Phenotyping the forest-concepts and progress. Heidi Dungey, Dave Pont, Jonathan Dash, Mike Watt, Toby Stovold, Emily Telfer

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1 Phenotyping the forest-concepts and progress Heidi Dungey, Dave Pont, Jonathan Dash, Mike Watt, Toby Stovold, Emily Telfer

2 A national aim to double forest productivity Static forest area

3 Phenotyping the forest What is it? What can we do? What have we done? Next steps

4 What is Phenotyping? extracting biologically meaningful signal from environmental and experimental noise (Cobb et al. 2013) 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) Controlled environment High throughput Non-destructive Image analysis Slide: Dave Pont

5 Phenotyping trees in the forest

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13 ?

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15 Cambium 1 Xylem

16 Who s your Daddy?

17 Pedigree Reconstruction 1. Progeny Trial 2. Parental Seed Orchard Trees DNA Fingerprint generated 3. Select Best Progeny 4. DNA Fingerprint generated 6. Selected Breeding with 7. Improved Progeny

18 $

19 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

20 GF+ 26 Clone x GF+ 19

21 Nursery picture

22 Where are we up to?

23 LAI and forest health/nutrition

24 Tree detection over the trial

25 Single tree detection and crown metrics

26 Correlations Pearson's r >=0.75 >=0.50 >=0.25 Height H Size Form Wood quality Disease Basic Volume Straightness Branchiness Malformation Stiffness Dothistroma density V S B M A D ρ 38 Diameter DBH TH CV F SHV RU Strong correlations: H DBH V r=0.90 r=0.82 r=0.84

27 Small changes in heritabilities were found from LiDAR-derived height and DBH AFTER BEFORE GROUND 0.0 Height DBH

28 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

29 Heatmap of DBH and terrain map of trial area

30 Remote sensing and phenotyping are core elements of a multi-year forestry research programme Scion (2014). Growing Confidence in Forestry's Future: Research Programme Rotorua, New Zealand: Forest Research Institute Ltd

31 Where we up to GCFF Research Programme programme/

32 Where we up to 2015 First estimate of utility of phenotyping platform for genetics trials And tree breeding

33 Where we up to Utility of phenotyping platform to assess trees that have better disease resistance has been examined and unique individuals selected

34 Where we up to Phenotyping platform has been used to better understand the drivers of growth and quality in radiata pine

35 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, 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, 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 Whistler, British Columbia, Canada, 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, 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, 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, October.

36 Dissemination of research Publications Yongjun 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.

37 Heidi Dungey, David Pont, Jonathan Dash, Mike Watt, Emily Telfer Scientist Date: March 2016

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