Article Meta-Modelling to Quantify Yields of White Spruce and Hybrid Spruce Provenances in the Canadian y Boreal Forest Suborna Ahmed 1,*, Valerie LeMay 1, Alvin Yanchuk 2, Andrew Robinson 3, Peter Marshall 1 and Gary Bull 1 1 Department of Forest Resources Management, University of British Columbia, Rm 2045, 2424 Main Mall, Vancouver, BC V6T 1Z4, Canada;
[email protected] (V.L.);
[email protected] (P.M.);
[email protected] (G.B.) 2 British Columbia Ministry of Forests, Lands and Natural Resources Operations, PO Box 9518, STN Prov Govt, Victoria, BC V8W9C2, Canada;
[email protected] 3 CEBRA, School of BioSciences, School of Mathematics & Statistics, University of Melbourne, Melbourne VIC 3010, Australia;
[email protected] * Correspondence:
[email protected]; Tel.: +1-604-827-2059 This manuscript is part of a Ph.D. thesis by the first author, available online at open.library.ubc.ca. y Received: 26 April 2020; Accepted: 26 May 2020; Published: 28 May 2020 Abstract: Tree improvement programs can improve forest management by increasing timber yields in some areas, thereby facilitating conservation of other forest lands. In this study, we used a meta-analytic approach to quantify yields of alternative white (Picea glauca (Moench) Voss) and hybrid spruce (Picea engelmannii Parry ex Engelmann x Picea glauca (Moench) Voss) stocks across planting sites in the boreal and hemiboreal forests of Canada. We extracted meta-data from published tree improvement program results for five Canadian provinces covering 38 planting sites and 330 white and hybrid spruce provenances. Using these meta-data and a random-coefficients nonlinear mixed-effects modelling approach, we modelled average height over time trajectories for varying planting site characteristics, as well as climate transfer distances between planting sites and provenances.