1. Darius D. and consortium
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No

2. General info
Start date:1st March 2016
Duration: 48 months
Budget: 7.9 million Euro (with a 6.7 Million Euro grant from the European Union)
Project officer: Silvia Gemini, Research Executive Agency (REA)
Coordinator: Dr. Bruno Fady, INRA, France

3. Overall concept and expected impact
Provide the European forestry sector with better knowledge and new tools for efficient management and sustainable use of FGR in the context of environmental change and evolving societal demands.
expand the current scientific knowledge on how genetic diversity, phenotypic trait diversity and environmental diversity co-vary over multiple spatial scales,
inform on the genetic basis of phenotypic trait variability and plasticity,
characterise in-situ and ex-situ conservation units and underused natural resources, and
produce models of future species distribution usable for FGR management under diverse policy and environmental scenarios.

4. Partners
Institut national de la recherche agronomique (INRA), France Agencia Estatal Consejo Superior de Investigaciones Científicas (CSIC), Spain Uppsala Universitet (UU), Sweden Aristotle University of Thessaloniki (AUTH), Greece European Forest Institute (EFI), Finland Bioversity International, Italy Philipps-Universität Marburg (PUM), Germany Consiglio Nazionale delle Ricerche (CNR), Italy Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Spain University of Oulu (UOULU), Finland IGA Technology Services (IGATS), Italy
Norwegian Institute for Bioeconomy Research (NIBIO), Norway Forestry Research Institute of Sweden (Skogforsk), Sweden Johann Heinrich von Thünen Institute (THÜNEN), Germany Bavarian Office for Forest Seeding and Planting (ASP), Germany The Natural Environment Research Council (NERC), Great Britain Aleksandras Stulginskis University (ASU), Lithuania INRA Transfert (IT), France Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), Switzerland Russian Academy of Sciences (RAS), Russia Radiata Pine Breeding Co Ltd (RPBC), New Zealand LIECO Gmbh & Co KGH (LIECO), Austria

5. Ex-situ collections in Europe
Investigated species
Tree species
Distribution
Major threats to FGR
Ex-situ collections in Europe
Nb in-situ DCUs
Abies alba
Alp, Con
Climate change, habitat loss
AT, DE, FR, GR
318
Betula pendula
Atl, Bor, Con
Habitat loss, grazing
FI, GB, LT, NO, SE 50
Fagus sylvatica
Atl, Alp, Con, Med
Climate change
DE, ES, FR, IT, GB, SE
469
Picea abies
Alp, Bor, Con
Climate change, pests
AT, DE, FI, FR, IT, LT, NO, SE
471
Pinus cembra
Alp
Fragmentation, habitat loss AT 56
Pinus halepensis
Med
Forest fire
ES, FR, IT, GR 26
Pinus nigra
Alp, Con, Med
Habitat loss, hybridization
DE, ES, FR, GR
145
Pinus pinaster
Atl, Med
Forest fire, pests 42
Pinus sylvestris
Alp, Bor, Con, Med
DE, ES, FI, FR, LT, NO, SE
313
Populus nigra
DE, ES, FR, IT 30
Quercus petraea
Atl, Con
Pests, hybridization
AT, DE, FR, NO
250
Taxus baccata
Alp, Atl, Con, Med
ES, IT

6. In-situ and ex-situ collections

7. Work packages Network of permanent plots Phenotyping protocols
Extension
Genotyping protocols

8. WP3 WP6 WP1 WP4 WP2 WP5 EU FGR database for conservation and use
Papers and conferences for the scientific community
Species / FGR distribution models
WP3
Physiologically relevant phenotypes
Current and potential strengths and weaknesses in the EU FGR conservation network
Website and online resources
Knowledge, models and tools on local adaptation
GenTree nested experimental sampling strategy
European wide network of permanent plots
WP6
WP1
Tools for sustainable FGR breeding and conservation
Workshops for stakeholders
WP4
Next Generation genotypes
Simple phenotyping and genotyping in collections and in-situ
WP2
GenTree stakeholder platform
Training courses
Links between work packages
FGR-friendly policy options
New gene and phenotype sampling strategies for conservation and breeding
WP5
Good Practices material
Economic effects of FGR-friendly regulations
Models for FGR-friendly sylviculture
FGR monitoring scheme

