Author: Suzanne Peace

In a recently published study, we developed a new genomic tool, PiSy50k, for use in Scots pine evolutionary research and forestry applications.  It is anticipated the tool will be important in tree breeding applications, such as when aiming to improve the important growth traits of the species via genomic selection, as well as for many other evolutionary and breeding purposes.

PiSy50k is a genotyping array that gives information on almost 50 000 single nucleotide polymorphisms (SNPs) spread around the genome. SNPs are nucleotide positions in DNA with more than one nucleotide variant in the species. SNP arrays have the big advantages over previously existing genomic tools that individual level genotype data can be gained quickly and handling of the data does not require a lot of bioinformatics work.

Scots pine is a dominating species in the Northern Eurasian boreal forest ecosystem. It is also a major source of wood-based products. Conifer trees commonly have very large genomes, and the Scots pine is not an exception. The genome of Scots pine consists of 24 million base pairs (Gbp), which is about eight times the size of, e.g., the human genome. A large genome with a lot of repetitive regions is challenging to study. The new array is a great advance for Scots pine research, because it allows an easy and quick way of studying genome-wide variation in a large sample of trees. These genotyping results are very useful, for example, in exploring population differences and relatedness between individuals.

Generally, individuals from the same population share the same SNP variant more often than individuals from different populations. However, the Scots pine populations are genetically very similar. The similarity is caused by the species’ very wide distribution range and long-distance wind pollination that connects the populations through gene flow. Despite small genetic differences between the Scots pine populations, our new array has such a high resolution that we were able to separate tree individuals from Finland and Scotland using the genotyping results. We also used the array results to recognize parent-offspring relationships and build pedigrees in the Finnish breeding population.

You can read more about this research in the following paper:

Chedly Kastally, Alina K. Niskanen, Annika Perry, Sonja T. Kujala, Komlan Avia, Sandra Cervantes, Matti Haapanen, Robert Kesälahti, Timo A. Kumpula, Tiina M. Mattila, Dario I. Ojeda, Jaakko S. Tyrmi, Witold Wachowiak, Stephen Cavers, Katri Kärkkäinen, Outi Savolainen, Tanja Pyhäjärvi. Taming the massive genome of Scots pine with PiSy50k, a new genotyping array for conifer research. The Plant Journal. (2021) https://doi.org/10.1111/tpj.15628

Top image: Scots pine. Credit: Tanja Pyhäjärvi

Image: Scots pine trial located in the north of Sweden, comparing plants from a selected cross representing stand seed (on the left) versus plants obtained from advanced-generation seed orchards (on the right). Credit: Ulfstand Wennström, Skogforsk.

Commercial forest stands or plantations are often regenerated artificially, usually by the transfer of forest reproductive material (FRM) in forms of seeds, seedlings, or cuttings, from somewhere else. With the progression of climate change, one important practical challenge for forest managers or forest owners is to select material that will thrive now, under the present climate, and also be able to withstand predicted climate. In order to ensure good general adaptation combined with good growth, the climatic condition of the targeted plantation site (deployment site) should be similar to that of the FRM’s origin, from which they are selected.

Provenance trials are a special type of plantation experiment that helps us understand how trees are adapted to different environmental conditions. However, with advancing tree breeding programs, deployment recommendations, as well as seed transfer models, developed on provenance trials, need further testing.

Scots pine (Pinus sylvestris L.) is the most widely distributed conifer in the world and is of considerable importance as a timber producing species, particularly in the Nordic countries. Transfer effect models for growth and survival of Scots pine in Sweden and Finland were recently developed, using unimproved seedlots available in provenance and progeny trials of both countries. However, the validity of these models for the transfer of improved seeds, e.g., seed obtained from advanced generation seed orchards, remained unknown.

Results of our study, which was a collaborative project between Sweden (Skogforsk) and Finland (Luke), and built on a 40 years of Scots pine improvement activities in the north of Sweden, revealed that the recently developed transfer models can even predict performance of Scots pine elite seeds, obtained from 1.5-generation seed orchards and which have undergone an intensive genetic selection.

Additionally, based on our result, “Plantval”, which is an operational breeding database for common deployment recommendations of Scots pine in Sweden and Finland, can be further utilized for transfer of genetically improved materials.

A fuller description of this work is available in: Hayatgheibi, H., Berlin M., Haapanen M., Kärkkäinen K., and Persson T.  2020 Application of Transfer Effect Models for Predicting Growth and Survival of Genetically Selected Scots Pine Seed Sources in Sweden.  Forests, 2020, 11, 1337.  doi:10.3390/f11121337

Marchi, Castellanos-Acuña, Hamann, Wang, Ray, Menzel

Newly published research in the journal Nature Scientific Data, describes how new climate data for Europe are downscaled to high resolution and accessed by users in a useful format for further analysis.

