Adaptation of tropical and subtropical pine plantation forestry to climate change: Realignment of Pinus patula and Pinus tecunumanii genotypes to 2020 planting site climates

Abstract

Pinus patula and Pinus tecunumanii, two pines native to Mexico and Central America, are important plantation species for the forestry sector in the tropics and subtropics. In recent decades, members of the International Tree Conservation & Domestication Program (CAMCORE), North Carolina State University, have established large, multisite provenance trials for these pine species. The data provide valuable information about species and provenance choice for plantation establishment in many regions with different climates. However, since climate is changing rapidly, it may become increasingly difficult to choose the right species and provenance to plant. The aim of this study is to test the suitability of seed material under changing climate of two P. patula varieties (P. patula var. patula and P. patula var. longipedunculata) and two P. tecunumanii ecotypes (highland and lowland). For each variety and ecotype, a site quality model was developed that statistically relates growth to environmental factors and couples the predictions to the average 2020 climate prediction of four general circulation models. Three developed models were significant and robust. Provenances of P. tecunumanii from lowland areas in Central America are expected to be most productive in 2020 because of their promising performance under rather hot and wet climates.

Keywords:

• Climate change impact predictions
• height growth
• management decision support tools
• provenance trials
• site quality modelling

Acknowledgements

The corresponding author would like to express his gratitude to the Ausgleichsstiftung Landwirtschaft und Umwelt, whose financial support enabled his thesis. We would further like to thank the CAMCORE members in Brazil, Colombia and South Africa for establishing, maintaining and measuring the research field plantings of P. patula and P. tecunumanii. We would like to thank William Woodbridge, Camcore data manager, for his assistance in preparing the large data sets for analysis and for providing support and updates throughout the course of the study. We thank Arjan de Bruijn for his valuable advice in the development of the site quality models and appreciate the valuable comments of two anonymous reviewers. Finally, we would like to thank Julian Ramirez Villegas, Edward Guevara and James Garcia from the agro-ecosystems resilience office in CIAT for their help and advice on the computation and implementation of the regression functions.