9. WP1 Innovative conservation strategies
Task1.1 Improving the European in-situ conservation network and ex-situ collections (using provenance data).
Task 1.2 Characterization of in-situ collections for 12 species x 1000 trees (genotyping based on a few relevant SNPs across species)
Task1.3. Bioversity. Identification of gaps in in situ conservation and of original resources for breeding
Task 1.4. ASP Test of strategies and methods for monitoring: which markers, traits, environmental factors (on 2 to 4 model species)

10. WP2 Innovative breeding strategies
Task2.1 Inventory & validation of advanced high throughput phenotyping (NIR etc.)
Task 2.2 Design and implementation of allele and genotype sampling strategies to enrich breeding programs
Task 2.3 Development of cost- and time-effective breeding strategies and methods
Task 2.4 Improvement of genetic diversity management in intensive advanced breeding programs to prevent the decrease of genetic diversity in FRM

11. WP4 - Quantification of key demographic and selection processes affecting genomic diversity
• Create a next-generation genetic diversity map for 7 key European forest tree species, identifying life-history traits and population features associated with high levels of genetic diversity.
• Associate genetic variation to environmental variables.
• Infer past population transfers and demography.
• Use a multi-scale (from local to species-wide) sampling scheme to infer the spatial scale of gene flow and local adaptation and identify alleles under local adaptation.

12. Sampling protocol
The sampling protocol provides step-by-step detailed requirements of traits to measure, data to record about the sites and individual trees, wood and leaf material to sample and describes how to prepare the material for DNA sequencing.
Photo: Sampling in Finland. Credit: Tanya Pyhäjärvi

14. Sampling of trees

15. Sampling of trees
A total of 12 species are sampled in the project. For each species, trees are sampled in 10 sites. In each site a pair of populations is selected for sampling, within the same type of landscape. The two populations in a pair consist of 25 trees each, and are located at two opposite ends of a the specific ecological gradient which is being investigated. Countries where the sampling is ongoing are: Austria, Finland, France, Germany, Greece, Italy, Lithuania, Norway, Russia, Spain, Sweden, Switzerland, United Kingdom.
Photo: Populus nigra in Rio Alberche (Spain). Credit: Barbara Carvalho/CSIC

16. ASU
Task 1.4 Genetic monitoring
Task 2.1 High throughput phenotyping
Task 1.1 Provenance modeling
Task 3.3. Seeding experiment

17. Phenology scoring in a Scots pine seed orchard
The on-land scoring scale female
Phenology scoring in a Scots pine seed orchard
8 m
16 m
24 m
In spring 2017 at 4 occasions, we scored on-land phenology of vegetative, male and female buds of 25 clones
At each scoring, we flew a drone at 3 altitudinal modes to photograph these clones at variable resolution
Image analysis on going
Which areal mode gave the closest scores to the on land measurements?

18. M&M phenology scores and seed color variation between the clones by “targeted RGB method”
Identifying RGB range for current year cones/shoots/strobili at the pixel level
Calculating % of pixels within this RGB interval from the toal number of pixels outlined by freehand tool in a crown
Problems: error due to similar colors of twigs, then perhaps small squares of equal size can be used instead of the freehand tool
Image taken at 4 m above the crowns

19. Cone yield in mature stands of Scots pine
Important for forecasting seed crops on tall trees, seed trees for natural regeneration
Counting cones manually on an image segment on the crown section with the greatest cone yield
In spring 2017, we flew drone over mature stands at variable H, resolution
Testing image analysis: counting an 1, 2, 3 squares
Image at 10 m above the crowns

20. Identification of population of Scots pine based on airplain fitted NIR spectroscopy
Equipment: Bekas Ch-32 ultralight aircraft, cruising speed 100 km/hour, altitude 700 m and RIKOLA hyperspectral, operating in the spectral range from 500 to 900 nm, spectral interval 1 nm, connection with RGB/CIR camera for improved object identification (NIKON D800E, RGB/NIR configuration, 28 or 50 mm) lenses.
ASU, Lithuania
Problem & aims
IS NIR able to identify different populations of Scots pine ?
Can NIR be used as a fast estimate for genetic diversity?
Aim was to run spectroscopy analysis on NIR areal images of provenance trial of distant populations of Scots pine
The objective of the study was to investigate capability of the aerial NIR imaging for separation of Scots pine populations based on the spectral image analysis.
Pixelmaped crows of a tree from Bashkiria provenance (left) and Kostroma (right) at 0.7 km altitude (ca. 25 pixels per tree)

22. http://www.gentree-h2020.eu/ @GentreeProject
More information
@GentreeProject