The data repository contains the scripts and the databases required to provide scale-free (i.e. for a specified latitude and longitude) monthly seasonal and annual user specified climate data from 1901 to 2019.

The repository additionally includes gridded climate projections (CMIP5-RCP scenarios) for 30-year climate normal periods (climate baseline [1961-1990]; 2020s [2010-2039], 2050s [2040-69] and 2080s [2070-2099]) for Europe from multiple published GCM and RCM sources.

The quality of ClimateEU estimates was evaluated against weather station data for a representative subset of climate variables. Dynamic environmental lapse rate algorithms employed by the software generate scale free climate variables for specific locations made a 10 to 50% improvement in accuracy compared to other gridded data sets.

ClimateEU was initially developed by two of the authors (Hamann and Wang). Ray and Marchi wished to use ClimateEU to access past and present climate data and future projections for their work in the B4EST project.

In B4EST ClimateEU data are used with tree trait data (e.g. relating to tree height and survival) from common garden provenance  and progeny trials. 

This enables calculation of the genetic adaptation and plasticity components of forest reproductive material under varying environmental conditions. The calculations will be used to understanding the extent to which it is possible to control the tolerance of trees to climatic and biological change through tree breeding.

The database and downscaling tool produces 48 monthly variables (monthly precipitation, minimum, maximum, and average temperature), as well as 36 derived bioclimatic variables useful in ecology forestry and agriculture.

In a previous post we explained how B4EST is working to quantify the potential impacts of climate change on forests in Europe by understanding how trees with different genetic characteristics are likely to perform under different projected future climates.

To achieve this, in B4EST we use models to analyse the effect of historical mean and extreme climatology on a range of provenances and seed orchards to derive information on how the progeny may respond to the future climate in different parts of Europe. Selected progeny forming new forest stands will need to cope with climate change, e.g. milder and wetter winters and/or episodes of drought in the summer, or late spring frosts.

Our models will assess how well the genetic diversity and phenotypic plasticity of material will allow selected material to respond to climatic extremes in the future. Climatic change may also influence pest and disease threats to trees and so this factor also requires consideration when selecting forest reproductive material (FRM).

A serious drawback of many climate data portals is that they lack the temporal resolution researchers need to analyse extreme events. For this reason statistical downscaling methods and downscaling tools such as ClimateEU were developed.

Our solution has been to prepare climatic data for Europe with a monthly time series for the B4EST project. The dataset has been firstly generated combining the CRU-TS for the period 1901-current and the high emissions scenario representative concentration pathway (RCP8.5) provided by UKCP18.  This UKCP18 pathway (RCP8.5) makes no assumptions about green-house gas emissions reductions being effective; in other words it provides an extreme view of the climate future in Europe.

This dataset has been selected as candidate for our Climate Downscaling Tool, known as ‘ClimateDT’. The monthly time series of both CRU-TS and UKCP18 allows the calculation of certain projected extreme events, such as: drought, extreme high and low temperature, monthly and seasonal precipitation extremes, predictions of the beginning and end of the frost-free period. These projections are simulated, so the extreme climatology calculations should best be offered as a frequency above or below a threshold value at a decadal time scale. The results will allow the calculation of probabilities that can be used to convert the climatic vulnerability of FRM into a risk of damage or mortality. Additional climatic dataset will be then added in future.

ClimateDT provides scale-free climate data for historic climates and future projections and is currently hosted on CNR-IBBR servers. Raw monthly temperature (minimum and maximum) and total monthly precipitation for the period 1901-current are downscaled from CRU-TS while future scenarios derive form UKCP18 layers.

The downscaling is performed to user defined locations of latitude, longitude and elevation to provide many climatic variables and indices. The available time span for derived data is January 1901 to December 2098 with a monthly time step. The system is based on a website for data uploading and a virtual machine for data processing in R environment. The data transfer between the machines occurs using a Secure Shell (SSH) cryptographic network protocol and an email alert is scheduled when uploaded data has been processed.

Downscaling is performed following the method of the ClimateEU software with some modifications:

1. The local lapse rate is calculated for every year and month by month and the adjustment to be applied to the climatic variable is multiplied times the r-squared of the regression in order to avoid over-adjustments;
2. Both temperature and precipitation variables are adjusted using the dynamic lapse rate if the regression is statistically significant;
3. Additional R packages such as dismo and SPEI are currently integrated in the process to provide additional well-referenced climatic indices often used in ecology and environmental science;

The calculation engine is totally open-source and new development could be integrated easily in the script thanks to the modular structure.

