Assessing site productivity based on national forest inventory data and its dependence on site conditions for spruce dominated forests in Germany

Susanne Brandl, Wolfgang Falk, Thomas Rötzer, Hans Pretzsch

Abstract


Aim of study: (i) To estimate site productivity based on German national forest inventory (NFI) data using above-ground wood biomass increment (ΔB) of the stand and (ii) to develop a model that explains site productivity quantified by ΔB in dependence on climate and soil conditions as well as stand characteristics for Norway spruce (Picea abies (L.) Karst.).

Area of study: Germany, which ranges from the North Sea to the Bavarian Alps in the south encompassing lowlands in the north, uplands in central Germany and low mountain ranges mainly in southern Germany.

Material and methods: Biomass increment of the stand between the 2nd and 3rd NFI was calculated as measure for site productivity. Generalized additive models were fitted to explain biomass increment in dependence on stand age, stand density and environmental variables.

Main results: Great part of the variation in biomass increment was due to differences in stand age and stand density. Mean annual temperature and summer precipitation, temperature seasonality, base saturation, C/N ratio and soil texture explained further variation. External validation of the model using data from experimental plots showed good model performance.

Research highlights: The study outlines both the potential as well as the restrictions in using biomass increment as a measure for site productivity and as response variable in statistical site-productivity models: biomass increment of the stand is a comprehensive measure of site potential as it incorporates both height and basal area increment as well as stem number. However, it entails the difficulty of how to deal with the influence of management on stand density.

Keywords: Site index; site potential; biomass increment; statistical model; climate.


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References


Albert M, Schmidt M, 2010. Climate-sensitive modelling of site-productivity relationships for Norway spruce (Picea abies (L.) Karst.) and common beech (Fagus sylvatica L.). Forest Ecol Manag 259 (4): 739-749. https://doi.org/10.1016/j.foreco.2009.04.039

Assmann E, 1961. Waldertragskunde. Organische Produktion, Struktur, Zuwachs und Ertrag von Waldbeständen. BLV Verlagsgesellschaft, München, Bonn, Wien. 1-490.

Barnes BV, Zak DR, Denton SR, Spurr SH, 1998. Forest Ecology, 4th edn. John Wiley & Sons, Inc., 1-774.

Bontemps J-D, Bouriaud O, 2014. Predictive approaches to forest site productivity. Recent trends, challenges and future perspectives. Forestry 87 (1): 109-128. https://doi.org/10.1093/forestry/cpt034

Brandl S, Falk W, Klemmt H-J, Stricker G, Bender A, Rötzer T, Pretzsch H, 2014. Possibilities and Limitations of Spatially Explicit Site Index Modelling for Spruce Based on National Forest Inventory Data and Digital Maps of Soil and Climate in Bavaria (SE Germany). Forests 5 (11): 2626-2646. https://doi.org/10.3390/f5112626

Brandl S, Mette T, Falk W, Vallet P, Rötzer T, Pretzsch H, 2018. Static site indices from different national forest inventories. Harmonization and prediction from site conditions. Ann For Sci 75 (2): 739. https://doi.org/10.1007/s13595-018-0737-3

BMELV, 2011. Aufnahmeanweisung für die dritte Bundeswaldinventur (BWI³) (2011-2012). Institut für Waldökologie und Waldinventuren im Johann Heinrich von Thünen-Institut. Bonn.

Böhner J, Röder A, Dietrich H, Kawohl T , Wehberg J, Wolf T, Kändler G, Mette T, in rev. 2018 (Ann For Sci). Temporal and spatial high-resolution climate data from 1961-2100 for the Germany National Forest Inventory (NFI).

Charru M, Seynave I, Morneau F, Bontemps J-D, 2010. Recent changes in forest productivity: An analysis of national forest inventory data for common beech (Fagus sylvatica L.) in north-eastern France. Forest Ecol Manag 260 (5): 864-874. https://doi.org/10.1016/j.foreco.2010.06.005

Charru M, Seynave I, Hervé J-C, Bontemps J-D, 2014. Spatial patterns of historical growth changes in Norway spruce across western European mountains and the key effect of climate warming. Trees 28 (1): 205-221. https://doi.org/10.1007/s00468-013-0943-4

