Open Access
Issue
Published:
03 April 2018
Species-specific, pan-European diameter increment models based on data of 2.3 million trees
)
Download citation
519
Views
11
Downloads
25
Crossref
N/A
WoS
29
Scopus
0
CSCD
Over the last decades, many forest simulators have been developed for the forests of individual European countries. The underlying growth models are usually based on national datasets of varying size, obtained from National Forest Inventories or from long-term research plots. Many of these models include country- and location-specific predictors, such as site quality indices that may aggregate climate, soil properties and topography effects. Consequently, it is not sensible to compare such models among countries, and it is often impossible to apply models outside the region or country they were developed for. However, there is a clear need for more generically applicable but still locally accurate and climate sensitive simulators at the European scale, which requires the development of models that are applicable across the European continent. The purpose of this study is to develop tree diameter increment models that are applicable at the European scale, but still locally accurate. We compiled and used a dataset of diameter increment observations of over 2.3 million trees from 10 National Forest Inventories in Europe and a set of 99 potential explanatory variables covering forest structure, weather, climate, soil and nutrient deposition.
Diameter increment models are presented for 20 species/species groups. Selection of explanatory variables was done using a combination of forward and backward selection methods. The explained variance ranged from 10% to 53% depending on the species. Variables related to forest structure (basal area of the stand and relative size of the tree) contributed most to the explained variance, but environmental variables were important to account for spatial patterns. The type of environmental variables included differed greatly among species.
The presented diameter increment models are the first of their kind that are applicable at the European scale. This is an important step towards the development of a new generation of forest development simulators that can be applied at the European scale, but that are sensitive to variations in growing conditions and applicable to a wider range of management systems than before. This allows European scale but detailed analyses concerning topics like CO2 sequestration, wood mobilisation, long term impact of management, etc.
menu
Species-specific, pan-European diameter increment models based on data of 2.3 million trees
)
Abstract
Over the last decades, many forest simulators have been developed for the forests of individual European countries. The underlying growth models are usually based on national datasets of varying size, obtained from National Forest Inventories or from long-term research plots. Many of these models include country- and location-specific predictors, such as site quality indices that may aggregate climate, soil properties and topography effects. Consequently, it is not sensible to compare such models among countries, and it is often impossible to apply models outside the region or country they were developed for. However, there is a clear need for more generically applicable but still locally accurate and climate sensitive simulators at the European scale, which requires the development of models that are applicable across the European continent. The purpose of this study is to develop tree diameter increment models that are applicable at the European scale, but still locally accurate. We compiled and used a dataset of diameter increment observations of over 2.3 million trees from 10 National Forest Inventories in Europe and a set of 99 potential explanatory variables covering forest structure, weather, climate, soil and nutrient deposition.
Diameter increment models are presented for 20 species/species groups. Selection of explanatory variables was done using a combination of forward and backward selection methods. The explained variance ranged from 10% to 53% depending on the species. Variables related to forest structure (basal area of the stand and relative size of the tree) contributed most to the explained variance, but environmental variables were important to account for spatial patterns. The type of environmental variables included differed greatly among species.
The presented diameter increment models are the first of their kind that are applicable at the European scale. This is an important step towards the development of a new generation of forest development simulators that can be applied at the European scale, but that are sensitive to variations in growing conditions and applicable to a wider range of management systems than before. This allows European scale but detailed analyses concerning topics like CO2 sequestration, wood mobilisation, long term impact of management, etc.
