Annals of Forest Science

, 74:63 | Cite as

EuMIXFOR empirical forest mensuration and ring width data from pure and mixed stands of Scots pine (Pinus sylvestris L.) and European beech (Fagus sylvatica L.) through Europe

  • Michael Heym
  • Ricardo Ruíz-Peinado
  • Miren Del Río
  • Kamil Bielak
  • David I. Forrester
  • Gerald Dirnberger
  • Ignacio Barbeito
  • Gediminas Brazaitis
  • Indrė Ruškytkė
  • Lluís Coll
  • Marek Fabrika
  • Lars Drössler
  • Magnus Löf
  • Hubert Sterba
  • Václav Hurt
  • Viktor Kurylyak
  • Fabio Lombardi
  • Dejan Stojanović
  • Jan Den Ouden
  • Renzo Motta
  • Maciej Pach
  • Jerzy Skrzyszewski
  • Quentin Ponette
  • Géraud De Streel
  • Vit Sramek
  • Tomáš Čihák
  • Tzvetan M. Zlatanov
  • Admir Avdagic
  • Christian Ammer
  • Kris Verheyen
  • Buraczyk Włodzimierz
  • Andrés Bravo-Oviedo
  • Hans Pretzsch
Data Paper

Abstract

Key message

This data set provides unique empirical data from triplets of Scots pine (Pinus sylvestrisL.) and European beech (Fagus sylvaticaL.) across Europe. Dendrometric variables are provided for 32 triplets, 96 plots, 7555 treesand 4695 core samples. These data contribute to our understanding of mixed stand dynamics.Dataset access athttp://dx.doi.org/10.5061/dryad.8v04m. Associated metadata available athttps://metadata-afs.nancy.inra.fr/geonetwork/apps/georchestra/?uuid=b3e098ca-e681-4910-9099-0e25d3b4cd52&hl=eng.

Keywords

EuMIXFOR data Mixed and monospecific stands Mixed stand dynamics Scots pine European beech 

1 Background

During the last decade, empirical research often showed superiority of ecosystem functions of mixed forest stands compared to monocultures, e.g. productivity (Río and Sterba 2009; Pretzsch et al. 2010, 2013; Vallet and Perot 2011, Condés et al. 2013; Bielak et al. 2014; Liang et al. 2016), structural diversity (Río et al. 2016a) or stability (Knoke et al. 2008). However, the underlying processes are often still poorly understood. Moreover, generalizations require empirical data from different growing conditions, e.g. soil and climate. This unique data set includes 32 triplets across 16 European countries (Fig. 1) representing different ecoregions. Each triplet includes two pure stands and one with the two species in mixture, all with similar climatic and soil conditions. Effects of mixing these species can be analysed with respect to their corresponding pure stands. Under the umbrella of COST Action FP1206 EuMIXFOR (European Network on Mixed Forests), these triplets were established for the common European tree species, Scots pine and European beech. These species differ in their functional traits, and the data set provides a unique opportunity to develop a general understanding of the causes and patterns of mixing responses.
Fig. 1

Distribution of the triplet locations across Europe and distribution of European beech and Scots pine according EUFORGEN (www.euforgen.org). Triangles represent triplet locations

2 Methods

2.1 Study sites

All stands (plots) represent mostly even-aged and mono-layered forests. Intra-specific age (species in pure vs species in mixed stand) within each triplet is always similar; however, inter-specific age may differ. Plots within a triplet are mostly rectangular in shape ranging from 0.01 to 1.6 ha in size. The geographical location, altitude, slope, aspect, mean annual temperature, annual precipitation and substrate are available for each plot (Table 1). Due to the established gradient, these variables differ between triplets but are similar within each triplet.
Table 1

Overview of all triplets with: Country; corresponding country, Triplet name; name of the triplet in Fig. 1, Triplet; triplet identification number, longitude and latitude (for mixed stands), age; average age for the triplet (years, yr), altitude; elevation above sea level (m), slope (degrees), aspect (degrees [clockwise from north]), t; mean annual temperature (C°), p; annual precipitation (mm) and substrate. Altitude, slope and aspect represent average plot values

