Fast-growing trees such as hybrid poplar (Populus spp.) are characterized by rapid growth and high biomass production. However, their tree productivity can vary depending on tree spacing, site conditions and genotype selection. In this study, we evaluated the impact of site × spacing × clone interaction on tree productivity. We selected plantations of four hybrid poplar clones (747215; Populus trichocarpa Torrey & A. Gray × P. balsamifera L., 915004, 915005; P. balsamifera × P. maximowiczii Henry and 915319; P. maximowiczii × P. balsamifera) established at three spacings (1 × 4 m, 2 × 4 m and 3 × 4 m) across three sites of contrasting productivity in northwestern Quebec, Canada. Increasing spacing from 1 × 4 m to 3 × 4 m led to larger stem and higher mean annual increment (MAI) and above-ground biomass on a per tree basis at the most productive site. In contrast, tree size remained smaller and unchanged across spacings at the least productive site, and for the least productive clone. The proportion of biomass allocated to the stem decreased when spacing increased from 1 × 4 m to 3 × 4 m to the benefit of branches. Hybrid poplar plantations’ productivity was promising, as MAI per hectare reached 20.10 m3 ha−1 year−1 at narrower spacing (1 × 4 m) at the most productive site for the most productive clone (915319). These findings highlight that site conditions modulate spacing effects and that high-yielding clones with optimal densities can maximize hybrid poplar productivity.

The spruce budworm is one of the principal defoliators affecting forest health in Canada. Studying the impact of spruce budworm on forests including understanding the extent and severity of tree defoliation and mortality caused by the outbreaks is important for forest management. In this study, we utilized LandTrendr algorithm to get synthetic Landsat time-series data and used it to characterize spruce budworm outbreaks. Here, we built 14 configurations of predictors from different seasonal mosaics (summer mosaic, winter mosaic, and a combination of summer and winter mosaic) and various data sources including remote sensing data, aerial survey data, and a combination of both. We then employed a random forest model, incorporating data from 892 field plots to estimate defoliation and mortality caused by the spruce budworm in eastern Québec in the years 2008, 2013, 2018 and 2022. Using only remote sensing data, an R2 value of 78% for defoliation and 55% for mortality was obtained as the best results. Incorporating aerial survey data resulted in an increase of 1% for defoliation and 7% for mortality. We also found that the combination of summer and winter mosaic data resulted in the highest R2 values, with improvements of 1?8% for defoliation and 2?14% for mortality compared to models using data from single season. Through the variable importance ranked by the best model configurations for defoliation and mortality, we concluded that normalize burn ratio from winter mosaic served as the most important variable for the defoliation and cumulative defoliation rating from aerial survey ranked the first in mortality. The proposed approach can be used to obtain yearly continuous defoliation and mortality maps and thus assist in monitoring forests and their dynamics during spruce budworm outbreaks for carbon budget modelling.