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Phenotyping the forest-concepts and progressHeidi Dungey, Dave Pont, Jonathan Dash, Mike Watt, Toby Stovold, Emily Telfer
A national aim to double forest productivity
Static forest area
Phenotyping the forest
• What is it?• What can we do?
• What have we done?• Next steps
What is Phenotyping ?
“the goal is to automate the acquisition of plant images, and other
physical data that can be used to quantify genotype by phenotype by
environment interactions”(Fahlgren et al. 2015)
“extracting biologically meaningful signal from environmental and
experimental noise” (Cobb et al. 2013)
Controlled environment
High throughputNon-destructiveImage analysis
Slide: Dave Pont
Phenotyping trees – in the forest
?
Cambium 1° Xylem
Who’s your Daddy?
Pedigree Reconstruction1. Progeny Trial 2. Parental Seed Orchard Trees
1 2 3 4 5 6
3. Select Best Progeny
6. Selected Breeding with 4. DNA Fingerprint generated
4. DNA Fingerprint generated
7. Improved Progeny
4 5
875 -345
850-055
$
What can we do?
• We can use the platform to identify outstanding families, trees and clones
• Adding coupe-level genetic information will allow us to models for optimal deployment
GF+ 26
Clone x
GF+ 19
Nursery picture
Where are we up to?
LAI and forest health/nutrition
Tree detection over the trial
Single tree detection and crown metrics
Correlations
Size Form Wood quality Disease
Height H
DiameterDBH
Volume V
StraightnessS
Branchiness B
MalformationM
Stiffness A
Basic density ρ
Dothistroma D38
TH 0.90 0.77 0.82 0.26 0.17 0.20 0.15 0.00 -0.49 CVF 0.72 0.82 0.84 0.22 0.15 0.16 -0.02 -0.01 -0.50 SHV 0.18 -0.16 -0.09 0.04 0.02 0.06 0.22 0.02 0.08 RU 0.01 -0.14 -0.10 0.05 0.06 0.07 0.12 0.08 0.15
Pearson's r >=0.75 >=0.50 >=0.25
Strong correlations:
H r=0.90
DBH r=0.82
V r=0.84
Small changes in heritabilities were found from LiDAR-derived height and DBH
0.3
0.2
0.1
0.0
Height DBH
AFTER
BEFORE
GROUND
Ongoing Work
• Database for phenotyping platform
• Statistics and method adaptation
• Spectral: colour and multispectral cameras
• UAV as a test bed to evaluate other sensors
• UAV as a tool to carry out evaluations of trials, plots and stands (including wood properties)
• Methods to phenotype lower stem and branching
Heatmap of DBH and terrain map of trial area
Remote sensing and phenotyping are core elements of a multi-year forestry research programme
Scion (2014). Growing Confidence in Forestry's Future: Research Programme 2013-2019. Rotorua, New Zealand: Forest Research Institute Ltd
Where we up to
GCFF Research Programme
https://gcff.nz/the‐programme/
Where we up to
2015First estimate of utility of phenotyping platform for genetics trials And tree breeding
Where we up to
2016‐2018Utility of phenotyping platform to assess trees that have better disease resistance has been examined and unique individuals selected
Where we up to
2016‐2018Phenotyping platform has been used to better understand the drivers of growth and quality in radiata pine
Outputs
International conferences• Pont, D., H., Watt, M.S., Morgenroth, J., & Dungey, H. S. (2016) Correlating tree size and quality with crown metrics from airborne
laser scanning. WoodQC, Quebec, Canada, 12-17 June.• Pont, D. (2016). The use of LiDAR for Phenotyping Trees. Keynote paper at the international conference: Forest Genetics for
Productivity, the next generation, Rotorua, New Zealand, 14-18 March.• Telfer, E J, Pont, D, Dash, J, Dungey, H S, & Moore, J R. (2015) Whole forest modelling: Reconstructing the past, present and
future performance of trees with big data. Paper presented at Queenstown Research Week, QMB Computational Genomics Satellite meeting. Queenstown, New Zealand, 3-4 September.
• Pont, D., Morgenroth, J., & Watt, M.S. (2013). Tree-based analysis of ALS to estimate tree size and quality. In, MeMoWood -Measurement Methods and Modelling Approaches for Predicting Desirable Future Wood Properties. Nancy, France, 1-4 October.
• Dungey, H., S, Pont, D., Li, Y., Wilcox, P., L, Telfer, E., J, Watt, M., S, Jefferson, P., A. (2013). Novel remote sensing phenotyping platform and genomic selection will boost the delivery of genetic gain of radiata pine in New Zealand. In, Forest Genetics 2013.Whistler, British Columbia, Canada, 22-25 July.
National conferences• Pont, D., Dungey, H., Watt, M.S., & Morgenroth, J. (2016). Phenotyping the forest: concepts and progress. Presented at the: Third
annual GCFF conference. Auckland, New Zealand, 12-13 May.• Pont, D., Watt, M.S., Morgenroth, J., Dungey, H., & Stovold, G.. (2016). Individual tree phenotyping. Presented to the:
Phenotyping Innovation Cluster meeting, Auckland, New Zealand, 11 May.• Pont, D., Watt, M.S., Morgenroth, J., Dungey, H., & Stovold, G. (2015). Phenotyping from remote sensing. Presented to the
Radiata Pine Breeding Company, Rotorua, New Zealand, 21 May.• Pont, D., Dungey, H., Watt, M.S., Morgenroth, J., & Stovold, G. (2015). Can we remotely sense phenotypic information from
genetics trials? Presented at the: Second Annual Conference of the GCFF research programme - “First glimpse at results”, Christchurch, New Zealand, 24-25 March.
• Pont, D., Watt. M.S., Dash, J., & Brownlie, R. (2014). Remote Sensing for Phenotyping. Presented to the: Forest Owners Association, Forest Growing Research Conference “More income, less risk”, Rotorua, New Zealand, 29-31 October.
Dissemination of research
PublicationsYongjun Li, Mari Suontama, Rowland Burdon, Heidi Dungey. Towards targeted deployment for capturing genetic gain with genotype by environment interaction in the forest estate: perspectives on research and application. Submitted to Trees Genetics and Genomes.
Two papers underway.
Technical reports• Pont, D., Watt, M.S., Morgenroth, J., Dungey, H., Brownlie, R.K., & Stovold, G. (2015). Locating individual
trees within a forest genetics trial. (GCFF TN-06). Rotorua, New Zealand; Growing Confidence in Forestry’s Future.
http://research.nzfoa.org.nz/www.scionresearch.com/gcff
Heidi Dungey, David Pont, Jonathan Dash, Mike Watt, Emily Telfer
Date: March 2016