Planned additional features include the use of a different interpolation method (Inverse Distance Weighted) as well as additional climatic indices or nonlinear function for dynamic lapse rate calculation.

In the current form 42 climatic indices are available for the Europe and North Africa. However, any other region of the globe is available upon request. An extension of ClimateDT on the whole globe is under development but not yet available in the B4EST portal.

An important goal of the B4EST project is to produce a tool for tree breeders to enable them to access to the genomic information that distinguish individual forest trees at a low cost.  

This would allow breeders to evaluate trees at an early stage of growth rather than having to wait many years until they have grown and they can observe a tree’s characteristics.  In turn this enables them to react promptly to new considerations like those related to global changes, such as those in our climate, which need to be accounted for when selecting tree material for forest establishment.

The genotyping tool ‘4TREES’ has been developed by B4EST for four economically important forest species: maritime pine, stone pine, ash tree and poplar. Similar arrays were also developed within B4EST for Norway spruce and Scots pine.

The 4TREES array is based on regions of the genome where a single nucleotide (one of the building blocks of DNA) varies between individuals of the same species.  These regions are called Single Nucleotide Polymorphisms or SNPs. Except for the stone pine, a pine species with extremely low levels of genetic variation, all of the SNPs used for this array come from existing genomic resources.

Having obtained hundreds of thousands SNPs per species, the first step was to use these resources for the production of a screening array, on which a set of approximately 137K SNPs per species (36K SNPs for stone pine) were tested on a representative panel of individuals of each species.

From this screening, 13,5K (6K SNPs for stone pine) highly informative SNPs were selected for each species to be on the 4TREE array. The most important criteria for these selections were to incorporate SNPs known to be in candidate genes or regions linked to traits of interest for breeders, such as resistance to dieback disease that ravages the ash tree populations. A good coverage of the genome was also important, as it has been shown to improve the predictions.

The 4TREE array will be a highly valuable genome-assisted breeding tool, made commercially available for cost effective genotyping for four important tree species in Europe.

Developing visualisation techniques to communicate to practitioners the urgency and impact of climate change on forests is a challenge. This is because the longer rotation lengths of trees compared to crops requires consideration of the projected climate change over periods of time that can be difficult to comprehend, for example 50 years into the future.

To help forest practitioners understand the anticipated future climate, we developed a Climate Matching Tool (CMT) in B4EST, with additional support from the Forestry Commission.  The CMT is a web-application that can be used on a computer or mobile device.  It aims to enable:

• forest planners to better understand the changes in future climatic conditions that new forests will experience;
• tree breeders to identify material that is better suited or matched to future climate conditions;
• forest managers to select material that is resistant or tolerant of environmental impacts e.g. extremes of frost, drought and heat; and/or biological impacts e.g. changes in the range of pests and pathogens due to climate change that could impact on future managed forests;
• the range of products from well-managed resilient forests to increase in society and help mitigate climate change.

These issues are particularly important considerations for forest decision makers given the need to make European forests more resilient to the direct and indirect impacts of environmental change and the focus in policy on forest expansion.

The CMT includes two modes of operation, a basic mode for quick analyses (Figure1), and an advanced mode for more complex or detailed enquiries (Figure 2). Both the basic and advanced modes allow the user to:

• locate and assess where the projected climate of a selected location currently exists (find an analogue of the future climate) or,
• find and locate in time and space where the climate of selected location will be in the future.

In Europe there is a rich legacy of introduced tree species from the Pacific North West, the first version of the CMT therefore focuses on these two areas. 

The CMT uses a method of matching published in 2005 (Broadmeadow et. al. 2005). It is based on three climatic variables:

• mean monthly temperature;
• mean monthly diurnal temperature range;
• and monthly precipitation.

The data used in the CMT is from the monthly time series of UKCP18 (RCP8.5) that is available at 12 km resolution for Europe, and 60 km globally. This is a high emissions scenario the earth is currently tracking, and makes no assumptions about emissions reductions being effective.  We have downscaled the global data for the Pacific North West to 12 km – the same resolution as Europe.

Basic mode climate matching can be based on 10-year or 30-year climate normal periods from 2001 to 2079, whereas the advanced mode offers 10-year or 30-year climate periods from 1981-2079. Both operating modes allow the selection of precipitation, temperature or both variables on which to match. In advanced mode, climate data for the months or seasons to use in a match can be selected, as may the number of pixels to match. The advanced mode allows the user to switch between Europe and the Pacific North West or both regions to calculate a match, if both regions are selected 10,000 pixels are available to be matched.

A results box is presented following a calculation. The results show the distribution of the ‘best match’ units for the number of pixels selected, and the position of the selected location in the distribution. The result colour rendering can also be switched. Help instructions are available and ‘tool tips’ are shown for each selectable drop-down box.

