Journal of Mountain Science

, Volume 14, Issue 4, pp 662–673 | Cite as

Vegetation-based bioindication of humus forms in coniferous mountain forests

  • Kerstin Anschlag
  • Dylan Tatti
  • Niels Hellwig
  • Giacomo Sartori
  • Jean-Michel Gobat
  • Gabriele Broll


Humus forms, especially the occurrence and the thickness of the horizon of humified residues (OH), provide valuable information on site conditions. In mountain forest soils, humus forms show a high spatial variability and data on their spatial patterns is often scarce. Our aim was to test the applicability of various vegetation features as proxy for OH thickness. Subalpine coniferous forests dominated by Picea abies (L.) H. Karst. and Larix decidua Mill. were studied in the Province of Trento, Italian Alps, between ca. 900 and 2200 m a.s.l. Braun-Blanquet vegetation relevés and OH thickness were recorded at 152 plots. The vegetation parameters, tested for their suitability as indicators of OH thickness, encompassed mean Landolt indicator values of the herb layer (both unweighted and cover-weighted means) as well as parameters of vegetation structure (cover values of plant species groups) calculated from the relevés. To our knowledge, the predictive power of Landolt indicator values (LIVs) for humus forms had not been tested before. Correlations between OH thickness and mean LIVs were strongest for the soil reaction value, but indicator values for humus, nutrients, temperature and light were also significantly correlated with OH thickness. Generally, weighting with species cover reduced the indicator quality of mean LIVs for OH thickness. The strongest relationships between OH thickness and vegetation structure existed in the following indicators: the cover of forbs (excluding graminoids and ferns) and the cover of Ericaceae in the herb layer. Regression models predicting OH thickness based on vegetation structure had almost as much predictive power as models based on LIVs. We conclude that LIVs analysis can produce fairly reliable information regarding the thickness of the OH horizon and, thus, the humus form. If no relevé data are readily available, a field estimation of the cover values of certain easily distinguishable herb layer species groups is much faster than a vegetation survey with consecutive indicator value analysis, and might be a feasible way of quickly indicating the humus form.


Landolt indicator values OH horizon Forest ecosystem Montane forest Italian Alps 


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This study was conducted in the context of the D.A.CH. project DecAlp and funded by the German Research Foundation (DFG) (Grant No. Br1106/23-1), the Swiss National Science Foundation (SNF) (Grant No. 205321L_141186) and the Austrian Science Fund (FWF). The authors thank all colleagues in the project for their outstanding cooperation. We are also grateful to Dott. Fabio Angeli (Ufficio Distrettuale Forestale di Malè) and the Stelvio National Park for supporting the field work. We thank the anonymous reviewers for valuable comments on an earlier version of the manuscript.