Chave J, Condit R, Lao S, Caspersen JP, Foster RB, Hubbell SP, 2003. Spatial and temporal variation of biomass in a tropical forest: results from a large census plot in Panama. J Ecol 91 (2): 240-252. https://doi.org/10.1046/j.1365-2745.2003.00757.x

Condés S, García-Robredo F, 2012. An empirical mixed model to quantify climate influence on the growth of Pinus halepensis Mill. stands in South-Eastern Spain. Forest Ecol Manag 284 (0): 59-68. https://doi.org/10.1016/j.foreco.2012.07.030

Dahm S, 2006. Auswertungsalgorithmen für die zweite Bundeswaldinventur. Bundesforschungsanstalt für Forst- und Holzwirtschaft Hamburg, Institut für Waldökologie und Waldinventuren. Eberswalde.

Dolos K, Bauer A, Albrecht S, 2015. Site suitability for tree species: Is there a positive relation between a tree species' occurrence and its growth? Eur J Forest Res 134 (4): 609-621. https://doi.org/10.1007/s10342-015-0876-0

Dormann CF, Elith J, Bacher S, Buchmann C, Carl G, Carré G, García Marquéz JR, Gruber B, Lafourcade B, Leitão PJ, et al., 2013. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography 36 (1): 27-46. https://doi.org/10.1111/j.1600-0587.2012.07348.x

Gustafson E, Lietz S, Wright J, 2003. Predicting the Spatial Distribution of Aspen Growth Potential in the Upper Great Lakes Region. Forest Sci 49 (4): 499-508.

Hartigan JA, Wong MA, 1979. A K-means clustering algorithm. Appl Stat 28, 100-108. https://doi.org/10.2307/2346830

Jenkins JC, Birdsey RA, Pan Y, 2001. Biomass and NPP estimation for the mid-Atlantic Region (USA) using plot-level forest inventory data. Ecol Appl 11 (4): 1174-1193. https://doi.org/10.1890/1051-0761(2001)011[1174:BANEFT]2.0.CO;2

Kahle HP, 2015. Kritische Überprüfung und Weiterentwicklung des Konzepts der forstlichen Standortproduktivität. Tagungsbericht, Kammerforst/Thüringen, 18.-20. Mai 2015. pp: 76-86.

Kauppi PE, Posch M, Pirinen P, 2014. Large Impacts of Climatic Warming on Growth of Boreal Forests since 1960. PLOS ONE 9 (11): 1-6. https://doi.org/10.1371/journal.pone.0111340

Keith H, Mackey BG, Lindenmayer DB, 2009. Re-evaluation of forest biomass carbon stocks and lessons from the world's most carbon-dense forests. P Natl Acad Sci USA 106 (28): 11635-11640. https://doi.org/10.1073/pnas.0901970106

Mayer H, 1992. Waldbau auf soziologisch-ökologischer Grundlage, 4th edn. Gustav Fischer Verlag, Stuttgart Jena New York, p 73.

Mehtätalo L, 2004. A longitudinal height-diameter model for Norway spruce in Finland. Can J Forest Res 34 (1): 131-140. https://doi.org/10.1139/x03-207

Mellert KH, Ewald J, Hornstein D, Dorado-Liñán I, Jantsch M, Taeger S, Zang C, Menzel A, Kölling C, 2016. Climatic marginality: a new metric for the susceptibility of tree species to warming exemplified by Fagus sylvatica (L.) and Ellenberg's quotient. Eur J Forest Res 135 (1): 137-152. https://doi.org/10.1007/s10342-015-0924-9

Mellert KH, Ewald J, 2014. Nutrient limitation and site-related growth potential of Norway spruce (Picea abies [L.] Karst) in the Bavarian Alps. Eur J Forest Res 133: 433-451. https://doi.org/10.1007/s10342-013-0775-1

Nothdurft A, Wolf T, Ringeler A, Böhner J, Saborowski J, 2012. Spatio-temporal prediction of site index based on forest inventories and climate change scenarios. Forest Ecol Manag 279 (0): 97-111. https://doi.org/10.1016/j.foreco.2012.05.018

Pretzsch H, 2002. Grundlagen der Waldwachstumsforschung. Parey Buchverlag, Berlin.

Pretzsch H, 2006. Von der Standflächeneffizienz der Bäume zur Dichte-Zuwachs-Beziehung des Bestandes. Beitrag zur Integration von Baum- und Bestandesebene. Allg Forst- u J-Ztg 177 (10/11): 188-199.