References(73)
Akaike H (1974) A new look at the statistical model identification. IEEE Trans Autom Control AC-19:716–723 https://doi.org/10.1109FTAC.1974.1100705
Andreassen K, Tomter SM (2003) Basal area growth models for individual trees of Norway spruce, Scots pine, birch and other broadleaves in Norway. Forest Ecol Manag 180:11–24. https://doi.org/10.1016/S0378-1127(02)00560-1
Barreiro S, Schelhaas MJ, Kändler G, Antón-Fernández C, Colin A, Bontemps J-D, Alberdi I, Cóndes S, Dumitru M, Ferezliev A, Fisher C, Gasparini P, Gschwantner T, Kindermann G, Kjartansson B, Kovácsevics P, Kucera M, Lundström A, Marin G, Mozgeris G, Nord-Larsen T, Packalen T, Redmond J, Sacchelli S, Sims A, Snorrason A, Stoyanov N, Thürig E, Wikberg P-E (2016) Overview of methods and tools for evaluating future woody biomass availability in European countries. Ann Forest Sci 73(4):823–837. https://doi.org/10.1007/s13595-016-0564-3
Bitterlich W (1952) Die Winkelzählprobe: Ein optisches Meßverfahren zur raschen Aufnahme besonders gearteter Probeflächen für die Bestimmung der Kreisflächen pro Hektar an stehenden Waldbeständen. Forstwissenschaftliches Centralblatt 71(7):215–225 https://doi.org/10.1007FBF01821439
Charru M, Seynave I, Hervé J-C, Bertrand R, Bontemps J-D (2017) Recent growth changes in western European forests are driven by climate warming and structured across tree species climatic habitats. Ann Forest Sci 74(2):33. https://doi.org/10.1007/s13595-017-0626-1
Cienciala E, Russ R, Šantrůčková H, Altman J, Kopáček J, Hůnová I, Štěpánek P, Oulehle F, Tumajer J, Ståhl G (2016) Discerning environmental factors affecting current tree growth in Central Europe. Sci Total Environ 573:541–554. https://doi.org/10.1016/j.scitotenv.2016.08.115
Duncker P, Barreiro SM, Hengeveld GM, Lind T, Mason WL, Ambrozy S, Spiecker H (2012) Classification of forest management approaches: a new methodological framework and its applicability to European forestry. Ecol Soc 17(4):51. https://doi.org/10.5751/ES-05262-170451
Dunger K, Petersson H, Barreiro S, Cienciala E, Colin A, Hylen G, Kusar G, Oehmichen K, Tomppo E, Tuomainen T, Ståhl G (2012) Harmonizing greenhouse-gas reporting from European forests – case examples and implications for EU level reporting. For Sci 58:248–256
Hasenauer H, Monserud RA (1997) Biased predictions for tree height increment models developed from smoothed 'data'. Ecol Model 98:13–22 https://doi.org/10.1016FS0304-3800896901933-3
Hector A, Bagchi R (2007) Biodiversity and ecosystem multifunctionality. Nature 448:188–190
Hengeveld GM, Nabuurs GJ, Didion M, van den Wyngaert I, Clerkx APPM, Schelhaas MJ (2012) A forest management map of European forests. Ecol Soc 17(4):53. https://doi.org/10.5751/ES-05149-170453
Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25:1965–1978 https://doi.org/10.1002Fjoc.1276
Hökkä H, Alenius V, Penttilä T (1997) Individual-tree basal area growth models for Scots pine, pubescent birch and Norway spruce on drained peatlands in Finland. Silva Fenn 31:161–178 https://doi.org/10.14214Fsf.a8517
Köhl M, Traub B, Päivinen R (2000) Harmonisation and standardisation in multi-national environmental statistics – mission impossible? Environm Monit Assess 63:361–380 https://doi.org/10.1023FA%3A1006257630216
Korf V (1939) Prispevek k matematicke definici vzrus-toveho zakona hmot lesnich porostu. Lesnicka Pracr 18:339–379
Kramer K, Degen B, Buschbom J, Hickler T, Thuiller W, Sykes MT, de Winter W (2010) Modelling exploration of the future of European beech (Fagus sylvatica L.) under climate change—range, abundance, genetic diversity and adaptive response. For Ecol Man 259:2213–2222
Laubhann D, Sterba H, Reinds GJ, De Vries W (2009) The impact of atmospheric deposition and climate on forest growth in European monitoring plots: an individual tree growth model. Forest Ecol Manag 258:1751–1761. https://doi.org/10.1016/j.foreco.2008.09.050
Lindner M, Fitzgerald JB, Zimmermann NE, Reyer C, Delzon S, van der Maaten E, Schelhaas MJ, Lasch P, Eggers J, van der Maaten-Theunissen M, Suckow F, Psomas A, Poulter B, Hanewinkel M (2014) Climate change and European forests: what do we know, what are the uncertainties, and what are the implications for forest management? J Environ Manag 146:69–83. https://doi.org/10.1016/j.jenvman.2014.07.030
MacFarlane DW, Green EJ, Brunner A, Burkhart HE (2002) Predicting survival and growth rates for individual loblolly pine trees from light capture estimates. Can J For Res 32:1970–1983 https://doi.org/10.1139Fx02-125
McRoberts RE, Hahn JT, Hefty GJ, Van Cleve JR (1994) Variation in forest inventory field measurements. Can J For Res 24:1766–1770. https://doi.org/10.1139/x94-228
