100m and 200m front crawl performance prediction based on anthropometric and physiological measurements

Background: The identification of the variables that are able to predict swimming performance is one of the main purposes of the “swimming science” community. Research question: The aims of the study were: (i) to compare the anthropometric and physiological profiles of 100m and 200m front crawl swimmers and; (ii) to identify anthropometric and physiological variables that account for the prediction of the swimming performance at the 100m and 200m front crawl events. Methods: Twenty-six male swimmers were divided in two groups (12 for 100m group and 14 to 200m group). The swimmers’ personal best performance for the 100m and the 200m front crawl was converted to FINA points. The subjects performed a graded swimming test and an all-out test (100 or 200m maximal swims) in different days, in which physiological measures were evaluated. Forward step-by-step linear regression models were computed to predict swimming performance. The subjects’ performances (season best and all-out test) were taken as dependent variables. The age, physiological and anthropometric measures were selected as independent variables. Results: Anthropometric and physiological profiles of 100 and 200m swimmers are different and the mean oxygen uptake during exercise combined with training experience may explain 200m front crawl best season performance with a high precision (≈2% error). The models computed were able to predict from 44 % (i.e. 200m all-out bout) to 61 % (i.e. 200m season best) swimming performance. Predictive power of the models was less accurate in the 100m event (error > 10%). Conclusions: The authors conclude that the extent to which the physiological and anthropometric variables combine to predict performance probable is group-specific

The Linkages Between Photosynthesis, Productivity, Growth and Biomass in Lowland Amazonian Forests

Understanding the relationship between photosynthesis, net primary productivity and growth in forest ecosystems is key to understanding how these ecosystems will respond to global anthropogenic change, yet the linkages among these components are rarely explored in detail. We provide the first comprehensive description of the productivity, respiration and carbon allocation of contrasting lowland Amazonian forests spanning gradients in seasonal water deficit and soil fertility. Using the largest data set assembled to date, ten sites in three countries all studied with a standardized methodology, we find that (i) gross primary productivity (GPP) has a simple relationship with seasonal water deficit, but that (ii) site-to-site variations in GPP have little power in explaining site-to-site spatial variations in net primary productivity (NPP) or growth because of concomitant changes in carbon use efficiency (CUE), and conversely, the woody growth rate of a tropical forest is a very poor proxy for its productivity. Moreover, (iii) spatial patterns of biomass are much more driven by patterns of residence times (i.e. tree mortality rates) than by spatial variation in productivity or tree growth. Current theory and models of tropical forest carbon cycling under projected scenarios of global atmospheric change can benefit from advancing beyond a focus on GPP. By improving our understanding of poorly understood processes such as CUE, NPP allocation and biomass turnover times, we can provide more complete and mechanistic approaches to linking climate and tropical forest carbon cycling

The FAIR Guiding Principles for scientific data management and stewardship

There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community