Pretzsch H, 2009. Forest Dynamics, Growth and Yield. Springer, Berlin Heidelberg. 664 pp. https://doi.org/10.1007/978-3-540-88307-4

Pretzsch H, Biber P, Schütze G, Uhl E, Rötzer T, 2014a. Forest stand growth dynamics in Central Europe have accelerated since 1870. Nat Commun 5: 4967. https://doi.org/10.1038/ncomms5967

Pretzsch H, Block J, Dieler J, Gauer J, Göttlein A, Moshammer R, Schuck J, Weis W, Wunn U, 2014b. Nährstoffentzüge durch die Holz- und Biomassenutzung in Wäldern. Schätzfunktionen für Biomasse und Nährelemente und ihre Anwendung in Szenariorechnungen. Allg Forst- u J-Ztg 185 (11/12): 261-285.

R Core Team, 2016. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.

Rehfuess KE, 1990. Waldböden. Parey, Hamburg.

Reineke LH, 1933. Perfecting a stand density index for even-aged forests. Journal Agric Res 46: 627-638.

Riedel T, Hennig P, Kroiher F, Polley H, Schmitz F, Schwitzgebel F, 2017. Die dritte Bundeswaldinventur (BWI 2012). Inventur- und Auswertemethoden. Johann Heinrich von Thünen-Institut.

Scrucca L, Fop M, Murphy TB, Raftery AE, 2016. mclust 5: clustering, classification and density estimation using Gaussian finite mixture models The R Journal 8/1: 205-233. https://doi.org/10.32614/RJ-2016-021

Seynave I, Gégout JC, Hervé JC, Dhôte JF, Drapier J, Bruno É, Dumé G, 2005. Picea abies site index prediction by environmental factors and understorey vegetation: a two-scale approach based on survey databases. Can J Forest Res 35: 1669-1678. https://doi.org/10.1139/x05-088

Skovsgaard JP, Vanclay JK, 2008. Forest site productivity: a review of the evolution of dendrometric concepts for even-aged stands. Forestry 81 (1): 13-31. https://doi.org/10.1093/forestry/cpm041

Stegen JC, Swenson NG, Enquist BJ, White EP, Phillips OL, Jorgensen PM, Weiser MD, Monteagudo A, Núñez Vargas P, 2011. Variation in above-ground forest biomass across broad climatic gradients. Global Ecol Biogeogr 20: 744-754. https://doi.org/10.1111/j.1466-8238.2010.00645.x

Vallet P, Perot T, 2016. Tree diversity effect on dominant height in temperate forest. Forest Ecol Manag 381: 106-114. https://doi.org/10.1016/j.foreco.2016.09.024

Von Wilpert K, Puhlmann H, Zirlewagen D, 2017. Regionalisierung - wie der Computer den Boden vorhersagt. AFZ - Der Wald:17-23.

Wang Q, Ni J, Tenhunen J, 2005. Application of a geographically-weighted regression analysis to estimate net primary production of Chinese forest ecosystems. Global Ecol Biogeogr 14 (4): 379-393. https://doi.org/10.1111/j.1466-822X.2005.00153.x

Watt MS, Palmer DJ, Kimberley MO, Höck BK, Payn TW, Lowe DJ, 2010. Development of models to predict Pinus radiata productivity throughout New Zealand. Can J Forest Res 40 (3): 488-499. https://doi.org/10.1139/X09-207

Wenk G, Antanaitis V, Smelko S, 1990. Waldertragslehre. Deutscher Landwirtschaftsverlag, Berlin. 1-448.

Wood S, 2011. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J R Stat Soc 73 (1): 3-36. https://doi.org/10.1111/j.1467-9868.2010.00749.x

Zeide B, 2001. Thinning and Growth: A full turnaround. J Forest 99: 20-25.

Zeide B, 2005. How to measure stand density. Trees 19 (1): 1-14. https://doi.org/10.1007/s00468-004-0343-x

Zell J, 2008. Methoden für die Ermittlung, Modellierung und Prognose der Kohlenstoffspeicherung in Wäldern auf Grundlage permanenter Großrauminventuren. Albert-Ludwigs-Universität, Freiburg im Breisgau.




DOI: 10.5424/fs/2019282-14423

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