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Biogeographic Lessons From Romanian Beech-Oak Forest Ecotones
for the Future of German Forests– The NEMKLIM Project
PD Dr. Stefan Hohnwald
Nemoral Forests Under Climate Extremes
Project head: Prof. Dr. Helge WalentowskiUniversity of Applied Sciences and ArtsFaculty of Resource ManagementPedology, Geobotany and Nature ConservationBüsgenweg 1a, north campus
Coordinator: PD Dr. Stefan Hohnwald
since January 2018; for 3 years (2018-2020)
budget: 489.175 €
Sponsered by the BfNwith funds of the Federal Ministry for the
Environment, Nature Conservation and Nuclear Safety
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NEMKLIM-Team
HAWK: Production, Use and Nuture of Woody Plants, and Botany
Prof. Dr. Henning Wildhagen
Albrecht-von-Haller-Institute for Plant Sciences
Vegetation and Phytodiversity Analysis
Prof. Dr. Erwin Bergmeier
Veronika Öder
Plant Ecology and Ecosystems Research
Prof. Dr. Christoph Leuschner
Jan Kasper
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Romanian NEMKLIM Partners
“Marin Drăcea” National Research and Development Institute in Forestry
Dr. Marius Teodosiu (coordinator)
Dr. Ana Petriţan
Gheorghe Marin
Daniel Turcu
Transilvanian University of Braşov
Assoc. Prof. Dr. V. Adrian Indreica
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Further NEMKLIM Cooperation
Forest Genetics and Tree BreedingProf. Dr. Oliver GailingDr. Markus Müller
BfN, Bonn-Bad GodesbergDr. Axel SsymankExpert in hoverflies (Syrphidae)
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• extreme dry and warm summer 2018 in Central Europe
• consequences of climate change on nature
• how Central European mixed beech forests will react to these ecological shifts?
• will beeches still dominate our forests?
• will other tree species alongside beech play a more important role?
• how will species composition react to these ecological shifts?
• such questions are topping the list, not only in forestry
Introduction
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Introduction- Forest Development in Germany
• how do our German forests look like in 60-80 years?
• climate change: on average, 2° K warmer, longer summer droughts
• Romanian Beech Forests are already 2 K warmer!
• „space for time“-approach
• what can we learn from Romanian Beechand oak forests?
• oak taxa are more competitive!
• species composition and biodiversity
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• in the context of energy transition
• strong consideration of the material timber but also on energetic firewood use
• based on the project results, recommendations for policy, forestry and further research will be formulated
• models and algorithms for the estimation ofdendro-biomass
• on basis of the forest Inventury data(NFI Data Romania)
Dendro-Biomass
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MARGINS project
NEMKLIM is based on research questions and results of theMARGINS project (Prof. Dr. Ewald, Prof. Dr. Menzel)
• beeches at its southern edge of the area in terms of vegetation and habitat• despite higher temperatures, beeches are accompanied by similar indicator plants• trees show a remarkable growth• this indicates a complex overlay of advantages and risks• longer growth period • but higher risks of drought stress
„In microclimatic favorable locations (humid gorges, proximity of waters), beeches descend even to astonishingly low elevations and directly encounter Mediterranean species such as the stone oak- a phenomenon that urgently needs further investigation!"
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The Beech in Europe
(Gebhardt et al. 2007)
Its ecological margins
• incomplete migration
• too cold winters
• not enough precipitation
• long droughts
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Potential Natural Vegetation of Europe
(Bohn et al. 2003)
• 1000 km away NW-SE• 550 km further south
• Comparison:central Germany → lower western Carpathians
• Hungarian oak (Quercus frainetto Ten.)• Turkey oak (Quercus cerris L.)• Balkanic durmast oak (Quercus dalechampii Ten.)
