Abstract
Research highlights: Using 10-year tree height data obtained after planting from the range-wide provenance trials of Abies sachalinensis, we constructed multivariate random forests (MRF), a machine learning algorithm, with climatic variables. The constructed MRF enabled prediction of the optimum seed source to achieve good performance in terms of height growth at every planting site on a fine scale. Background and objectives: Because forest tree species are adapted to the local environment, local seeds are empirically considered as the best sources for planting. However, in some cases, local seed sources show lower performance in height growth than that showed by non-local seed sources. Tree improvement programs aim to identify seed sources for obtaining high-quality timber products by performing provenance trials. Materials and methods: Range-wide provenance trials for one of the most important silvicultural species, Abies sachalinensis, were established in 1980 at nine transplanting experimental sites. We constructed an MRF to estimate the responses of tree height at 10 years after planting at eight climatic variables at 1 km × 1 km resolution. The model was applied for prediction of tree height throughout Hokkaido Island. Results: Our model showed that four environmental variables were major factors affecting height growth—winter solar radiation, warmth index, maximum snow depth, and spring solar radiation. A tree height prediction map revealed that local seeds showed the best performance except in the southernmost region and several parts of northern regions. Moreover, the map of optimum seed provenance suggested that deployment of distant seed sources can outperform local sources in the southernmost and northern regions. Conclusions: We predicted that local seeds showed optimum growth, whereas non-local seeds had the potential to outperform local seeds in some regions. Several deployment options were proposed to improve tree growth.
Highlights
Plant species often show local adaptation, which is a process by which populations genetically diverge in response to natural selection specific to their habitat [1]
Tree height was negatively affected by several meteorological factors at other sites; for example, snow pressure broke branches in A31, late frost damaged young shoots in A36, and winter cold injury or desiccation occurred in A38 where transplants were not covered by snow because of the shallowest snow depth among all testing sites
The multivariate random forests (MRF) showed that WinSR, warmth index (WI), maximum snow depth (MSD), and SprSR were important factors affecting the height growth of A. sachalinensis, the order of importance differed among regional groups (Figure 3)
Summary
Plant species often show local adaptation, which is a process by which populations genetically diverge in response to natural selection specific to their habitat [1]. Forests 2020, 11, 1058 observed when plants are transplanted to different growth environments [2] This is observed for long-lived forest tree species with a wide distribution range which are often genetically adapted to local climatic environments, despite their extensive gene flow [3]. Provenance trials of forest trees have aimed to identify optimal seed provenances to ensure successful tree planting [4,5,6]. These attempts often result in accepting local seeds to avoid maladaptation caused by environmental mismatch between the afforestation site and the seed origin [7]. Range-wide provenance trials of Pinus sylvestris revealed that progeny derived from warmer climates outgrew local seed sources in central and northern sites, whereas local seeds grew best in southern sites [8]
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