Supplementary material

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  1. Aberegg I, Egli M, Sartori G, et al. (2009) Modelling spatial distribution of soil types and characteristics in a high Alpine valley (Val di Sole, Trentino, Italy). Studi Trentini di Scienze Naturali 85: 39–50.Google Scholar
  2. Ad-hoc-AG Boden (2005) Bodenkundliche Kartieranleitung, fith ed. Hannover: E. Schweizerbart'sche Verlagsbuchhandlung. (In German)Google Scholar
  3. Aeschimann D, Lauber K, Moser DM, et al. (2004) Flora alpina. Bern: Haupt Verlag.Google Scholar
  4. Andreetta A, Ciampalini R, Moretti P, et al. (2011) Forest humus forms as potential indicators of soil carbon storage in Mediterranean environments. Biology and Fertility of Soils 47 (1): 31–40. DOI: 10.1007/s00374-010-0499-zCrossRefGoogle Scholar
  5. Andreetta A, Cecchini G, Bonifacio E, et al. (2016) Tree or soil? Factors influencing humus form differentiation in Italian forests. Geoderma 264, Part A: 195–204. DOI: 10.1016/j.geoderma.2015.11.002CrossRefGoogle Scholar
  6. Ascher J, Sartori G, Graefe U, et al. (2012) Are humus forms, mesofauna and microflora in subalpine forest soils sensitive to thermal conditions? Biology and Fertility of Soils 48 (6): 709–725. DOI: 10.1007/s00374-012-0670-9CrossRefGoogle Scholar
  7. Bednorz F, Reichstein M, Broll G, et al. (2000) Humus forms in the forest-alpine tundra ecotone at Stillberg (Dischmatal, Switzerland): Spatial heterogeneity and classification. Arctic, Antarctic, and Alpine Research 32 (1): 21–29. DOI: 10.2307/1552406CrossRefGoogle Scholar
  8. Bernier N, Ponge JF (1994) Humus form dynamics during the sylvogenetic cylce in a mountain spruce forest. Soil Biology & Biochemistry 26 (2): 183–220. DOI: 10.1016/0038-0717(94) 90161-9CrossRefGoogle Scholar
  9. Bernier N, Gillet F (2012) Structural relationships among vegetation, soil fauna and humus form in a subalpine forest ecosystem: a Hierarchical Multiple Factor Analysis (HMFA). Pedobiologia 55 (6): 321–334. DOI: 10.1016/j.pedobi.2012. 06.004CrossRefGoogle Scholar
  10. Blasi C (2010) La vegetazione d’Italia con carta delle Serie di Vegetazione in scala 1:500000. Roma: Palombi. (In Italian)Google Scholar
  11. Böhner J, Antonic O (2009) Land surface parameters specific to topo-climatology. In: Hengl T, Reuter HI (eds.), Geomorphometry -Concepts, Software, Applications. Amsterdam: Elsevier. pp 195–226.CrossRefGoogle Scholar
  12. Böhner J, Köthe R, Conrad O, et al. (2002) Soil regionalisation by means of terrain analysis and process parameterization. In: Micheli E, Nachtergaele F, Montanarella L (eds.), Soil Classification 2001. The European Soil Bureau Research Report No. 7, EUR 20398 EN, Luxembourg. pp 213–222.Google Scholar
  13. Bonifacio E, Falsone G, Petrillo M (2011) Humus forms, organic matter stocks and carbon fractions in forest soils of northwestern Italy. Biology and Fertility of Soils 47 (5): 555–566. DOI: 10.1007/s00374-011-0568-yCrossRefGoogle Scholar
  14. Braun-Blanquet J (1964) Pflanzensoziologie. Grundzüge der Vegetationskunde, third ed. Wien, New York: Springer. (In German)Google Scholar
  15. Broll G, Brauckmann HJ, Overesch M, et al. (2006) Topsoil characterization -recommendations for revision and expansion of the FAO-Draft (1998) with emphasis on humus forms and biological features. Journal of Plant Nutrition and Soil Science 169 (3): 453–461. DOI: 10.1002/jpln.200521961CrossRefGoogle Scholar
  16. Carletti P, Vendramin E, Pizzeghello D, et al. (2009) Soil humic compounds and microbial communities in six spruce forests as function of parent material, slope aspect and stand age. Plant and Soil 315(1-2): 47–65. DOI: 10.1007/s11104-008-9732-zCrossRefGoogle Scholar
