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Improving the efficiency of genetic selection in Sitka spruce using spatial and competition factors

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Abstract

Sitka spruce (Picea sitchensis (Bong.) Carr.), is a highly valuable commercial tree species in Ireland, UK and elsewhere in Europe but there is relatively little information available in relation to the performance of improved material. The primary objective of this study was to explore the genetic variation in competitive ability and determine how a spatial model improved the accuracy of genetic estimates over time for Sitka spruce. To achieve this, the study employed competition index (Hegyi’s I) and spatial analysis on height and diameter data from genetic improvement trials in Ireland. A total of 54 half-sib families of Sitka spruce were included in the study. The trials had been established to identify superior genotypes among the plus trees which had been included in the improvement programme in Ireland. The study revealed that spatial heterogeneity significantly affects the genetic estimates for Sitka spruce. The results indicated that spatial analysis could provide valuable insights into the genetic trials of Sitka spruce and improve the accuracy of genetic evaluations. Heritability increased by 104% for diameter at breast height (DBH) using a spatial analysis approach, compared with a more traditional non-spatial model approach. It was found that thinning practices can affect the response of genetic performance to spatial heterogeneity. In addition, genetic variation in competitive ability was observed. The results highlight the importance of considering spatial dependence in the design but more importantly in the analysis of genetic trials and provides a framework for future research on the genetic improvement of Sitka spruce populations.

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Acknowledgements

Mr Dermot O’Leary the Nurseries Production Business Manager at Coillte provided permission to access the trial sites and original datasets. The authors acknowledge gratefully the assistance of Mr Donal O’Hare, Mr Ciaran Foran and Ronan Cashell in carrying out inventory and field data collection. Also Dr Alistair Pfeiffer who provided information about original establishment and research objectives concerning the field trials.

Funding

The research leading to these results received funding from the Irish Department of Agriculture, Food and the Marine (Genesis Project, 17/C/297).

Author information

Authors and Affiliations

  1. School of Agriculture and Food Science, UCD Forestry, University College Dublin, Dublin, D04V1W8, Ireland

    Shuyi Yang, Brian Tobin & Conor O’Reilly

  2. UCD Earth Institute, University College Dublin, Dublin, D04V1W8, Dublin, Ireland

    Brian Tobin

  3. Crops, Environment and Land Use Programme, Crop Science Department, Teagasc, Oak Park, Carlow, R93XE12, Ireland

    Stephen Byrne

  4. Department of Organisms and Systems Biology, Polytechnic School of Mieres, University of Oviedo, E-33600 Mieres, Asturias, Spain

    Pedro Álvarez-Álvarez

  5. Forestry Development Department, Teagasc, Athenry, Galway, H65R718, Ireland

    Niall Farrelly

Authors
  1. Shuyi Yang
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  4. Pedro Álvarez-Álvarez
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Contributions

SY collected data in 2019, analysed the data and wrote the original manuscript. BT and CO supervised each stage of the study, reviewed and edited the original manuscript. SB and PÁ-Á reviewed and edited the text and contributed to the methodological approaches used. NF organized the historical data. The authors greatly appreciate the anonymous reviewers’ insightful comments and suggestions that improved the quality of this paper.

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Correspondence to Shuyi Yang.

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Appendix 1

Appendix 1

See Tables 5, 6 and 7, Figs. 5, 6, 7 and 8.

Table 5 The model fit statistics of model G and variances obtained from model G
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Table 6 Global spatial dependence coefficient (global Moran’s I value) for growth traits and Pilodyn penetration depth at various ages at Site 2
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Table 7 The comparison of model fit statistics between the base model (B) and models including first-order autoregressive spatial terms (AR1) at Site 2
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Fig. 5
figure 5

Local indicator of spatial dependence (LISA) cluster and outlier map based on analysis of local Moran’s I for a) DBH at age 15, b) DBH at age 20 and c) DBH at age 24 along the row and columns of the layout map of the experiment (solid lines in the figures indicate the boundaries of blocks) at Site 1

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Fig. 6
figure 6

Local indicator of spatial dependence (LISA) cluster and outlier map based on analysis of local Moran’s I for a) height (HT) at age 11, b) diameter at breast height (DBH) at age 11, c) HT at age 32, d) DBH at age 32 and e) Pilodyn penetration depth (PP) at age 13 at Site 2 along the row and columns of the layout map of the experiment (solid lines in the figures indicate the boundaries of blocks) of the layout map of the experiment. Note: regarding subject-neighbouring trees, HH: high-high cluster; LL: low-low cluster; HL: high-low outlier; LH: low–high outlier; NS: non-significant

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Fig. 7
figure 7

The heat map of prediction of breeding values for growth traits at ADH across ages based on base model (left) and best model including the spatial term (right) at Site 1. Family ranks (i.e. from 1 to 56) were labelled as numbers with red and blue colours

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Fig. 8
figure 8

The heat map of prediction of breeding values for growth traits and Pilodyn at CGY across ages based on base model (left) and best model including the spatial term (right) at Site 2. Family ranks (i.e. from 1 to 56) were labelled as numbers with red and blue colour

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Yang, S., Tobin, B., Byrne, S. et al. Improving the efficiency of genetic selection in Sitka spruce using spatial and competition factors. New Forests (2023). https://doi.org/10.1007/s11056-023-10019-5

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