McRoberts RE, Tomppo E, Schadauer K, Vidal C, Ståhl G, Chirici G, Lanz A, Cienciala E, Winter S, Brad Smith W (2009) Harmonizing National Forest Inventories. J Forest 107:179–187
Mehtätalo L (2005) Height-diameter models for Scots pine and birch in Finland. Silva Fenn 39(1):55–66 https://doi.org/10.14214Fsf.395
Monserud RA, Sterba H (1996) A basal area increment model for individual trees growing in even- and uneven-aged forest stands in Austria. Forest Ecol Manag 80:57–80 https://doi.org/10.1016F0378-1127895903638-5
Morin X, Fahse L, Scherer-Lorenzen M, Bugmann H (2011) Tree species richness promotes productivity in temperate forests through strong complementarity between niches. Ecol Lett 14(12):1211–1219 https://doi.org/10.1111Fj.1461-0248.2011.01691.x
Nabuurs GJ, van Brusselen J, Pussinen A, Schelhaas MJ (2006) Future harvesting pressure on European forests. Eur J For Res 126:391–400 https://doi.org/10.1007Fs10342-006-0158-y
Panagos P, Van Liedekerke M, Jones A, Montanarella L (2012) European soil data Centre: response to European policy support and public data requirements. Land Use Policy 29:329–338. https://doi.org/10.1016/j.landusepol.2011.07.003 https://doi.org/10.1016Fj.landusepol.2011.07.003
Peng C (2000) Growth and yield models for uneven-aged stands: past, present and future. Forest Ecol Manag 132:259–279 https://doi.org/10.1016FS0378-1127899900229-7
Pukkala T (1989) Predicting diameter growth in an even-aged Scots pine stand with a spatial and a non-spatial model. Silva Fenn 23:101–116
Quicke HE, Meldahl RS, Kush JS (1994) Basal area growth of individual trees: a model derived from a regional longleaf pine growth study. For Sci 40:528–542
Richards FJ (1959) A flexible growth function for empirical use. J Exp Bot 10:290–300 https://doi.org/10.1093FjxbF10.2.290
Ritchie MW, Hann DW (1986) Development of a tree height growth model for Douglas-fir. Forest Ecol Manag 15(2):135–145. https://doi.org/10.1016/0378-1127(86)90142-8
Schröder J, Soalleiro RR, Alonso GV (2002) An age-independent basal area increment model for maritime pine trees in Northwestern Spain. Forest Ecol Manag 157:55–64 https://doi.org/10.1016FS0378-1127800900657-5
Seidl R, Baier P, Rammer W, Schopf A, Lexer MJ (2007) Modelling tree mortality by bark beetle infestation in Norway spruce forests. Ecol Model 206:383–399 https://doi.org/10.1016Fj.ecolmodel.2007.04.002
Stage AR (1963) A mathematical approach to polymorphic site index curves for grand fir. For Sci 9:167–180
Trabucco A, Zomer RJ, Bossio DA, Van Straaten O, Verchot LV (2008) Climate change mitigation through afforestation/reforestation: a global analysis of hydrologic impacts with four case studies. Agric Ecosyst Environ 126:81–97 https://doi.org/10.1016Fj.agee.2008.01.015
Verkerk PJ, Antilla P, Eggers J, Lindner M, Asikainen A (2011) The realisable potential supply of woody biomass from forests in the European Union. Forest Ecol Manag 261:2007–2015 https://doi.org/10.1016Fj.foreco.2011.02.027
Wykoff WR (1990) A basal area increment model for individual conifers in the northern rocky mountains. For Sci 36:1077–1104
Zhang Y, Chen HYH, Reich PB (2012) Forest productivity increases with evenness, species richness and trait variation: a global meta-analysis. J Ecol 100(3):742–749 https://doi.org/10.1111Fj.1365-2745.2011.01944.x
Zhao D, Borders B, Wilson M, Rathbun SL (2006) Modeling neighborhood effects on the growth and survival of individual trees in a natural temperate species-rich forest. Ecol Model 196:90–102 https://doi.org/10.1016Fj.ecolmodel.2006.02.002
Zomer RJ, Trabucco A, Bossio DA, Verchot LV (2008) Climate change mitigation: a spatial analysis of global land suitability for clean development mechanism afforestation and reforestation. Agric Ecosyst Environ 126:67–80
Publication history
Copyright
Acknowledgements
We thank all the national forest inventories that have made their data available, in particular the French IGN, the German Bundeswald Inventur, IPLA SpA for the data in Piemonte and Regione Autonoma Valle d'Aosta for the data in Piemonte. We thank all the NFI field crews for their hard work that made this study possible. We thank Bert van der Werf for his contributions to the development of the procedures for data preparation and statistical analysis, and Raymond van der Wijngaart for his help with the weather data. We thank JRC/EU AGRI4CAST for making the weather data available. We thank the EU for funding the Cost Actions PROFOUND FP1304 and USEWOODFP1001 through which some of the data contacts were established.
Rights and permissions
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
FEEDBACK 
TOP