org/10.1093/forestry/cpq048.
   Web of Science ®Google Scholar
• Mason EG, Rose RW, Rosner LS. 2007. Time vs. light: a potentially useable light sum hybrid model to represent the juvenile growth of Douglas-fir subject to varying levels of competition. Canadian Journal of Forest Research-Revue Canadienne De Recherche Forestiere. 37:795–805. doi:https://doi.org/10.1139/X06-273.
   Web of Science ®Google Scholar
• Meehl GA, Tebaldi C, Nychka D. 2004. Changes in frost days in simulations of twentyfirst century climate. Clim Dyn. 23:495–511. doi:https://doi.org/10.1007/s00382-004-0442-9.
   Web of Science ®Google Scholar
• Mensah AA, Holmstrom E, Petersson H, Nystrom K, Mason EG, Nilsson U. 2021. The millennium shift: Investigating the relationship between environment and growth trends of Norway spruce and Scots pine in northern Europe. For Ecol Manag. 481. doi:https://doi.org/10.1016/j.foreco.2020.118727.
   Web of Science ®Google Scholar
• Monteith JL. 1977. Climate and Efficiency of Crop Production in Britain. Philos Trans R Soc London, Ser. B-Biol Sci. 281:277–294. doi:https://doi.org/10.1098/rstb.1977.0140.
   Web of Science ®Google Scholar
• Nilsson U, Agestam E, Ekö P-M, Elfving B, Fahlvik N, Johansson U, Karlsson K, Lundmark T, Wallentin C. 2010. Thinning of Scots pine and Norway spruce monocultures in Sweden – Effects of different thinning programmes on stand level gross- and net stem volume production. Stud For Suec. 219:1–46.
   Google Scholar
• Peters W, Bastos A, Ciais P, Vermeulen A. 2020. A historical, geographical and ecological perspective on the 2018 European summer drought. Philos Trans R Soc, Ser. B-Biol Sci. 375. doi:https://doi.org/10.1098/rstb.2019.0505.
   Web of Science ®Google Scholar
• Pinjuv G, Mason EG, Watt M. 2006. Quantitative validation and comparison of a range of forest growth model types. For Ecol Manag. 236:37–46. doi:https://doi.org/10.1016/j.foreco.2006.06.025.
   Web of Science ®Google Scholar
• Priestley CHB, Taylor RJ. 1971. On the Assessment of Surface Heat Flux and Evaporation Using Large-Scale Parameters. Mon Weather Rev. 100:81–91.
   Web of Science ®Google Scholar
• Rachid-Casnati C, Mason EG, Woollons RC, Landsberg JJ. 2020. Modelling growth of Pinus taeda and Eucalyptus grandis as a function of light sums modified by air temperature, vapour pressure deficit, and water balance. N Z J For Sci. 50. doi:https://doi.org/10.33494/nzjfs502020x17x.
   Web of Science ®Google Scholar
• R Core Team. 2016. R: A language and environment for statistical computing. Vienna, Austia: R Foundation for Statistical Computing.
   Google Scholar
• Rogers A, Medlyn BE, Dukes JS, Bonan G, von Caemmerer S, Dietze MC, Kattge J, Leakey ADB, Mercado LM, Niinemets U, et al. 2017. A roadmap for improving the representation of photosynthesis in Earth system models. New Phytol. 213:22–42. doi:https://doi.org/10.1111/nph.14283.
   PubMed Web of Science ®Google Scholar
• Ruiz-Pérez G, Vico G. 2020. Effects of Temperature and Water Availability on Northern European Boreal Forests. Front For Global Change. 34. doi:https://doi.org/10.3389/ffgc.2020.00034.
   Google Scholar
• Ruosteenoja K, Markkanen T, Venalainen A, Raisanen P, Peltola H. 2018. Seasonal soil moisture and drought occurrence in Europe in CMIP5 projections for the 21st century. Clim Dyn. 50:1177–1192. doi:https://doi.org/10.1007/s00382-017-3671-4.
   Web of Science ®Google Scholar
• Rytter L, Andreassen K, Bergh J, Eko PM, Gronholm T, Kilpelainen A, Lazdina D, Muiste P, Nord-Larsen T. 2015. Availability of Biomass for Energy Purposes in Nordic and Baltic Countries: Land Areas and Biomass Amounts. Baltic Forestry. 21:375–390.