• thermophilous mixed deciduous broad-leaved forests:• sub-Mediterranean-subcontinental thermophilous bitter oak forests
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Study Region with 3 Real Replications
Maciova
Milova
Eşelniţa
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Data Inquiry and Compilation
• forest inventory data (NFI Data Romania; 1218 plots)
• available vegetation data (BOHN & NEUHÄUSL 2000/2003)RFD Romanian Forest Data base; 924 plots (INDREICA 2012)
• climate data of Worlclim 1.4, (HIJMANS et al. 2018)
• Soil data from NFI [pH, C:N]
• European Soil Database for AWC
Mscr. THEODOSIU et al. accepted to AFR, 7.12.2018
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KÖLLING & ZIMMERMANN (2014)Heinrichs et al. (2016)
Romania
Climatic Development of Central Germany
Warming of 1.3-3.7°C
RepresentativeConcentration Pathways:RCP 2.6, 4.5, 6.0, 8.5
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T. METTE
for HEINRICHS ET AL. (2016)
excluded
Târnava Mare
Climatic adjustments
Mean temperature of the warmest quarter Precipitation sum of the warmest quarter
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Lippa 126 mNN, 10.8°C; 604 mm January -1.3; July 20.9°C
QUERCETUM FRAINETTO- CERRIS
(G20) Q. frainetto, Q. cerris, A. tataricum
POTENTILLO MICRANTHAE- Q. DALECHAMPII
(G16)Q. cerris, Q. dalechampii, Q. petraea, T. tomentosa, Acer campestre.,U. minor, Carpinus betulus
CARPINO-FAGETUM (F126)F. sylvatica, C. betulus, Quercus petraea
(BOHN & NEUHÄUSL 2003)
500 mNN
700 mNN
300 mNN10°C
9°C
8°C
100 mNN11°C
800 mNN7,5°C
Transect 1- Milova
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t1
t2
t3
Franconian Plateau-Carpathians
(WALENTOWSKI ET AL. 2017)
Transectst1 = Milovat2 = Maciovat3 = Eşelniţa
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The EQm was calculated based on data in table 1 of WALENTOWSKI et al (2017), using the modified formula from MELLERT et al. (2018):EQm=BIO10/BIO18*1000 (temperature & precipitation of warmest quarter)
Tipping points, defined as a threshold for abrupt and irreversible change, and that the risk associated with crossing multiple tipping points increases with rising temperature (IPCC AR5)
(THEODOSIU et al. submitted)
t1
t2
t3
1218 NFI + 924 RFD plots
Western Carpathians
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do the tree species show growth responses to climatic extremes (to which extent: tolerance, sensitive, resilience…)?
How do F. sylvatica and Q. petraea react to climatic extremes (comparison with reference sites in Germany)
Do Q. cerris, Q. frainetto and T. tomentosa show a higher drought tolerance?
how does annual radial growth of selected forest stands perform?
What shifts in carbon storage can we expect if the predicted climate scenarios occur?
What consequences does this have for future forest management?
how do forest structures, carbon stocks and edaphic factors vary over the identified height gradient?
Characterization of the study area to solidify our space for time hypothesis
Identify structural similarities and differences to the reference sites in Germany
Dendrology, Forest Inventory, Soil Science
over this altitude gradient- climate sensitivity of the species F. sylvatica and
Q. petraea at its „distribution edge“, Q. cerris, Q. frainetto and T. tomentosa
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parent material: mesotrophic soil conditions on silicate or leached loess and loam
physical soil conditions: similar rooting depth (min. 70 cm), water storage capacities
pH range: pH levels slightly to moderately acidic pH 4.2- pH 5.0 (silicate buffer)
terrain and slope exposition: sites should be on slopes with expositions towards the dominant direction of sunlight: S, S - W, W, S – E or E (no extreme shaded sites)
stand age: 60 years
anthropogenic parameters: The German reference sites have all been subject to anthropogenic influence however had been exempted from recent harvesting impacts. Sites in western Romania should have the same characteristics
Transect Criteria
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Anticipated Elevation Gradient
Anticipated elevation gradient
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Transects
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480 dendro-core samples under analysis: sample preparation, cross dating and climate sensitivity analysis
sample tree criteria:dbh > 40 cm, healthy, monopodial and dominant in the canopy layer (Kraft 1-3)
→min. 30 samples for Q. cerris, Q. frainetto and T. tomentosa per gradient→min. 60 samples each for F. sylvatica and Q. petraea→ additional 30 samples for F. sylvatica in a northern slope at low altitude (350 m) total = 240 per gradient
