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National forest inventories (NFI) have a long history providing data to obtain nationally representative and accurate estimates of growing stock. Today, in most NFIs additional data are collected to provide information on a range of forest ecosystem functions such as biodiversity, habitat, nutrient and carbon dynamics. An important driver of nutrient and C cycling is decomposing biomass produced by forest vegetation. Several studies have demonstrated that understory vegetation, particularly annual plant litter of the herb layer can contribute significantly to nutrient and C cycling in forests. A methodology to obtain comprehensive, consistent and nationally representative estimates of herb layer biomass on NFI plots could provide added value to NFIs by complementing the existing strong basis of biomass estimates of the tree and tall shrub layer. The study was based on data from the Swiss NFI since it covers a large environmental gradient, which extends its applicability to other NFIs.
Based on data from 405 measurements in nine forest strata, a parsimonious model formulation was identified to predict total and non-ligneous herb layer biomass. Besides herb layer cover, elevation was the main statistically significant explanatory variable for biomass. The regression models accurately predicted biomass based on absolute percentage cover (for total biomass: R2=0.65, p=0; for non-ligneous biomass: R2=0.76; p=0) as well as on cover classes (R2=0.83; p=0; and R2=0.79, p=0), which are typically used in NFIs. The good performance was supported by the verification with data from repeated samples. For the 2nd, 3rd, and 4th Swiss NFI estimates of non-ligneous above-ground herb layer biomass 586.6±7.7, 575.2±7.6, and 586.7±7.9 kg·ha-1, respectively.
The study presents a methodology to obtain herb layer biomass estimates based on a harmonized and standardized attribute available in many NFIs. The result of this study was a parsimonious model requiring only elevation data of sample plots in addition to NFI cover estimates to provide unbiased estimates at the national scale. These qualities are particularly important as they ensure accurate, consistent, and comparable results.
National forest inventories (NFI) have a long history providing data to obtain nationally representative and accurate estimates of growing stock. Today, in most NFIs additional data are collected to provide information on a range of forest ecosystem functions such as biodiversity, habitat, nutrient and carbon dynamics. An important driver of nutrient and C cycling is decomposing biomass produced by forest vegetation. Several studies have demonstrated that understory vegetation, particularly annual plant litter of the herb layer can contribute significantly to nutrient and C cycling in forests. A methodology to obtain comprehensive, consistent and nationally representative estimates of herb layer biomass on NFI plots could provide added value to NFIs by complementing the existing strong basis of biomass estimates of the tree and tall shrub layer. The study was based on data from the Swiss NFI since it covers a large environmental gradient, which extends its applicability to other NFIs.
Based on data from 405 measurements in nine forest strata, a parsimonious model formulation was identified to predict total and non-ligneous herb layer biomass. Besides herb layer cover, elevation was the main statistically significant explanatory variable for biomass. The regression models accurately predicted biomass based on absolute percentage cover (for total biomass: R2=0.65, p=0; for non-ligneous biomass: R2=0.76; p=0) as well as on cover classes (R2=0.83; p=0; and R2=0.79, p=0), which are typically used in NFIs. The good performance was supported by the verification with data from repeated samples. For the 2nd, 3rd, and 4th Swiss NFI estimates of non-ligneous above-ground herb layer biomass 586.6±7.7, 575.2±7.6, and 586.7±7.9 kg·ha-1, respectively.
The study presents a methodology to obtain herb layer biomass estimates based on a harmonized and standardized attribute available in many NFIs. The result of this study was a parsimonious model requiring only elevation data of sample plots in addition to NFI cover estimates to provide unbiased estimates at the national scale. These qualities are particularly important as they ensure accurate, consistent, and comparable results.