Broadmeadow, M.S.J., Ray, D., Samuel, C.J.A., 2005. Climate change and the future for broadleaved tree species in Britain. Forestry 78, 145–161. doi:10.1093/forestry/cpi014

The genetic selection of trees (breeding) and associated production of improved forest reproductive material (FRM) have a long history in many European countries. Looking forward, the tree breeding sector plays a strategic role in ensuring the resilience of forests under climate change, but its evolution is difficult to predict as it results from the complex interactions of technology, institutional actors, regulatory environment, market developments and the research and development sector.

An objective of B4EST is to understand this complexity in order to develop solutions and recommendations that fit the broader context within which tree breeding operates. As the organisation of breeding and production and diffusion of FRM is organised quite differently depending on the species and countries, the analysis is based on four cases capturing this diversity (Maritime Pine, Norway Spruce, Eucalyptus and Poplar).

The project will investigate the economic potential of the genomic revolution, which has transformed selection in many sectors (e.g., animal production) but, to date, has had little impact in the forest sector. Genomic selection has the potential to accelerate the breeding process and improve the assessment of genetic value, but it also involves significant costs and requires new knowledge. Inevitably, the relative newness of the technology also introduces uncertainty, and the specificities of tree biology – for instance the length of the production cycle or time to sexual maturity – introduce constraints that are not present in other natural resource sectors.

The project will compare the relative efficiency of genomic-based and traditional breeding methods for three species (Maritime pine, Norway spruce and Populus sp.). This part will be based on the collaboration between tree geneticists familiar with the comparison of different methodologies for the selection of trees and economists familiar with cost-benefit analysis. The work will complement the analyses that have already been made in the literature by better taking into account the costs of the different basic operations related to tree breeding and the possible substitutions among those costs when modifying the structure of the breeding programs. The results will inform private and public breeders about when and why one type of selection is economically preferable to another, and under which conditions genomic selection is likely to be broadly adopted by the forest sector in the future.

B4EST will also investigate the broader innovation systems to which tree breeding activities belong, with the aim of anticipating the evolution of the sector and how that evolution may be influenced, for instance through the use of public policies or regulatory changes. The task will analyse the organisational structure of the breeding sector by mapping the main public and private organisations involved and their relationships, defining the key aspects of the regulatory framework and identifying the salient market forces shaping the sector. Drawing on case studies of three species (Maritine Pine, Eucalyptus, Norway Spruce) and five countries (France, Spain, Portugal, Finland, Sweden), the project will highlight the main drivers of change in tree breeding in Europe. Stay tuned!

More information:

Institut National de la Recherche Agronomique (INRA), Grenoble Applied Economics Lab (GAEL): Stéphane Lemarié, Aline Fugeray-Scarbel, Eve Audoin

Natural Resources Institute Finland (Luke), Bioeconomy and Environment Research Unit: Xavier Irz

Eucalyptus globulus and hybrids are important non-native tree species with a high production capacity. They also have a potential to grow on marginal lands and thereby provide an income for depressed rural areas in Southern Europe.

B4EST aims to identify and develop eucalypt trees well adapted to growing in rapidly changing environments, and those more tolerant to biological and climate threats. To do this it is first necessary to evaluate the amount of genetic variation amongst eucalypt trees for tolerance to cold (freezing temperatures) and to water stress. Tolerance to snout beetle (Gonipterus platensis), today’s eucalyptus pest with highest impact in Iberian Peninsula, is also being evaluated.

Trees’ growth and survival performances in these conditions will be assessed in a variety of circumstances:

1. in a field trial with contrasted water availability,
2. in controlled conditions for drought and cold tolerance,
3. in breeding trials with contrasted drought and/or cold exposure, and
4. in fields and laboratory for resistance to Gonipterus.

B4EST field trial with contrasted water availability

A field trial was established in June 2018 in central Portugal with thirty clones (genetically identical individuals) of Eucalyptus globulus and Eucalyptus spp.  Hybrids are distributed in three blocks: 1) without irrigation, 2) with irrigation and additional fertilization, 3) without irrigation and on a soil with seasonal waterlogging.

The trial has a weather station on site to monitor daily climate variables (Figure 1a).  It also has sensors monitoring the soil water and the amount of water supplied by irrigation (Figure 1b).   The trees are also equipped with sensors to monitor daily micro-fluctuations in the trunks. This monitoring will provide measures of the extent to which the trees are adjusting in the short-term to site conditions, and of specific responses to drought by trees.

So far, the heights of the Eucalyptus clones have been assessed ten times. The results show that faster growth occurred as expected in the irrigated block and that clones which already have adult leaves show a faster early growth.