  17. Cornwell WK, Cornelissen JHC, Amatangelo K, et al. (2008) Plant species traits are the predominant control on litter decomposition rates within biomes worldwide. Ecology Letters 11 (10): 1065–1071. DOI: 10.1111/j.1461-0248.2008.01219.xCrossRefGoogle Scholar
  18. Costantini EAC, Fantappié M, L’Abate G (2013) Climate and Pedoclimate of Italy. In: Costantini EAC, Dazzi C (eds.), The Soils of Italy. Dordrecht, Heidelberg, New York: Springer. pp. 19–37.CrossRefGoogle Scholar
  19. Descheemaeker K, Muys B, Nyssen J, et al. (2009) Humus form development during forest restoration in exclosures of the Tigray Highlands, northern Ethiopia. Restoration Ecology 17 (2): 280–289. DOI: 10.1111/j.1526-100X.2007.00346.xCrossRefGoogle Scholar
  20. Diekmann M (2003) Species indicator values as an important tool in applied plant ecology -a review. Basic and Applied Ecology 4 (6): 493–506. DOI: 10.1078/1439-1791-00185CrossRefGoogle Scholar
  21. Dierschke H (1994) Pflanzensoziologie. Stuttgart: Ulmer. (In German)Google Scholar
  22. Dorrepaal E (2007) Are plant growth-form-based classifications useful in predicting northern ecosystem carbon cycling feedbacks to climate change? Journal of Ecology 95 (6): 1167–1180. DOI: 10.1111/j.1365-2745.2007.01294.xCrossRefGoogle Scholar
  23. Egli M, Mirabella A, Sartori G, et al. (2006) Effect of north and south exposure on weathering rates and clay mineral formation in Alpine soils. Catena 67 (3): 155–174. DOI: 10.1016/j.catena.2006.02.010CrossRefGoogle Scholar
  24. Egli M, Sartori G, Mirabella A, et al. (2009) Effect of north and south exposure on organic matter in high Alpine soils. Geoderma 149 (1): 124–136. DOI: 10.1016/j.geoderma.2008.11.027CrossRefGoogle Scholar
  25. Ellenberg H, Weber HB, Düll R, et al. (1992) Zeigerwerte von Pflanzen in Mitteleuropa (Indicator values of plants in Central Europe). Scripta Geobotanica 18. Göttingen: Goltze. (In German, with English summaries)Google Scholar
  26. Ewald J (1999) Relationships between floristic and micro site variability in coniferous forests of the Bavarian Alps. Phytocoenologia 29 (3): 327–344. DOI: 10.1127/phyto/29/1999/327CrossRefGoogle Scholar
  27. Ewald J (2000) The influence of coniferous canopies on understorey vegetation and soils in mountain forests of the northern Calcareous Alps. Applied Vegetation Science 3 (1): 123–134. DOI: 10.2307/1478926CrossRefGoogle Scholar
  28. Ewald J (2009) Epigeic bryophytes do not improve bioindication by Ellenberg values in mountain forests. Basic and Applied Ecology 10 (5): 420–426. DOI: 10.1016/j.baae.2008.09.003CrossRefGoogle Scholar
  29. Ewald J, Hennekens S, Conrad S, et al. (2013) Spatial and temporal patterns of Ellenberg nutrient values in forests of Germany and adjacent regions -a survey based on phytosociological databases. Tuexenia 33: 93–109.Google Scholar
  30. Fischer HS (2015) On the combination of species cover values from different vegetation layers. Applied Vegetation Science 18 (1): 169–170. DOI: 10.1111/avsc.12130CrossRefGoogle Scholar
  31. Gobat J-M, Aragno M, Matthey W (2010) Le sol vivant: bases de pédologie, biologie des sols, third ed. Lausanne: PPUR Presses polytechniques et universitaires romandes. (In French)Google Scholar
  32. Hellwig N, Anschlag K, Broll G (2016) A fuzzy logic based method for modeling the spatial distribution of indicators of decomposition in a high mountain environment. Arctic, Antarctic, and Alpine Research 48 (4): 623–635. DOI: 10.1657/AAAR0015-073CrossRefGoogle Scholar
  33. Hellwig N, Graefe U, Tatti D, et al. (2017) Upscaling the spatial distribution of enchytraeids and humus forms in a high mountain environment on the basis of GIS and fuzzy logic. European Journal of Soil Biology 79: 1–13. DOI: 10.1016/j.ejsobi. 2017.01.001CrossRefGoogle Scholar
