Increased interest in mixed forests is due to evidence of them being more resource-use efficient and stable forest systems. However, intrinsic and extrinsic factors moderate interspecific species interactions generating different effects in productivity. Here, we explore a method to detect mixing effects in a specific mixture combination (Pinus sylvestris L. and Pinus pinaster Ait.), comparing the growth of mixed stands with that of monocultures. Combined tree and stand-level analyses also helped determine which mixing effects are most important for forest functioning and how changes at one level influence patterns at another level. Data from the Spanish National Forest Inventory were used to compare growth efficiency in mixed and pure stands; we relied on relative stand density indices to determine species-specific site occupancy. This same concept was used to evaluate competition status and inter/intra-specific competition effects as modifiers of potential growth at the tree-level. We observed that growth efficiency in both species increased with the proportion of the complementary species in the stand. At the tree-level, intraspecific competition was higher than interspecific competition in Scots pine tree growth, showing that it had benefited from the mixture. In contrast, maritime pine did not show a competitive response to the interspecific interaction, indicating that tree growth was more strongly influenced by the competition structure (size-symmetric and size-asymmetric) than by the species of the competitors. Our results highlight the importance of combining stand-level analysis with that of tree-specific competition relationships when studying mixed-species forests.

Models that incorporate known species-mixing effects on tree growth are essential tools to properly design silvicultural guidelines for mixed-species stands. Here, we developed generalized height–diameter (h-d) and basal area growth models for mixed stands of two main forest species in Spain: Scots pine (Pinus sylvestris L.) and Maritime pine (Pinus pinaster Ait.). Mixed-effects models were fitted from plot measurement and tree rings data from 726 Scots pine and 693 Maritime pine trees from mixed and pure stands in the Northern Iberian Range in Spain, with the primary objective of representing interactions between the species where they are interspersed in mixtures of varying proportions. An independent dataset was used to test the performance of the h-d models against models previously fitted for monospecific stands of both species. Basal area increment models were evaluated using a 10-fold block cross-validation procedure. We found that species mixing had contrasting effects on the species in both models. In h-d models, the species-mixing proportion determined the effect of species interactions. Basal area growth models showed that interspecific competition was influential only for Maritime pine; however, these effects differed depending on the mode of competition. For Scots pine, tree growth was not restricted by interspecies competition. The combination of mixed-effect models and the inclusion of parameters expressing species-mixing enhanced estimates of tree height and basal area growth compared with the available models previously developed for pure stands. Although the species-mixing effects were successfully represented in the fitted models, additional model components for accurately simulating the stand dynamics of mixtures with Scots pine and Maritime pine and other species mixtures require similar model refinements. Upon the completion of analyses required for these model refinements, the degree of improvement in simulating growth in species mixtures, including the effects of different management options, can be evaluated.

When combining multi-temporal airborne laser scanning (ALS) data sets, forest height growth assessments can be compromised due to variations in ALS acquisitions. Herein, we demonstrate the importance of assessing and harmonizing the vertical alignment of multi-temporal ALS data sets used for height growth calculations. Using four ALS acquisitions (2005–2018) in a temperate mixedwood forest, we developed an ALS data harmonization approach and quantified the impact of the harmonization on derived height periodic annual increment (PAI), comparing the ALS-derived PAI to PAI derived from non-harmonized ALS data sets and field measurements. We found significant differences in PAI derived from harmonized and non-harmonized data, and these differences were greater for shorter growth intervals. Data harmonization resulted in a consistent PAI series that reduced uncertainties associated with the different ALS acquisitions. Although overall there was a strong relationship between field and ALS height measures ( R² ≥ 0.88), we found a weak relationship between the field- and ALS-derived PAI ( R² = 0.12). We identified systematic errors in field-based tree height measures in plots with complex crowns, tall trees, and restricted visibility. We demonstrate the need for harmonizing multi-temporal ALS data sets for the generation of PAI and, likewise, highlight the need of carefully scrutinize field-measured heights and associated increments.

Laser scanning sensors mounted on drones enable on-demand quantification of forest structure through the collection of high-density point clouds (500+ points m⁻²). These point clouds facilitate the detection of individual trees enabling the quantification of growth-related variables within a stand that can inform precision management. We present a methodology to link incremental growth data obtained from tree cores with crown models derived from drone laser scanning, quantifying the relative growth condition of individual trees and their neighbours. We stem-mapped 815 trees across five stands in north-central British Columbia, Canada of which 16% were cored to quantify recent basal area growth. Point clouds from drone laser scanning and orthomosaic imagery were used to locate trees, model three-dimensional crown features, and derive competition metrics describing the relative distribution of crown sizes. Local access to water and light were simulated using topographic wetness and potential solar irradiance indices derived from high-resolution terrain and surface models. Wall-to-wall predictions of recent basal area growth were produced from the best-performing model and summarized across a grid alongside a tree-level competition index. Overall, crown volume was most strongly correlated with observed differences in 5-year basal area increment (R² = 0.70, P < .001). Competition and solar irradiance metrics were significant as univariate predictors (P < .001) but nonsignificant when included in multivariate models with crown volume. Using predictions from the best-performing model and laser-scanning-derived competition metrics, we present a newly developed growth competition index to assess variability and inform commercial thinning prescription prioritization. Growth predictions, competition metrics, and the growth competition index are summarized into maps that could be used in an operational workflow. Our methodology presents a new capacity to capture and quantify intra-stand variation in growth by combining competition metrics and measures of recent growth with high-density drone laser scanning data.