Assessing drought and cold resistance in controlled conditions

An experiment to test the tolerance of trees to cold (freezing) temperatures by 15 clones, including forest reproductive material being used in operational forestry, has been carried out and the damage to the foliage and survival rates of the trees are being evaluated from one week to one month after the stress. 

Two eucalyptus clones with contrasting tolerance to drought were selected and used in a pilot study of drought tolerance carried out in the Toulouse Plant Microbe Phenotyping Platform in Toulouse (Figure 2). The results are being analysed to set up a larger experiment in the near future using the same trees as was used in the assessment of tolerance to cold.

Selecting clones with drought and cold resilience from field trials for further analysis

The breeding trials more exposed to cold and/or to drought were identified and split into four classes: 1) trials exposed to cold, 2) trials exposed to drought; 3) trials exposed to cold and drought; 4) trials less exposed to cold and drought.

An analysis was carried out ranking existing eucalyptus clones for growth and survival.  This allowed the identification of 163 eucalyptus clones as a training population for genomic selection (a form of genetic selection using DNA markers). Leaves and wood samples from 160 clones have already been collected and have been sent for DNA extraction and micro-densitometry, respectively.

Assessing the susceptibility of eucalyptus to eucalyptus snout beetle

Laboratory tests are being carried out to assess the susceptibility of different eucalyptus trees to eucalyptus snout beetle (Gonipterus platensis). Twelve different trees of eucalyptus have already been tested – with the extent of foliage consumption by the Eucalyptus snout beetle and amount of egg laying in the leaves being evaluated. Field assessments have also been undertaken evaluating eucalyptus snout beetle attacks in eucalyptus breeding trials. Field work is still under way to evaluate the susceptibility to this beetle of more eucalyptus trees.

This work is carried out within the B4EST project as a collaboration between Altri Florestal (Portugal), CIRAD (France) and UPS (France) using the genetic material and field trials of Altri.

Climatic data is an important component of ecological models.  In B4EST we want to quantify the potential impacts of climate change on forests in Europe by understanding how different tree phenotypes (trees of different characteristics) are likely to perform under different projected future climates. To do this we will be reanalysing existing European growth trials of tree provenance (geographic seed origin) and progeny (genetic/family origin) using data on historic and projected future climate.  

As part of our work we will need to deal with biases in climate projections as both global and regional climate models include some interpolation errors which need be known and understood.  In B4EST, we will be quantifying these errors, or discrepancies, between different data sources so we can better understand the uncertainties in our ecological models.

A particular challenge to our task is that whilst a wide range of climatic data is now available from web portals, time series data (data indexed in time order) are rarely available at high resolution (5 km or less).

Frequently used time-series databases include:

• the Climate Research Unit (CRS) data TS.4.02 from the University of East Anglia, UK,
• the Copernicus climate data portal (EU), and
• the UKCP18 data from the Centre for Environmental Data Analysis (CEDA).

The data provided at these portals are too coarse for site sensitive scientific studies, such as those used in B4EST. For this reason, many groups have developed different downscaling methods, which increase the resolution of the data enabling it to be used at smaller spatial scales.

Climate data software ClimateWNA and the ClimateEU (https://sites.ualberta.ca/~ahamann/data.html) provide an interface in the form of a standalone MS Windows application, enabling users to extract and downscale PRISM 1961-1990 monthly normal climate data (1.25 x 1.25 arcmin, 4 km approximately) to scale-free point data based on latitude, longitude and elevation.

The climatic time series data availability for ClimateEU is unfortunately not suitable for the requirements of B4EST. In order to overcome this time limitation, as well as additionally replicating and controlling the downscaling process, B4EST (Work Package One) has followed the main steps developed by the ClimateEU team, to migrate the ClimateEU method into R programming language (for more detail, see the explanation below).

The testing procedure is currently under development. Once the statistical process has been defined, CRU time series, UKCP18 surfaces and other climatic surfaces with historical data and future projections will be made freely available to enable scientists to develop a range of climate data for ecological and genetic studies in a user-friendly R-shiny web app (https://shiny.rstudio.com/).  In this way scientists working in B4EST will be able to access reliable climatic time series data to characterize the historic and future climate of their provenance trials and experiments.

Footnote

Migrating the ClimateEU method into R programming language – the process

Using Worldclim ver 1.4 as a test climatic surface (representing the 1961-1990 climatic normal period) with 30 arc-second of spatial resolution and other data obtained from meteorological stations across Europe, a scale-free bi-linear interpolation process with a dynamic local lapse rate adjustment has been completed. Other spatial interpolation techniques such as inverse distance weighting have been tested to increase the range of interpolation methods with which to compare results.