Country

Triplet name

Triplet

Longitude

Latitude

Age

Altitude

Slope

Aspect

t

p

Substrate

Austria

Aus_1

1048

16° 23′ 20.00″

47° 22′ 34.00″

40

525

18

213

8.5

750

Loamy sand

Belgium

Bel_2

1063

04° 19′ 29.60″

50° 45′ 06.10″

115

160

1

270

7.5

852

Loam

Belgium

Bel_1

1057

05° 27′ 00.00″

50° 01′ 48.00″

150

545

3

180

11

1175

Stony loam

Bosnia & Herzegovina

BHe_1

1059

18° 29′ 56.12″

44° 13′ 34.56″

135

697

25

225

9.5

939

Humus-silicate soil-ranker

Bulgaria

Bul_1

1047

23° 21′ 03.00″

41° 53′ 43.00″

65

1187

18

350

6

750

Loamy sand

Czech Republic

Cze_1

1049

16° 36′ 08.78″

49° 18′ 14.40″

45

440

7

45

7.5

620

Cambisol mezotrofic

Czech Republic

Cze_2

1058

13° 12′ 45.90″

49° 58′ 02.50″

55

554

11

328

7.1

656

Dystric and podzol cambisol

France

Fra_1

1040

07° 29′ 13.60″

48° 58′ 41.80″

60

275

34

315

9.7

948

Sandstone sandy soil

Germany

Ger_1

1033

11° 14′ 12.49″

48° 34′ 57.95″

57

430

1

45

8.5

700

Slightly loamy sand

Germany

Ger_2

1031

09° 03′ 54.36″

50° 06′ 48.74″

55

250

0

 

9

720

Slightly loamy sand

Germany

Ger_3

1032

10° 58′ 13.12″

49° 53′ 11.64″

47

250

2

30

8

650

Loamy sand

Germany

Ger_4

1071

08° 01′ 03.88″

49° 24′ 57.77″

65

40

1

60

9

675

Loamy sand

Germany

Ger_5

1034

08° 10′ 48.58″

48° 59′ 11.66″

57

370

3

0

10

675

Slightly loamy sand

Germany

Ger_6

1070

12° 44′ 08.30″

48° 11′ 12.47″

65

400

0

 

8

560

Slightly loamy sand

Germany

Ger_7

1061

13° 36′ 54.28″

52° 04′ 45.47″

80

73

0

 

8.6

520

Sandy

Italy

Ita_1

1055

10° 56′ 10.61″

46° 04′ 02.93″

40

1034

8

31

7.8

1050

Cutanic luvisoil

Italy

Ita_2

1062

07° 03′ 53.30″

44° 54′ 12.49″

55

1475

25

315

7.9

938

Inceptisol

Lithuania

Lit_1

1051

22° 24′ 24.10″

55° 04′ 47.30″

90

25

0

 

6.5

750

Sand and slightly loamy sand

Lithuania

Lit_2

1052

21° 32′ 23.44″

55° 27′ 02.80″

111

20

0

 

6.5

800

Sand and slightly loamy sand

Netherlands

Net_1

1043

06° 01′ 20.42″

52° 25′ 40.55″

47

34

2

68

9.7

825

Coarse sand

Poland

Pol_2

1036

19° 54′ 42.27″

53° 48′ 19.15″

81

136

0

 

7.9

666

Loamy sand and sand

Poland

Pol_1

1035

14° 36′ 17.51″

53° 20′ 07.40″

55

60

0

 

9.2

556

Slightly loamy sand

Poland

Pol_3

1037

20° 41′ 08.90″

50° 59′ 27.96″

76

383

2

275

7.8

662

Sandstone loamy sand and loam

Poland

Pol_4

1044

20° 13′ 45.84″

50° 01′ 27.60″

57

208

1

0

8.2

650

Slightly loamy sand

Poland

Pol_5

1045

20° 19′ 37.26″

50° 01′ 36.00″

55

213

0

 

