DOI: 10.5061/dryad.5hqbzkhhk
title: Data from: Leveraging UAV spectral and thermal traits for the genetic improvement of resistance to Dothistroma needle blight in Pinus radiata D.Don

description: Phenotyping is critical in tree breeding, but traditional methods are
 often labour-intensive and not easily scalable. Resistance to biotic and
 abiotic stress is a key focus in tree breeding programmes. While heritable
 traits derived from spectral remote sensing have been identified in trees,
 their application to tree phenotyping remains unexplored. This study
 investigates in situ high-throughput hyperspectral and thermal
 imaging for assessing Dothistroma needle blight (DNB) resistance in Pinus
 radiata D.Don. Using UAV-based hyperspectral and thermal imaging during a
 severe DNB outbreak in a clonal trial in New Zealand, we computed
 narrow-band hyperspectral indices (NBHIs), canopy temperature indices,
 radiative transfer inverted plant traits, and solar-induced fluorescence.
 Visual severity scores and remote sensing indices were modelled using
 spatially explicit mixed-effect linear models integrating pedigree and
 genomic data in a single-step genomic evaluation. Multi-trait models and
 sampling simulations were used to evaluate the potential of remote sensing
 indices to supplement or replace traditional phenotyping. Remote sensing
 indices exhibited narrow-sense heritability values comparable to severity
 scores (up to 0.37) and high absolute correlation coefficients with
 severity scores (up to 0.79). Carotenoid and chlorophyll-related NBHIs
 were the most informative, reflecting the physiological impacts of DNB.
 Combining partial visual scoring with NBHIs maintained high estimated
 breeding value (EBV) accuracy (0.68) at 50% scoring and moderate accuracy
 (0.59) at 20% scoring. EBV correlation with full scoring was above 0.8
 even at 20% scoring. Using solely the most heritable NBHI, achieved 0.71
 breeding value accuracy and 0.79 absolute EBV correlation with severity
 scores, suggesting NBHIs can replace visual scoring with minimal precision
 loss. By utilising UAV-based hyperspectral and thermal imaging to capture
 single-tree phenotypes related to disease in a forestry trial and pairing
 the data to genomic evaluation, this study establishes that remote sensing
 data offers an efficient, scalable alternative to traditional phenotyping.
 Our approach constitutes a major step towards characterising specific
 physiological responses, facilitating the discovery of the genetic
 architecture of physiological traits, and significantly enhancing genetic
 improvement.
description: # Leveraging UAV spectral and thermal traits for the genetic improvement
 of resistance to Dothistroma needle blight in Pinus radiata D.Don Dataset
 DOI: [10.5061/dryad.5hqbzkhhk](10.5061/dryad.5hqbzkhhk) ## Description of
 the data and file structure ### Files and variables #### File:
 data_confidentialised.zip **Description:**  **pedigree.csv:**  Pedigree of
 all clones used in the study and their ancestors. This is used to create
 the A matrix (pedigree-based relationship matrix). * Tree: genotype index.
 All trees used in the published research article have an entry in this
 column. - Mom: index of the seed parent of the corresponding Tree * Dad:
 index of the pollen parent of the corresponding Tree. The table is sorted
 so that the row giving the pedigree of an individual appears before any
 row where that individual appears as a parent. It uses identity 0 or NA
 for unknown parents. **phenotypes.csv:** This file contains phenotypes and
 trial design information for all trees included in the published article.
 * Severity: Visual Dothistroma needle blight severity score of individual
 tree - Row, Column: location of individual tree in the breeding trial *
 Block: design structure of the breeding trial. - Genotype: clone index
 corresponds to "Tree" indices present in pedigree.csv and column
 names of SNPs.csv. All other columns: Remote sensing indices investigated
 in the study. please refer to published article, Table S1, for details.
 **SNPs.csv:** This file is a matrix of single-nucleotide polymorphisms
 (SNPs) for the clones present in the published article. This SNP matrix
 was used to calculate the G matrix (marker-based relationship matrix). SNP
 markers are in rows and individual genotypes (clones) in columns. The
 genotype indices (column names) correspond to indices present in
 pedigree.csv and to indices present in the "Genotype" column of
 phenotypes.csv. **remote_sensing_index_names.txt:** This file defines all
 remote sensing indices including the remote sensing index codes and full
 names ## Code/software All files are csv files which can be open in most
 data analysis software or in Microsoft Excel. ## Access information Other
 publicly accessible locations of the data: * NA Data was derived from the
 following sources: * NA