Major difficulties face those seeking to produce improved Maritime pine seed.  These include invasive pests and climate change which are leading to a decrease in seed production.  Another challenge is pollen contamination from surrounding non-improved stands which is having a negative impact on the genetic value of the produced seeds. In the B4EST project, we are testing on a small scale, whether the use of a greenhouse seed orchard can improve the quality and quantity of seed production.

Tree breeding programmes aim to produce improved plant material to use for afforestation. The best performing trees for climate adaptation, productivity or resistance to pest or pathogens, are selected and planted or grafted in a seed orchard managed to produce seeds. These seeds from selected parents are then used to produce seedlings to be planted in the forest. In this way the genetic gain from the breeding program is deployed in the forest so as to better provide wood and forest services.

Maritime pine is the first planted species in France: more than 50% of commercialized seedlings each year are maritime pine seedlings, all issued from improved material. The breeding program has led to several generations of open pollinated and extensively managed seed orchards that are used to produce enough seedlings for 15, 000 to 20, 000 hectares of plantation each year. However, the conditions of seed production are changing. Invasive pests (Leptoglossus occidentalis) perhaps combined with climate change (summer drought) are drastically decreasing seed yields.  Already this is forcing orchard managers to harvest former generation seed orchards to reconstitute their stocks.  Moreover, pollen contamination (estimated to be over 90% in some cases – Bouffier et al, 2017) induces heavy losses in seed genetic value.

A greenhouse seed orchard would allow for a more intensive management of seed production, with a good control of environmental conditions. For the first time in the Maritime pine breeding programme, we tested mass pollination under greenhouse conditions.  We want to evaluate the technical feasibility of this method, the potential of seed yield per cone and the pollen contamination rate. Twenty grafted trees in 100 litre pots were placed in a greenhouse during flowering time and mass pollinated, then transferred outside for cone maturation. In the absence of an authorised efficient chemical control, bags were placed on young cones for protection against seed bugs. After harvest, the seeds will be analysed for their molecular marker (sections of DNA) profiles to check their pedigrees.

This part of B4EST research is being led by INRA, France.

Reference

Bouffier L., Debille S., Alazard P., Pastuszka P., Raffin A., Harvengt L., Lelu-Walter M.-A., Musch B., Trontin J.-F. (2017). Pollen contamination and mating structure in maritime pine clonal seed orchards. IUFRO Seed Orchard Conference 2017 (2.09.01 Unit), 04-06 sept. 2017, Bålsta (Suède). Poster, pp. 65.

Dr Tanja Pyhäjärvi , University of Oulu, Finland

All breeding is based on genetic differences among individuals. In traditional breeding, the similarity of genetic background and the relatedness among individuals is predicted from pedigrees. Pedigrees have been an important part of breeding for a long time. All of us have that friend who has a pure bred dog with a fancy pedigree on some database or on paper. Mom, dad, mom’s mom, mom’s dad etc., you know it.

In modern breeding, the exact pedigree information is not always needed. Why? Because we can use information from DNA level variation to figure out relatedness among individuals. On top of that, it is also possible to follow which of the thousands of variable sites in a genome are linked to a certain desired or unwanted characteristics. DNA level information also tells us directly about the amount of genetic diversity and extent of inbreeding within a given individual.

Many breeding methods that use DNA level information have one problem. They need a lot of it! Often information is needed from tens of thousands of DNA locations and from thousands of individuals. So, what do you do when you want to peek into DNA level differences among thousands of trees? One of the goals of B4EST project is to develop new high throughput methods for easier and cheaper genotyping to tackle this issue.

Luckily there are multiple technological options to achieve this goal. In B4EST, we have chosen a method called genotyping arrays. These postcard size arrays have 384 wells, and each well contains 50 000 different type of small DNA pieces that are designed to capture a different variable site in a tree’s genome. Short, sticky custom designed pieces of DNA are attached to a glass surface and form microscopic clusters. Then the sample genome’s DNA is fragmented in small pieces and allowed to find a matching sticky piece on the surface. And that’s not all: samples are also labeled with fluorescent dyes. With a special light conditions and a very good camera, an image of the array can be obtained.

Just add some image processing and algorithms and voilá!, different genotypes (heterozygotes and homozygotes) can be identified. And that is how you can genotype hundreds of trees and tens of thousands genetic markers in one go.

Of course making all this to work smoothly requires a lot of existing genetic and technical knowledge. The arragys that we intend to design in B4EST project will be publicly available for anyone who needs efficient genotyping of Norway spruce, Scots pine, poplars, maritime pine, ash and stone pine.