8.2

650

Loamy sand

Serbia

Ser_1

1056

19° 37′ 30.00″

43° 42′ 17.40″

75

1080

21

0

7.7

1020

Loam with a little sand

Slovakia

Slo_1

1046

18° 31′ 11.19″

48° 33′ 09.18″

55

524

15

90

6.9

730

Cambisoil

Spain

Sp_1

1042

03° 10′ 19.00″ W

42° 05′ 57.00″

40

1293

47

120

8.9

860

Sandy loam

Spain

Sp_2

1041

02° 15′ 44.23″

42° 10′ 18.09″

50

1116

30

135

8

1100

Loam slightly clay

Sweden

Swe_1

1054

13° 35′ 35.00″

56° 09′ 12.00″

80

120

13

222

8

700

Loamy sand

Sweden

Swe_2

1053

14° 11′ 46.00″

55° 42′ 33.00″

65

25

8

120

7

800

Sandy till

Ukraine

Ukr_1

1060

23° 39′ 44.00″

49° 57′ 05.00″

105

316

0

 

7.6

673

Slightly loamy sand

2.2 Survey protocol

A standard protocol for data collection was established and applied for each triplet. For all trees exceeding diameter at breast height of 7 cm, mandatory attributes (Table 2) were defined and collected including tree number (Nr), tree species, diameter at breast height (dbh), tree height (h) and crown base height (cbh). In addition, on each plot, increment core samples for 10–20 dominant trees per species and angle count samples (acs) for the sampled trees were collected (Bitterlich 1952). Specific information is assigned to each tree indicating its status (alive, dead or damaged). Only standing trees were recorded. Spatial location (Cartesian coordinates) of individual trees was defined as an optional variable but was measured on most plots.
Table 2

Overview of measured mandatory and optional descriptive and dendrometric attributes

 

Variable

Description

Mandatory

Longitude

Plot specific

Latitude

Plot specific

Altitude

Plot specific; m (E. a.s.l.)

Slope

Plot specific; degrees

Aspect

Plot specific; degrees

Plot size

Plot specific; hectares

Date of establishment/measurement

Triplet specific; yyyy-mm

Age

Species and plot specific; years

Tree number

Living trees; ascending order

Tree species

Scientific species name

Diameter at breast height

Tree specific; cm

Tree height

Tree specific; m

Crown base height

Tree specific; m

Increment cores

2 core samples/tree, 10–20 trees/plot/species

Local density

1–2 angle count measurements/cored tree; m2 ha−1

Optional

x-coordinate

Tree specific (Cartesian)

y-coordinate

Tree specific (Cartesian)

Crown radii

Tree specific; azimuth (degrees) and distance (m)

The tree numbering system is in ascending order starting from 1 for trees located within the plot and, if recorded, 901 for trees outside but with part of their crowns inside the plot. For all labelled trees, the corresponding species was determined. Diameter at breast height was measured for all trees using girth tape while a digital hypsometer was used for measuring tree height and crown base height. Increment cores aiming to reach the pith were taken at 1.30 m height in north and east directions from the stem using increment borers. Likewise, 1–2 angle count samples (acs) were recorded using a relascope with basal area factor 4 or 1 m2 ha−1. All data were stored using predefined templates. In total, 7555 trees were measured and 4695 core samples were taken.

For 24 triplets, optional data of individual tree location and crown radii were collected. Crown radii (m) were mostly measured in N, NE, E, SE, S, SW, W and NW cardinal directions with a minimum of four directions. Tree spatial information is given in Cartesian coordinates referring to a point of origin, e.g. the south west corner post of the corresponding plot.