  34. Hiller B, Müterthies A, Holtmeier FK, et al. (2002) Investigations on spatial heterogeneity of humus forms and natural regeneration of Larch (Larix decidua Mill.) and Swiss Stone Pine (Pinus cembra L.) in an alpine timberline ecotone (Upper Engadine, Central Alps, Switzerland). Geographica Helvetica 57 (2): 81–90. DOI: 10.5194/gh-57-81-2002CrossRefGoogle Scholar
  35. Hiller B, Nuebel A, Broll G, et al. (2005) Snowbeds on Silicate Rocks in the Upper Engadine (Central Alps, Switzerland)-Pedogenesis and Interactions among Soil, Vegetation, and Snow Cover. Arctic, Antarctic, and Alpine Research 37 (4): 465–476.CrossRefGoogle Scholar
  36. Käfer J, Witte JPM (2004) Cover-weighted averaging of indicator values in vegetation analyses. Journal of Vegetation Science 15 (5): 647–652. DOI: 10.1111/j.1654-1103.2004.tb02306.xCrossRefGoogle Scholar
  37. Klaus VH, Kleinebecker T, Boch S, et al. (2012) NIRS meets Ellenberg's indicator values: Prediction of moisture and nitrogen values of agricultural grassland vegetation by means of near-infrared spectral characteristics. Ecological Indicators 14 (1): 82–86. DOI: 10.1016/j.ecolind.2011.07.016CrossRefGoogle Scholar
  38. Küchler M, Küchler H, Bedolla A, et al. (2015) Response of Swiss forests to management and climate change in the last 60 years. Annals of Forest Science 72 (3): 311–320. DOI: 10.1007/s13595-014-0409-xCrossRefGoogle Scholar
  39. Lalanne A, Bardat J, Lalanne-Amara F, et al. (2008) Opposite responses of vascular plant and moss communities to changes in humus form, as expressed by the Humus Index. Journal of Vegetation Science 19 (5): 645–652. DOI: 10.3170/2007-8-18431CrossRefGoogle Scholar
  40. Lalanne A, Bardat J, Lalanne-Amara F, et al. (2010) Local and regional trends in the ground vegetation of beech forests. Flora 205 (7): 484–498. DOI: 10.1016/j.flora.2009.12.032CrossRefGoogle Scholar
  41. Landolt E, Bäumler B, Erhardt A, et al. (2010) Ecological indicator values and biological attributes of the flora of Switzerland and the Alps. Bern: Haupt. (In German)Google Scholar
  42. Li P, Wang Q, Endo T, et al. (2010) Soil organic carbon stock is closely related to aboveground vegetation properties in coldtemperate mountainous forests. Geoderma 154 (3): 407–415. DOI: 10.1016/j.geoderma.2009.11.023CrossRefGoogle Scholar
  43. Ma HP, Yang XL, Guo QQ, et al. (2016) Soil organic carbon pool along different altitudinal level in the Sygera Mountains, Tibetan Plateau. Journal of Mountain Science 13 (3): 476–483. DOI: 10.1007/s11629-014-3421-6CrossRefGoogle Scholar
  44. Mansfield ER, Helms BP (1982) Detecting multicollinearity. The American Statistician 36(3a): 158–160. DOI: 10.1080/00031305.1982.10482818CrossRefGoogle Scholar
  45. Meng X-L, Rosenthal R, Rubin DB (1992) Comparing correlated correlation coefficients. Psychological Bulletin 111 (1): 172–175. DOI: 10.1037/0033-2909.111.1.172CrossRefGoogle Scholar
  46. Minasny B, McBratney AB (2006) A conditioned Latin hypercube method for sampling in the presence of ancillary information. Computers & Geosciences 32 (9): 1378–1388. DOI: 10.1016/j.cageo.2005.12.009CrossRefGoogle Scholar
  47. Möller H (1997) Reaktions-und Stickstoffzahlen nach Ellenberg als Indikatoren für die Humusform in terrestrischen Waldökosystemen im Raum Hannover. Tuexenia 17: 349–365. (In German)Google Scholar
  48. Moore ID, Grayson R, Ladson A (1991) Digital terrain modelling: a review of hydrological, geomorphological, and biological applications. Hydrological Processes 5 (1): 3–30. DOI: 10.1002/hyp.3360050103CrossRefGoogle Scholar
  49. Myers L, Sirois MJ (2006) Spearman Correlation Coefficients, Differences between, Encyclopedia of Statistical Sciences. Hoboken: John Wiley & Sons, Inc.Google Scholar
  50. Nieto-Lugilde D, Lenoir J, Abdulhak S, et al. (2015) Tree cover at fine and coarse spatial grains interacts with shade tolerance to shape plant species distributions across the Alps. Ecography 38 (6): 578–589. DOI: 10.1111/ecog.00954CrossRefGoogle Scholar