The use of genomic information for tree selection has the potential to identify more accurately individual trees carrying favorable genes and at earlier stages of their development than we had ever done before.  This innovation, known as genomic selection, is possible as soon as trees are big enough to provide the small amount of tissue (for example, a leave or a needle) that is required for DNA analyses. Young plants can be selected for their future performance or by their resistance or tolerance to environmental stresses at the nursery, well before their evident characteristics (definite phenotypes) are available in the field.  It is thought the technique will be revolutionary for forest trees. 

The most revolutionary benefit comes from the shortening of the generally long time required to assess phenotypically a candidate to selection, usually 10 to 15 years in the best of the cases and dependent on the species. Making things faster is not just a question of return of investment; early evaluation brings promptness and flexibility when facing challenging demands, especially those associated to the suitability of existing varieties to global change. One good example in B4EST is that of selecting Ash trees that are tolerant to a rapidly expanding wave of a destructive fungus.

B4EST has made a strategic choice of the most economically, ecologically and socially important tree species in Europe for innovative breeding, covering a wide range of current and potential habitats, industrial uses and societal values, with three broadleaf (hardwood) and four conifer (softwood) species. Some are the object of advanced breeding programs (Norway spruce, Scots pine, maritime pine and poplar), and are therefore the ideal candidates to adopt further breeding innovations. Some represent highly productive non-native species of great potential in Europe (Eucalyptus), while others species like Stone pines provide sustainable high value non-wood products or are highly threatened like Ash. These species can also be considered as pioneer models for all 48 forest tree species listed in EU directives regulating forest reproductive material trade.

Genomic selection requires screening genomes with thousands of genetic markers.  Such tools are typically expensive to develop for a single partner, or for a single species, unless high demands can be guaranteed over a long period. B4EST represents the ideal framework to gather partners and species to a level of demand that opens up the door to extremely competitive rates, within the consortium and for external partners.

B4EST is developing three genotyping arrays, highly miniaturized devises able to screen up to 50,000 markers each, for six of the species lacking such resources (Norway spruce, Scots pine, maritime pine, stone pine, ash and poplar). They will be available in September 2019.  It will also develop genomic selection at an operational level on all the above species (including Eucalyptus), in different contexts and for different objectives. By doing so, new statistical tools able to handle massive data will be tested over relevant collections of more than 2,000 individuals per species, paving the way for further implementations in other species in the future.

This article explains research that will take place in B4EST Work Package 3 which is led by Dr Leopoldo Sanchez Rodriguez, INRA (Orleans).

The Ålbrunna Norway spruce seed orchard. Currently the main provider of improved plant material in central Sweden. One of many seed sources that need to be characterized in detail in the B4EST project. Picture courtesy of  Almqvist, Skogforsk

The selection of plant materials, for example seedlings and saplings, is one of the most important decisions that a forest manager will make when establishing or regenerating a forest. The decisions made will have a fundamental influence on the performance of the trees and will have consequences over the entire life of a forest stand. If chosen correctly, used at suitable sites and managed properly, genetically improved plants combine higher rates of growth, improved stem and wood quality and will be better adapted to cope as our climate changes.

In B4EST we are combining new scientific advances in forest genetics, silviculture, molecular biology and climate research with the needs of forest owners and other stakeholders to develop guidelines and decision support tools to enable greater and more informed use of genetically improved plants in forestry. An important feature of our work is that the information and tools we create will be applicable to contemporary genetically improved plant material like seed orchard crops and clones or clonal mixes.

To develop the guidelines and tools we will make use of mathematical models developed in B4EST which describe how trees of different genetic make-up react to climatic conditions (like drought, heat, precipitation and frost) as well as damaging biological agents such as insects and fungi. The models will be used to predict the performance of genetically improved plant material in future climatic conditions to understand ‘deployment zones’.

To inform the development of our models and tools, we will also be working with for example, forest owners and forest owner associations, forest managers, larger forest companies to explore and create a generic definition of tree ‘performance’. We will explore for example if it should include tree growth and survival, increased stem and wood quality, as well as the ability to avoid damages from climatic events (like frost or drought) and attacks from biological agents such as insects and fungi.

As we discussed earlier, the performance of a forest stand results from the combination of the plant material used and the way in which it is managed once planted. A major aim of the project will be to improve existing models which estimate tree growth and stand dynamics (how the trees as a group or a stand develop over time given for example, silvicultural actions and the properties of a site) to take account of the genetic gains that may be achieved from using genetically improved plant material and other important factors identified by B4EST, like group performance, genetic diversity and adaptive properties. These will be used to simulate different silvicultural practices at both forest stand and landscape scales and enable us to compare, evaluate and recommend alternative ways of managing forest stands as well as optimizing the genetic gain.

