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.

Abstract Under the current global biodiversity crisis, there is a need for automated and noninvasive monitoring techniques that can gather large amounts of data cost-effectively at various ecological scales, from local to large spatial scales. These data can then be analyzed to inform stakeholders and decision-makers. One such technique is passive acoustic monitoring, which is commonly coupled with automatic identification of animal species based on their sound. Automated sound analyses usually require the training of sound detection and identification algorithms. These algorithms are based on annotated acoustic datasets which mark the occurrence of sounds of species inside sound recordings. However, compiling large annotated acoustic datasets is time-consuming and requires experts, and therefore, they normally cover reduced spatial, temporal, and taxonomic scales. This data paper presents WABAD, the World Annotated Bird Acoustic Dataset for passive acoustic monitoring. WABAD is designed to provide the public, the research community, and conservation managers with a novel and globally representative annotated acoustic dataset. This database includes 5047?min of audio files annotated to species-level by local experts with the start and end time and the upper and lower frequencies of each identified bird vocalization in the recordings. The database has a wide taxonomic and spatial coverage, including information on 91,931 vocalizations from 1192 bird species recorded at 72 recording sites in 29 recording locations (mainly countries) and distributed across 13 biomes. WABAD can be used, for example, for developing and/or validating automatic species detection algorithms, answering ecological questions, such as assessing geographical variations on bird vocalizations, or comparing acoustic diversity indices with species-based diversity indices. The dataset is published under a Creative Commons Attribution 4.0 International license that permits redistribution and reuse on the condition that the original work is properly credited.