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)

Abstract

Combining national forest inventory (NFI) data with digital site maps of high resolution enables spatially explicit predictions of site productivity. The aim of this study is to explore the possibilities and limitations of this database to analyze the environmental dependency of height-growth of Norway spruce and to predict site index (SI) on a scale that is relevant for local forest management. The study region is the German federal state of Bavaria. The exploratory methods comprise significance tests and hypervolume-analysis. SI is modeled with a Generalized Additive Model (GAM). In a second step the residuals are modeled using Boosted Regression Trees (BRT). The interaction between temperature regime and water supply strongly determined height growth. At sites with very similar temperature regime and water supply, greater heights were reached if the depth gradient of base saturation was favorable. Statistical model criteria (Double Penalty Selection, AIC) preferred composite variables for water supply OPEN ACCESSForests 2014, 5 2627 and the supply of basic cations. The ability to predict SI on a local scale was limited due to the difficulty to integrate soil variables into the model.

Static site indices from different national forest inventories: harmonization and prediction from site conditions

“…However, changes in silvicultural prescriptions like earlier rotation of fast growing stands can introduce a bias into cross-sectional data, but on the other hand, longitudinal data integrate changing site conditions which surely affect height growth. Evidence for forests in Central Europe suggests accelerated growth over the last decades (Pretzsch et al 2014;Charru et al 2014;Spiecker 1999). A promising solution to the dilemma is the interpretation of longitudinal data with dynamic site conditions (Yue et al 2016).…”

“…Any model that relates a site index based on yield tables to recent site condition data would be afflicted with an age trend: on the same site, young stands would be assigned a higher yield class and thus a better site quality than old stands. The realization that site conditions are not constant and that non-table conforming growth is rather the rule than the exception, questions how reliably a site can be classified by yield tables (Skovsgaard and Vanclay 2013; emphasis on enhanced growth due to climate change: Spiecker 1999;Pretzsch et al 2014).…”

Far from the madding crowd: Tolerance toward human disturbance shapes distribution and connectivity patterns of closely related Martes spp.

“…Briefly, hypervolumes are computed across kernel density estimation (KDE), importance‐sampling Monte Carlo integration and range‐testing techniques, overcoming computational inefficiencies that limited previous methods to low‐dimensional systems (for a detailed description see Blonder et al, , Blonder, ). Multivariate KDE has been then applied in several fields of research, that is, to investigate the usefulness of forest inventories data (Brandl et al, ), global invasion patterns ( Rhinella marina ; Tingley, Vallinoto, Sequeira, & Kearney, ), processes causing the latitudinal gradient in species richness (Lamanna et al, ), nest‐site niches ( Ammodramus spp. ; Kern, ), niche diversity (European freshwater species; Iversen, Jacobsen, & Sand‐Jensen, ), genetic differentiation patterns ( Malayemys spp.…”

Tree Species Site Suitability as a Combination of Occurrence Probability and Growth and Derivation of Priority Regions for Climate Change Adaptation

“…At the first step, the decision to calculate SDMs and growth models instead of using tree abundance (number of stems, stem density), site index [14] or other variables which could be calculated based on NFI data, presumably influenced the results. Additionally, regardless of the response variables used, the definition of the model, i.e., how to consider repeated measurements and the nested sampling design of the German NFI, needed careful attention.…”

“…Growth is one of the processes which determine species distribution. Growth parameters are also used to estimate species' ecological demands and responses to climate change [9][10][11][12][13][14]. When competition is considered, tree growth can be assumed to be less influenced by human management than distribution, which is an advantage for determining site suitability.…”