Finally, we will also develop a framework for providing deployment recommendations for genetically improved plants that are based on biological constraints rather than administrative ones. In other words the information will be categorised over regions with similar biological backgrounds and climatic conditions and a common characterization of plant material. In this way we hope to facilitate cross-border collaboration in the use of plant materials. We will also be identifying and discussing with relevant stakeholders any national and EU rules that may restrain or prevent the use of new clones and varieties.

This article explains the research that will take place in B4EST Work Package 4 which is led by Mats Berlin, Skogforsk, Sweden.

Although in tree biology we often study single characteristics or ‘traits’, we know that in reality natural selection has acted on all traits at the same time. The tree we see in the forest is the outcome of maximising different essential functions of the tree under pressure from the living and physical environment. Tree breeders have typically focussed on improving some traits, like growth rate or productivity, at the expense of other potentially valuable characteristics. But in a changing world, where future environments are uncertain, we need to find ways to make forests resilient, and making more use of a wider range of the traits of a tree is key.

In the B4EST project, we will use specially selected populations of trees to study how different traits, like growth rate, pest and disease resistance, drought tolerance and seed production, work together. For example, a tree that invests a lot of resources in disease resistance might not grow as tall as others; in this case we would try to find good combinations of these characteristics that mean we have productive trees that are also able to tolerate disease.

We will study how these interactions change when the trees are planted in different environments and how they work in different species. We will try to find the genes that control these combinations of traits and use the knowledge we gain to find ways to breed trees that are diverse, resilient and productive at the same time. In this way, we hope to help secure our forests against the uncertainty of environmental change.

The work described above is being completed within B4EST Work Package 2 which is led by Ricardo Alia from CIFOR-INIA.

B4EST began in May 2018, and is a Horizon2020 EU funded project led by Dr Catherine Bastien from INRA in Orleans, France. The project aims to enhance the role of forests in Europe in providing for biodiversity, carbon storage, and the provision of renewable raw materials for the expanding bio-based economy, under climate change. B4EST will also identify new opportunities for wood and non-wood (e.g. nuts, seeds and fruits) products based on forest-based raw materials that could also stimulate the European bio-economy.

The bio-based economy currently utilises 50-60% of the harvested wood from 161 million hectares of forest (38% of land area) in Europe. This forest industry and the European industries dependent on wood products represents 7% of the EU manufacturing GDP which together support 3.5 million jobs. Rapid climate change is a current cause of uncertainty in the sector and numerous studies predict impacts to European forest production that would have a knock-on impact on the industrial sector and jobs.

The project is using eight study species to demonstrate novel techniques in the identification, assessment, and deployment of environmentally and genetically tested and improved trees that will be better suited to a future climate. The B4EST project study species are: Scots pine, Norway spruce, maritime pine, stone pine, Douglas-fir, eucalyptus, ash and poplar. All these species have high importance from an economic, ecological, and/or social perspective.

An important part of the project will be to use trials in Europe of trees of selected provenance (region of tree or seed origin) and/or progeny (descendancy) to identify and assess the combined resistance of trees to climatic and biological stresses, for example drought, heat, frost, pests and diseases. We’ll also be assessing how trees of selected progeny are better able to adapt to these stresses.

How will we do this? We’ll be studying existing genetic trials in what are known as ‘common garden experiments’. These are trials designed to test a range of genotypes – trees with the same genetic makeup. These could be trees of different provenances that have undergone natural selection, or trees of known progeny. In some cases the trials also test selected clonal material.

Common garden experiments test all the selected trees across a range of environmental conditions. This enables us to measure important characteristics (‘traits’ such as height growth, timber straightness, disease resistance) to understand the different ways a genotype performs and responds to a range of environmental conditions (‘phenotypic’ variation).

We aim to model several important responses simultaneously, combining measures of characteristics such as tree height, tree form, wood quality, disease/pest resistance and consider trade-offs between characteristics. The models will be used to predict how genotypes will perform in parts of Europe as our climate changes, and under pressures of certain pests and diseases. The genetic variation of tree material will be studied to understand and predict to what extent it is possible to control the tolerance of trees to climatic and biological disturbances through tree breeding. This will help define the suitable regions for future deployment of tree breeding material under climate change.

The research described will be undertaken by B4EST’s Work Package 1 which is led by Forest Research in the UK, and involves fourteen partners: INRA (France), INIA (Spain), Skogforsk (Sweden), LUKE (Finland), EFI (Europe), SLU (Sweden), NERC-CEH (UK), WR (Netherlands), CIRAD (France), CREA (Italy), NIBIO (Norway ), UPS (France), ALTRI (Portugal).