  51. Ponge JF (2003) Humus forms in terrestrial ecosystems: a framework to biodiversity. Soil Biology and Biochemistry 35 (7): 935–945. DOI: 10.1016/S0038-0717(03)00149-4CrossRefGoogle Scholar
  52. Ponge JF (2013) Plant–soil feedbacks mediated by humus forms: A review. Soil Biology and Biochemistry 57: 1048–1060. DOI: 10.1016/j.soilbio.2012.07.019CrossRefGoogle Scholar
  53. Ponge JF, Sartori G, Garlato A, et al. (2014) The impact of parent material, climate, soil type and vegetation on Venetian forest humus forms: A direct gradient approach. Geoderma 226-227: 290–299. DOI: 10.1016/j.geoderma.2014.02.022CrossRefGoogle Scholar
  54. Provincia Autonoma di Trento and Servizio Foreste e Fauna (n.d.) I Dati Della Pianificazione Forestale Aggiornati al 31/12/2004. Trento. (In Italian)Google Scholar
  55. Provincia Autonoma di Trento (2006-2008) LIDAR rilievo 2006/2007/2008. (Available online at:, accessed on 03 Sep. 2016)Google Scholar
  56. Roudier P, Hewitt AE, Beaudette DE (2012) A conditioned Latin hypercube sampling algorithm incorporating operational constraints. In: Minasny B, Malone BP, McBratney AB (eds.), Digital Soil Assessments and Beyond: Proceedings of the 5th Global Workshop on Digital Soil Mapping 2012, Sydney, Australia. London: CRC Press. pp 227–231.CrossRefGoogle Scholar
  57. Sartori G, Mancabelli A (2009) Carta dei suoli del Trentino: scala 1:250.000. Museo Tridentino di Scienze Naturali di Trento, Centro di Ricerca per l’Agrobiologia e la Pedologia di Firenze. (In Italian)Google Scholar
  58. Schaffers AP, Sýkora KV (2000) Reliability of Ellenberg indicator values for moisture, nitrogen and soil reaction: a comparison with field measurements. Journal of Vegetation Science 11 (2): 225–244. DOI: 10.2307/3236802CrossRefGoogle Scholar
  59. Scherrer D, Körner C (2011) Topographically controlled thermal-habitat differentiation buffers alpine plant diversity against climate warming. Journal of Biogeography 38 (2): 406–416. DOI: 10.1111/j.1365-2699.2010.02407.xCrossRefGoogle Scholar
  60. Szymura TH, Szymura M, Maciol A (2014) Bioindication with Ellenberg's indicator values: A comparison with measured parameters in Central European oak forests. Ecological Indicators 46: 495–503. DOI: 10.1016/j.ecolind.2014.07.013CrossRefGoogle Scholar
  61. Wagener J (2014) Die Vegetation der Region Val di Sole in den italienischen Alpen. Master thesis, University of Osnabrueck, Osnabrueck. (In German)Google Scholar
  62. Wookey PA, Aerts R, Bardgett RD, et al. (2009) Ecosystem feedbacks and cascade processes: understanding their role in the responses of Arctic and alpine ecosystems to environmental change. Global Change Biology 15 (5): 1153–1172. DOI: 10.1111/j.1365-2486.2008.01801.xCrossRefGoogle Scholar
  63. Zackrisson O, Nilsson MC, Dahlberg A, et al. (1997) Interference mechanisms in conifer-Ericaceae-feathermoss communities. Oikos 78 (2): 209–220. DOI: 10.2307/3546287CrossRefGoogle Scholar
  64. Zevenbergen LW, Thorne CR (1987) Quantitative analysis of land surface topography. Earth Surface Processes and Landforms 12 (1): 47–56. DOI: 10.1002/esp.3290120107CrossRefGoogle Scholar

Copyright information

© Science Press, Institute of Mountain Hazards and Environment, CAS and Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Kerstin Anschlag
    • 1
  • Dylan Tatti
    • 2
    • 3
  • Niels Hellwig
    • 1
  • Giacomo Sartori
    • 4
  • Jean-Michel Gobat
    • 2
  • Gabriele Broll
    • 1
  1. 1.Institute of GeographyUniversity of OsnabrueckOsnabrueckGermany
  2. 2.Functional Ecology LaboratoryUniversity of Neuchâtel, Rue Emile-Argand 11NeuchâtelSwitzerland
  3. 3.School of Agricultural, Forest and Food Sciences HAFLBern University of Applied SciencesZollikofenSwitzerland
  4. 4.MUSETrentoItaly

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