Alberdi I, Condés S, Martínez-Millán J (2010) Review of monitoring and assessing ground vegetation biodiversity in national forest inventories. Environ Monit Assess 164:649-676. https://doi.org/10.1007/s10661-009-0919-4
Alberdi I, Condés S, Mcroberts RE, Winter S (2018) Mean species cover: a harmonized indicator of shrub cover for forest inventories. Eur J Forest Res 137:265-278. https://doi.org/10.1007/s10342-018-1110-7
Bates D, Maechler M, Bolker B, Walker S (2015) Fitting linear mixed-effects models using lme4. J Stat Software 67:1-48. https://doi.org/10.18637/jss.v067.i01
Clifford D, Cressie N, England JR, Roxburgh SH, Paul KI (2013) Correction factors for unbiased, efficient estimation and prediction of biomass from log-log allometric models. Forest Ecol Manag 310:375-381. https://doi.org/10.1016/j.foreco.2013.08.041
Davis GE (1993) Design elements of monitoring programs: the necessary ingredients for success. Environ Monit Assess 26:99-105. https://doi.org/10.1007/bf00547489
de Wit HA, Palosuo T, Hylen G, Liski J (2006) A carbon budget of forest biomass and soils in Southeast Norway calculated using a widely applicable method. Forest Ecol Manag 225:15-26
Gilliam FS (2007) The ecological significance of the herbaceous layer in temperate Forest ecosystems. BioScience 57:845-858. https://doi.org/10.1641/b571007
Heinrichs S, Bernhardt-Römermann M, Schmidt W (2010) The estimation of aboveground biomass and nutrient pools of understorey plants in closed Norway spruce forests and on clearcuts. Eur J For Res 129:613-624. https://doi.org/10.1007/s10342-010-0362-7
Kumar P, Chen HYH, Searle EB, Shahi C (2018) Dynamics of understorey biomass, production and turnover associated with long-term overstorey succession in boreal forest of Canada. Forest Ecol Manag 427:152-161. https://doi.org/10.1016/j.foreco.2018.05.066
Landuyt D, Lombaerde ED, Perring MP, Hertzog LR, Ampoorter E, Maes SL, Frenne PD, Ma S, Proesmans W, Blondeel H, Sercu BK, Wang B, Wasof S, Verheyen K (2019a) The functional role of temperate forest understorey vegetation in a changing world. Glob Chang Biol 25:3625-3641. https://doi.org/10.1111/gcb.14756
McRoberts RE, Tomppo EO, Næsset E (2010) Advances and emerging issues in national forest inventories. Scand J Forest Res 25:368-381. https://doi.org/10.1080/02827581.2010.496739
Melo LC, Schneider R, Fortin M (2018) Estimating model- and sampling-related uncertainty in large-area growth predictions. Ecol Model 390:62-69. https://doi.org/10.1016/j.ecolmodel.2018.10.011
Muukkonen P, Mäkipää R (2006) Empirical biomass models of understorey vegetation in boreal forests according to stand and site attributes. Boreal Environ Res 11:355-369
Muukkonen P, Mäkipää R, Laiho R, Minkkinen K, Vasander H, Finér L (2006) Relationship between biomass and percentage cover in understorey vegetation of boreal coniferous forests. Silva Fenn 40:231-245
Shaver GR, Chapin FS (1991) Production: biomass relationships and element cycling in contrasting Arctic vegetation types. Ecol Monogr 61:1-31. https://doi.org/10.2307/1942997
Thürig E, Kaufmann E (2010) Increasing carbon sinks through forest management: a model-based comparison for Switzerland with its eastern plateau and eastern Alps. Eur J Forest Res 129:563-572. https://doi.org/10.1007/s10342-010-0354-7
Welch NT, Belmont JM, Randolph JC (2007) Summer ground layer biomass and nutrient contribution to above-ground litter in an Indiana temperate deciduous Forest. Am Midl Nat 157(11-26):16
J. Schneuwly (internship student) and NFI scientific collaborators M. Baume and F. Giudici were responsible for the meticulous and efficient work in the field. Valuable advice on the implementation of the project were made by F. Cioldi, C. Düggelin, M. Keller, A. Kupferschmid, P. Waldner and M. Walser (all WSL). Work material was provided by the NFI (F. Cioldi) and M. Walser. Comments to earlier versions of the manuscript by N. Rogiers and F. Giudici are gratefully acknowledged. We further appreciate the valuable comments of two anonymous reviewers.
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