2.3 Data processing—stand level data

Stand characteristics such as mean tree dimension, basal area (BA m2 ha−1) and volume stock per hectare (V m3 ha−1) were derived for each plot and species group, pine and beech. Additional coniferous and deciduous species within the mixed stands were grouped either as pine or beech, respectively. In pure stands, all additional species were assigned to the corresponding main species. Stand attributes are based on all surveyed living trees within the corresponding plot and are expressed per ha. A Petterson height curve function (Petterson 1955) was parameterized for each plot and species group. Missing tree heights and the height of quadratic mean diameter tree (hg) were calculated applying the corresponding height curve function. Stand volume was derived while taking into account each individual tree’s diameter at breast height, derived tree height and species-specific form factors (Franz 1971). All stand characteristics (Table 3) were calculated using standard evaluation software available at the Chair of Forest Growth and Yield Science, TU München (Biber 2013).
Table 3

Example (triplet 1033) of main stand characteristics per plot with: Country; corresponding country for the triplet, Triplet; triplet identification number, Plot plot identification (pibe; mixed stand pi; pure pine, be; pure beech), Year; year of survey, Species; species name, Age; plot age at survey, N; number of trees, dg; quadratic mean diameter (cm), hg height of quadratic mean diameter (m), BA; basal area (m2) and V; merchantable volume per hectare (m3). All variables refer to the main species and 1 ha

Country

Triplet

Plot

Year

Species

Age

N

dg

hg

BA

V

(cm)

(m)

(m2 ha−1)

(m3 ha−1)

Germany

1033

pibe

2013

Pine

50

330

25.5

22.8

16.8

175

 

pibe

2013

Beech

50

818

15.7

19.5

15.9

157

 

pibe

2013

Total

 

1148

  

32.8

332

 

pi

2013

Pine

65

286

33.2

26

24.7

295

 

be

2013

Beech

53

1032

17

24

23.3

279

3 Access to data and metadata description

The data set is available from the Dryad Digital Repository http://dx.doi.org/10.5061/dryad.8v04m (Heym et al. 2017) and cover five files (Contact.txt, TripletInformation.txt, Trees.txt, Crowns.txt and Cores.txt). Contact.txt file includes all contact information of the specific data provider. Associated metadata available at https://metadata-afs.nancy.inra.fr/geonetwork/apps/georchestra/?uuid=b3e098ca-e681-4910-9099-0e25d3b4cd52&hl=eng.

TripletInformation.txt provides plot and stand characteristics. The first two columns (Triplet and Plot) identify each plot. Plot characteristics cover year and month of the survey (year and month), plot size (area), location (longitude and latitude), elevation above sea level (altitude), inclination (slope), exposition (aspect), temperature (t), precipitation (p) and substrate (substrate). Stand characteristics cover tree species (species), age (Age), number of trees (N), quadratic mean diameter (dg) and corresponding height (hg), basal area (BA) and merchantable stand volume over bark (V). All variables refer to 1 ha and the main species (species) pine or beech, respectively. Additional species are assigned either as pine or beech as described in methods.

Trees.txt includes all measured tree attributes. Each row describes one tree and can be identified by columns Triplet, Plot and Nr. Tree information covers scientific species name (species), tree’s spatial information (x and y), diameter at breast height (dbh), tree height (h), crown base height (cbh) and two angle count measurements for the cored trees (acs_1 and acs_2). Column cored distinguishes cored trees (1) and non-cored trees (0). Any additional information is given in column remarks.

Crowns.txt contains all measured crown radii (distance) with its corresponding cardinal direction (azimuth). Column Triplet, Plot and Nr ensure the link to the corresponding tree in Trees.txt. In addition y_axis_to_north describes the deviation of the y-axis to north direction.

Cores.txt file contains all year ring width values (rw) for each year (year) and the two different directions (azimuth). Column Triplet, Plot and Nr ensure unique tree identification and link the corresponding tree attributes.

Missing information is always described using NA. All tables can be linked based on the columns Triplet, Plot and Nr. Detailed description of the metadata can be found in the supplementary material (EuMIXFOR Scots pine - European beech data.xlsx).

