Overstory influences on light attenuation patterns and understory plant community diversity and composition in southern boreal forests of Quebec

We have characterized overstory light transmission, understory light levels, and plant communities in mixedwood boreal forests of northwestern Quebec with the objective of understanding how overstory light transmission interacts with composition and time since disturbance to influence the diversity and composition of understory vegetation, and, in turn, the further attenuation of light to the forest floor by the understory. Overstory light transmission differed among three forest types (aspen, mixed deciduous-conifer, and old cedar-dominated), with old forests having higher proportions of high light levels than aspen and mixed forests, which were characterized by intermediate light levels. The composition of the understory plant communities in old forests showed the weakest correlation to overstory light transmission, although those forests had the largest range of light transmission. The strongest correlation between characteristics of overstory light transmission and understory communities was found in aspen forests. Species diversity indices were consistently higher in aspen forests but showed weak relationships with overstory light transmission. Light attenuation by the understory vegetation and total height of the understory vegetation were strongly and positively related to overstory light transmission but not forest type. Therefore, light transmission through the overstory influenced the structure and function of understory plants more than their diversity and composition. This is likely due to the strong effect of the upper understory layers, which tend to homogenize light levels at the forest floor regardless of forest type. The understory plant community acts as a filter, thereby reducing light levels at the forest floor to uniformly low levels

TRY plant trait database – enhanced coverage and open access

Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Semi-blind iterative joint channel estimation and K-best sphere decoding for MIMO

An efficient and high-performance semi-blind scheme is proposed for Multiple-Input Multiple-Output (MIMO) systems by iteratively combining channel estimation with K-Best Sphere Decoding (SD). To avoid the exponentially increasing complexity of Maximum Likelihood Detection (MLD) while achieving a near optimal MLD performance, K-best SD is considered to accomplish data detection. Semi-blind iterative estimation is adopted for identifying the MIMO channel matrix. Specifically, a training-based least squares channel estimate is initially provided to the K-best SD data detector, and the channel estimator and the data detector then iteratively exchange information to perform the decision-directed channel update and consequently to enhance the detection performance. The proposed scheme is capable of approaching the ideal detection performance obtained with the perfect MIMO channel state information