   Web of Science ®Google Scholar
• Rytter L, Ingerslev M, Kilpelainen A, Torssonen P, Lazdina D, Lof M, Madsen P, Muiste P, Stener LG. 2016. Increased forest biomass production in the Nordic and Baltic countries - a review on current and future opportunities. Silva Fenn. 50. doi:https://doi.org/10.14214/sf.1660.
   Web of Science ®Google Scholar
• Sand P. 2004. Adaptation of 3-PG to novel species: guidelines for data collection and parameter assignment (Technical Report 141). Cooperative Research Centre for Sustainable Production Forestry.
   Google Scholar
• Schumacher FX. 1939. A new growth curve and its application to timber yield studies. J For. 37:819–820.
   Google Scholar
• SLU. 2021. Forest statistics 2021. Umeå, Sweden: Department of Forest Resource Management.
   Google Scholar
• SMHI. 2020a. Climate scenarios [Online]. Available: https://www.smhi.se/en/climate/future-climate/climate-scenarios/ [Accessed June 16 2020].
   Google Scholar
• SMHI. 2020b. UERRA - Uncertainties in Ensembles of Regional ReAnalyses [Online]. Available: https://www.smhi.se/en/research/research-departments/meteorology/uerra-uncertainties-in-ensembles-of-regional-reanalyses-1.107636 [Accessed June 16 2020].
   Google Scholar
• Snowdon P. 2001. Short-term predictions of growth of Pinus radiata with models incorporating indices of annual climatic variation. For Ecol Manag. 152:1–11. doi:https://doi.org/10.1016/S0378-1127(00)00453-9.
   Web of Science ®Google Scholar
• Subramanian N, Nilsson U, Mossberg M, Bergh J. 2019. Impacts of climate change, weather extremes and alternative strategies in managed forests. Ecoscience. 26:53–70. doi:https://doi.org/10.1080/11956860.2018.1515597.
   Web of Science ®Google Scholar
• Taylor AR, Chen HYH, VanDamme L. 2009. A Review of Forest Succession Models and Their Suitability for Forest Management Planning. For Sci. 55:23–36. doi:https://doi.org/10.1093/forestscience/55.1.23.
   Google Scholar
• Toreti A, Belward A, Perez-Dominguez I, Naumann G, Luterbacher J, Cronie O, Seguini L, Manfron G, Lopez-Lozano R, Baruth B, et al. 2019. The Exceptional 2018 European Water Seesaw Calls for Action on Adaptation. Earths Future. 7:652–663. doi:https://doi.org/10.1029/2019ef001170.
   Web of Science ®Google Scholar
• Vico G, Way DA, Hurry V, Manzoni S. 2019. Can leaf net photosynthesis acclimate to rising and more variable temperatures? Plant Cell Environ. 42:1913–1928. doi:https://doi.org/10.1111/pce.13525.
   PubMed Web of Science ®Google Scholar
• Waring R, Landsberg J, Linder S. 2016. Tamm Review: Insights gained from light use and leaf growth efficiency indices. For Ecol Manag. 379:232–242. doi:https://doi.org/10.1016/j.foreco.2016.08.023.
   Web of Science ®Google Scholar
• Way DA, Yamori W. 2014. Thermal acclimation of photosynthesis: on the importance of adjusting our definitions and accounting for thermal acclimation of respiration. Photosynth Res. 119:89–100. doi:https://doi.org/10.1007/s11120-013-9873-7.
   PubMed Web of Science ®Google Scholar
• Weiskittel AR, Hann DW, Kershaw JA, Vanclay JK. 2011. Forest Growth and Yield Modeling. John Wiley & Sons. doi:https://doi.org/10.1002/9781119998518.
   Google Scholar
• Weiskittel AR, Maguire DA, Monserud RA, Johnson GP. 2010. A hybrid model for intensively managed Douglas-fir plantations in the Pacific Northwest, USA. Eur J For Res. 129:325–338. doi:https://doi.org/10.1007/s10342-009-0339-6.
   Web of Science ®Google Scholar
• Zang, C., Pretzsch, H. & Rothe, A. 2012. Size-dependent responses to summer drought in Scots pine, Norway spruce and common oak. Trees, 26, 557-569. doi:https://doi.org/10.1007/s00468-011-0617-z
   Web of Science ®Google Scholar