4 Technical validation

Validation of each data set was performed including crosschecks by hand, graphical and numerical tests. First, the unit of tree attributes and year ring width was carefully validated. Tree and corresponding core labels were compared manually and corrected where needed. Cross-dating of radial increment was performed by each collaborator and inconsistent cores were dropped. The relationship of individual tree height to its corresponding diameter at breast height, expressed by a Petterson height curve, was analysed for each triplet, shown as an example for triplet 1032 in Fig. 2.
Fig. 2

Detecting (left side,a) and correcting (right side,b) inconsistent height measurements. Data from triplet 1032 for pine (black) and beech (grey) in pure (triangles) and mixed stands (circles). Dashed and solid lines representing species-specific Petterson height curve for mixed and pure stands, respectively

Crown base height was validated by visualizing cbh and corresponding tree height. If spatial information was available, visual inspection was applied to validate tree position and crown measurements. In addition, allometric relationships between diameter at breast height and crown projection area (cpa), calculated based on mean squared crown radii, cpa = adbhb , were validated visually (Fig. 3).
Fig. 3

Crown projection area (cpa) and diameter at breast height (dbh) for all measured pine (left side) and beech (right side) in pure (triangles) and mixed stands (circles). Allometric relationships are described by solid (pure stands) and dashed lines (mixed stands)

Location of border trees was inspected by means of graphical output. Core data were also compared with species-specific variation of basal area increment in pure and mixed stands within each triplet (Fig. 4).
Fig. 4

Example of basal area increment per calendar year for beech (upper; a, b), and pine (lower; c, d), in pure (left; a, c) and mixed stand (right; b, d), for triplet 1035. Grey solid lines represent arithmetic means and n refers to number of cores for years shown on x-axis

All data were imported into an access database and technical plausibility checks were applied, e.g. data types or duplicates.

5 Reuse potential and limits

The original data set covers attributes at the tree level, increments cores, stand and triplet characteristics and has been used for different purposes by Pretzsch et al. (2015, 2016), Río et al. (2016b, 2017), Dirnberger et al. (2016) and Forrester et al. (2017). The data set presented here describes the original data, but ongoing projects can complement the published data by additional measurements of resource supply, nutritional status and wood properties. Furthermore, it has the potential to reveal the effect of site conditions on the mixing responses at the stand, species and individual tree level. Available spatial information allows analyses of crown projection (Dirnberger et al. 2016), crown architecture or light regimes. The available core data allow retrospective analyses and can be linked to corresponding tree attributes, e.g. Pretzsch et al. (2016) or Río et al. (2017). Reconstructing stand characteristics provides temporal stand level information, e.g. Pretzsch et al. (2015). Moreover, spatial information of most of the plots allows for distance dependent analysis at small scales.

However, for specific analyses, some plot sizes could be too small. Tree and stand characteristics are only available for the year of survey. Therefore, temporal analyses require reconstructions at the tree and stand levels.

Notes

Acknowledgements

This article is based upon work from COST Action FP1206 (EuMIXFOR), supported by COST (European Cooperation in Science and Technology). All contributors thank their national funding institutions and the woodland owners for agreeing to establish, measure, analyse and reuse data from the triplets. Many thanks to the anonymous reviewer and data paper handling editor Marianne Peiffer for their helpful comments to improve the early draft of the manuscript.

Compliance with ethical standards

Funding

Networking for the design and discussion of the transect study was supported by COST Association during FP1206 COST Action (EuMIXFOR: European mixed forests – Integration Scientific Knowledge in Sustainable Forest Management). Funding for the establishment of the plots and collection of the data belonged to co-author.

Supplementary material

13595_2017_660_MOESM1_ESM.xlsx (29 kb)
ESM 1(XLSX 28 kb).

