Neighbour species richness and local structural variability modulate aboveground allocation patterns and crown morphology of individual trees

Abstract

Local neighbourhood interactions are considered a main driver for biodiversity–productivity relationships in forests. Yet, the structural responses of individual trees in species mixtures and their relation to crown complementarity remain poorly understood. Using a large‐scale forest experiment, we studied the impact of local tree species richness and structural variability on above‐ground wood volume allocation patterns and crown morphology. We applied terrestrial laser scanning to capture the three‐dimensional structure of trees and their temporal dynamics. We found that crown complementarity and crown plasticity increased with species richness. Trees growing in species‐rich neighbourhoods showed enhanced aboveground wood volume both in trunks and branches. Over time, neighbourhood diversity induced shifts in wood volume allocation in favour of branches, in particular for morphologically flexible species. Our results demonstrate that diversity‐mediated shifts in allocation pattern and crown morphology are a fundamental mechanism for crown complementarity and may be an important driver of overyielding.

Neighbourhood diversity mitigates drought impacts on tree growth

“…Next to resource partitioning, microclimate amelioration via facilitative neighbourhood interactions might act as a further mechanism by which the water demand of a focal tree growing in diverse neighbourhoods is decreased. For example, increasing tree species diversity at the local neighbourhood scale allows for more complex structured and densely packed canopies by shifts in wood volume allocation in favour of branches over time (Kunz et al, ). This, in turn, can reduce irradiance, air and soil surface temperature as well as vapour pressure deficits at the leaf surface and the evaporative demand of whole trees (Montgomery, Reich, & Palik, ), therefore improving abiotic growing conditions during drought.…”

Community and structural constraints on the complexity of eastern North American forests

“…As hypothesized, we found that between sites canopy rugosity was greatest where taxonomic, particularly family, richness was high. Crown morphological divergence in more diverse forests (Benavides, Scherer‐Lorenzen, & Valladares, 2019; Kunz et al, 2019) may enhance complexity by supplying a variety of architectures, heights and forms. In our study, crown morphological redundancy may explain why family – rather than species or genus – richness was more highly correlated with canopy rugosity.…”

“…Canopy structural complexity, which describes horizontal and vertical variation in vegetation density, height or distribution, is an emergent ecosystem property that may be viewed as a product of several forest community and structural features (Ali, 2019; Ali et al, 2019; Ehbrecht, Schall, Juchheim, Ammer, & Seidel, 2016; Forrester, 2019; LaRue, Hardiman, Elliott, & Fei, 2019; Seidel, 2018; Wilkes et al, 2016). Plant taxonomic and phylogenetic diversity supports complexity by supplying interspecific genetic diversity and the related crown architectural variety required to build complex canopy structures (Figure 1a; Ali et al, 2019; Fotis et al, 2018; Jucker, Bouriaud, & Coomes, 2015; Kunz et al, 2019; Pretzsch et al, 2015; Seidel, Annighofer, et al, 2019). Leaf mass or area provides the construction materials with which to manufacture complex canopy structures (Figure 1b; Hardiman, Bohrer, Gough, Vogel, & Curtis, 2011; Pretzsch et al, 2016).…”

Structural Changes in Boreal Forests Can Be Quantified Using Terrestrial Laser Scanning

“…Although it has been demonstrated that TLS technology can be used for detecting structural changes in single trees as well as changes in the canopy structure [30][31][32][33][34][35], there is still a limited understanding on how different kinds of forest structural changes can be quantified using TLS in boreal forest conditions. Srinivasan et al [30] used TLS data from two time points (2009 and 2012) for modeling tree biomass changes in East Texas.…”

“…The approach can be used at the local neighborhood level for revealing the extent of canopy space-filling, identifying interactions between trees, and analyzing complementary space use. Kunz et al [34] used TLS data to capture the three-dimensional structure of trees and investigate their temporal dynamics. Bi-temporal TLS data was collected between 2012 and 2016 and QSMs were then used to reconstruct tree stems and branches followed by the generation of 2D and 3D alpha-shapes for deriving additional crown shape and size parameters.…”