additional tree parameters:sample trees: position (elevation, exposition) dbh, height, social position and vitality
the nearest competitors (max. 3): species, distance, dbh and height
Dendrology Cores
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for each gradient:
2 dendrometer plots per forest type (1. Q. spp., 2. Q. petraea & F. sylvatica and 3. F. sylvatica)
→ 6 plots per gradientplot size 30 m x 30 m, all trees enumeratedplots were allocated where species distribution was favorable“pure mixture stands”
Forest type 1 (n) Forest type 2 (n) Forest type 3 (n)
Transect A 38 & 46 58 & 49 35 & 38
Transect B 39 & 51 51 & 47 48 & 49
Total (n)174 205 170
Dendrometer Plots
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Inventory
Area
Area
(ha)
Sample
size (n)
Sample intensities
dbh ≥ 7 cm
Sample intensities
7 cm > dbh & h ≥ 130 cm
Sample intensities
h < 130 cm
Inventory A 357,69 90 0,79 0,07 0,03
Inventory B 352,53 90 0,80 0,07 0,03
inventory in entire 250 m buffer (N,E,S,W)
sample design: systematic sampling 200 m x 200 m grid
plot design: circular, nested area plots
Dbh class r (cm) Plot size (m²) Sample area
(Invent. A)
Sample area
(Invent. B)
dbh ≥ 7cm 1000 314.2 m2 28278 m2 28278 m2
7 cm > dbh & h ≥ 130 cm 300 28.3 m2 2547 m2 2547 m2
h < 130 cm 200 12.6 m2 1134 m2 1134 m2
Forest Inventories
r1
r2
r3
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21.08.2018Anja Gröning
Soil Sampling Sites
Litter samples Soil samples Total samples
Milova 16 48 64
Maciova 16 48 64
Total 32 96 128
sieving and drying
water content
pH(H2O) , pH(KCL)
C / N analyzer
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Module 2: Vegetation, Phytodiversity, Deadwood
data collection 76 relevés
1. transect A_Milovarelevés: May and August 2018n = 34 plots
2. transect B_Maciovarelevés : August 2018n = 42 plots
200 m2 square plots (demarcation)
some on forest inventory plots
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Deadwood Evaluation
data collection:
round plots (r=7.89 m) on inventory points
combined collection: dead Wood Manual IFN and BWI 3 (BMELV 2012, ICAS 2013)1. qualitative recording
degree of decomposition (class 1 (poor)- 5 (strong))Knife probe
2. quantitative recordingStanding and lying objectsMeasurement by means of Vertex IV, tapes, dbh-tapes
recording in standardized recording sheets according to German and Romanian manuals
Object
Height/Length [m]
d1/2/dbh[cm]
Record when
Dead, standing tree Height dbh D1/3 ≥ 10 cm
Standing Snag Height dbh D1/3 ≥ 10 cm, Height ≥ 1.3 m
Stump Height D(upper rim) D(upper rim) ≥ 5,6 cm
Lying dead tree (with root) Length d1/2 D1/2 ≥ 10 cm, length ≥ 1 m
Lying dead tree/trunk Length d1/2 D1/2 ≥ 10 cm, length ≥ 1 m
Lying branch Length d1/2 D1/2 ≥ 10 cm, length ≥ 1 m
Lying crown Length + Mean Height
d1/2 D1/2 ≥ 10 cm, length ≥ 1 m
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Intraspecific Variability of Genetic and Morphological Characteristics of Sessile Oak (Quercus petraea)
Prof. Dr. Henning Wildhagen
University of Applied Sciences and Arts Göttingen (HAWK), Germany
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genetic Differentiation & Diversity of Q. petraea s.l. along environmental gradients
leaf morphological Diversity and Differentiation of Q. petraea s.l.
distribution of the taxa Quercus dalechampii and Quercus polycarpa
accordances between genetic and morphological differentiation
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Leaf Morphological Analysis
data collection with slingshots (BigShot)
round plots (r = 100 m) on Inventory plots 5 plots/ transect between 200-600 m a.s.l)
probes of Q. petraea (and spp.) and Q. frainettoMinimal distances between trees 10 m
leaf and Cupula material from treetops (crowns)
sampling of tree dbh, heights, exposition, GPS
(4)
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(Kremer et al. 2002)Morphological Analysis32
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Definition of New Variables
LL
LW
WP
PL
Intercalaryvein
SW
LW+2
LLW
LLW+1
Sinuate lobe or lobe withsecondarylobe(s): NLSL
*
* Shape oftip of widestlobe (STWL)
1=
tru
nca
ted
2=
rou
nd
ed
3=o
btu
se
4=a
cute
5=
acu
min
ate
+ + shape of tipof lamina (STL)
1=
tru
nca
ted
2=
rou
nd
ed
3=o
btu
se
4=
acu
te
5=
acu
min
ate
6=e
mar
gin
ate
33
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Development of MATLAB®-Standalone Application34
Thank you!Stefan Hohnwald
Fakultät RessourcenmanagementFaculty of Resource ManagementBüsgenweg 1a37077 GöttingenGermany