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Copyright information

© INRA and Springer-Verlag France SAS 2017

Authors and Affiliations

  • Michael Heym
    • 1
  • Ricardo Ruíz-Peinado
    • 2
    • 3
  • Miren Del Río
    • 2
    • 3
  • Kamil Bielak
    • 4
  • David I. Forrester
    • 5
  • Gerald Dirnberger
    • 6
  • Ignacio Barbeito
    • 7
  • Gediminas Brazaitis
    • 8
  • Indrė Ruškytkė
    • 8
  • Lluís Coll
    • 9
  • Marek Fabrika
    • 10
  • Lars Drössler
    • 11
  • Magnus Löf
    • 11
  • Hubert Sterba
    • 6
  • Václav Hurt
    • 12
  • Viktor Kurylyak
    • 13
  • Fabio Lombardi
    • 14
  • Dejan Stojanović
    • 15
  • Jan Den Ouden
    • 16
  • Renzo Motta
    • 17
  • Maciej Pach
    • 18
  • Jerzy Skrzyszewski
    • 18
  • Quentin Ponette
    • 19
  • Géraud De Streel
    • 19
  • Vit Sramek
    • 20
  • Tomáš Čihák
    • 21
  • Tzvetan M. Zlatanov
    • 22
  • Admir Avdagic
    • 23
  • Christian Ammer
    • 24
  • Kris Verheyen
    • 25
  • Buraczyk Włodzimierz
    • 4
  • Andrés Bravo-Oviedo
    • 2
    • 3
  • Hans Pretzsch
    • 1
  1. 1.Chair for Forest Growth and Yield ScienceTechnische Universität MünchenMunichGermany
  2. 2.Department of Silviculture and Forest Systems ManagementINIA-CIFORMadridSpain
  3. 3.Sustainable Forest Management Research Institute University of Valladolid & INIAValladolidSpain
  4. 4.Department of SilvicultureWarsaw University of Life SciencesWarsawPoland
  5. 5.Swiss Federal Institute for Forest, Snow and Landscape Research WSLBirmensdorfSwitzerland
  6. 6.Department of Forest and Soil ScienceBOKU University of Natural Resources and Life SciencesViennaAustria
  7. 7.Laboratoire d’Etude des Ressources Forêt Bois (LERFoB)INRA centre of NancyChampenouxFrance
  8. 8.Institute of Forest Biology and Silviculture, Faculty of Forest Science and EcologyAleksandras Stulginiskis UniversityKaunas distLithuania
  9. 9.Department of Agriculture and Forest Engineering – Forest Sciences Centre of Catalonia (CTFC)University of LleidaLleidaSpain
  10. 10.Department of Forest Management and Geodesy, Faculty of ForestryTechnical University in ZvolenZvolenSlovakia
  11. 11.Southern Swedish Forest Research CentreSwedish University of Agricultural SciencesAlnarpSweden
  12. 12.Department of SilvicultureMendel UniversityBrnoCzech Republic
  13. 13.Forestry Academy of Sciences of UkraineLvivUkraine
  14. 14.Dipartimento di AGRARIAUniversità Mediterranea di Reggio CalabriaReggio CalabriaItaly
  15. 15.Institute of Lowland Forestry and EnvironmentUniversity of Novi SadNovi SadSerbia
  16. 16.Forest Ecology and Forest Management GroupWageningen University & ResearchWageningenThe Netherlands
  17. 17.Department of Agricultural, Forest and Food Sciences DISAFAUniversity of TurinTurinItaly
  18. 18.Department of Silviculture, Institute of Forest Ecology and SilvicultureUniversity of AgricultureKrakowPoland
  19. 19.Faculty of Bioscience Engineering & Earth and Life InstituteUniversité catholique de LouvainLouvain-la-NeuveBelgium
  20. 20.Forestry and Game Management Research InstituteOpocnoCzech Republic
  21. 21.Forestry and Game Management Research InstituteJílovištěCzech Republic
  22. 22.Department of SilvicultureForest Research InstituteSofiaBulgaria
  23. 23.Faculty of ForestryUniversity SarajevoSarajevoBosnia and Herzegovina
  24. 24.Abteilung Waldbau und Waldökologie der gemäßigten ZonenGeorg-August-Universität GöttingenGöttingenGermany
  25. 25.Faculty of Bioscience Engineering, Forest & Nature LabGhent UniversityMelleBelgium

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