Tibial Loading Increases Osteogenic Gene Expression and Cortical Bone Volume in Mature and Middle-Aged Mice

There are conflicting data on whether age reduces the response of the skeleton to mechanical stimuli. We examined this question in female BALB/c mice of different ages, ranging from young to middle-aged (2, 4, 7, 12 months). We first assessed markers of bone turnover in control (non-loaded) mice. Serum osteocalcin and CTX declined significantly from 2 to 4 months (p<0.001). There were similar age-related declines in tibial mRNA expression of osteoblast- and osteoclast-related genes, most notably in late osteoblast/matrix genes. For example, Col1a1 expression declined 90% from 2 to 7 months (p<0.001). We then assessed tibial responses to mechanical loading using age-specific forces to produce similar peak strains (−1300 µε endocortical; −2350 µε periosteal). Axial tibial compression was applied to the right leg for 60 cycles/day on alternate days for 1 or 6 weeks. qPCR after 1 week revealed no effect of loading in young (2-month) mice, but significant increases in osteoblast/matrix genes in older mice. For example, in 12-month old mice Col1a1 was increased 6-fold in loaded tibias vs. controls (p = 0.001). In vivo microCT after 6 weeks revealed that loaded tibias in each age group had greater cortical bone volume (BV) than contralateral control tibias (p<0.05), due to relative periosteal expansion. The loading-induced increase in cortical BV was greatest in 4-month old mice (+13%; p<0.05 vs. other ages). In summary, non-loaded female BALB/c mice exhibit an age-related decline in measures related to bone formation. Yet when subjected to tibial compression, mice from 2–12 months have an increase in cortical bone volume. Older mice respond with an upregulation of osteoblast/matrix genes, which increase to levels comparable to young mice. We conclude that mechanical loading of the tibia is anabolic for cortical bone in young and middle-aged female BALB/c mice

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

Determinants of change in subtropical tree diameter growth with ontogenetic stage

We evaluated the degree to which relative growth rate (RGR) of saplings and large trees is related to seven functional traits that describe physiological behavior and soil environmental factors related to topography and fertility for 57 subtropical tree species in Dinghushan, China. The mean values of functional traits and soil environmental factors for each species that were related to RGR varied with ontogenetic stage. Sapling RGR showed greater relationships with functional traits than large-tree RGR, whereas large-tree RGR was more associated with soil environment than was sapling RGR. The strongest single predictors of RGR were wood density for saplings and slope aspect for large trees. The stepwise regression model for large trees accounted for a larger proportion of variability (R 2 = 0.95) in RGR than the model for saplings (R 2 = 0.55). Functional diversity analysis revealed that the process of habitat filtering likely contributes to the substantial changes in regulation of RGR as communities transition from saplings to large trees. © 2014 Springer-Verlag Berlin Heidelberg

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