Testing the Metabolic Scaling Theory of tree growth

1.  Metabolic Scaling Theory (MST) predicts a 'universal scaling law' of tree growth. Proponents claim that MST has strong empirical support: the size-dependent growth curves of 40 out of 45 species in a Costa Rican forest have scaling exponents indistinguishable from the MST prediction.
2.  Here, we show that the Costa Rican study has been misinterpreted. Using Standardized Major Axis (SMA) line-fitting to estimate scaling exponents, we find that four out of five species represented by more than 100 stems have scaling exponents that deviate significantly from the MST prediction. On the other hand, sample sizes were too small to make strong inferences in the cases of 33 species represented by fewer than 50 stems.
3.  Recently, it has been argued that MST is useful for predicting average scaling exponents, even if individual species do not conform to the theory. We find that the mean scaling exponent of the Costa Rican trees is greater than predicted (across-species mean  =  0.44), and hypothesize that scaling exponents in natural forests will generally be greater than predicted, because the theory fails to model asymmetric competition for light.
4.   Synthesis . We highlight shortcomings in the interpretation of data used in support of a key MST prediction. We recommend that future research into biological scaling should compare the merits of alternative models rather than focusing attention on tests of a single theory.     

Estimating switchgrass productivity in the Great Plains using satellite vegetation index and site environmental variables

Switchgrass is being evaluated as a potential feedstock source for cellulosic biofuels and is being cultivated in several regions of the United States. The recent availability of switchgrass land cover maps derived from the National Agricultural Statistics Service cropland data layer for the conterminous United States provides an opportunity to assess the environmental conditions of switchgrass over large areas and across different geographic locations. The main goal of this study is to develop a data-driven multiple regression switchgrass productivity model and identify the optimal climate and environment conditions for the highly productive switchgrass in the Great Plains (GP). Environmental and climate variables used in the study include elevation, soil organic carbon, available water capacity, climate, and seasonal weather. Satellite-derived growing season averaged Normalized Difference Vegetation Index (GSN) was used as a proxy for switchgrass productivity. Multiple regression analyses indicate that there are strong correlations between site environmental variables and switchgrass productivity (r = 0.95). Sufficient precipitation and suitable temperature during the growing season (i.e., not too hot or too cold) are favorable for switchgrass growth. Elevation and soil characteristics (e.g., soil available water capacity) are also an important factor impacting switchgrass productivity. An anticipated switchgrass biomass productivity map for the entire GP based on site environmental and climate conditions and switchgrass productivity model was generated. Highly productive switchgrass areas are mainly located in the eastern part of the GP. Results from this study can help land managers and biofuel plant investors better understand the general environmental and climate conditions influencing switchgrass growth and make optimal land use decisions regarding switchgrass development in the GP.     

Analysis of Rates Using a Generalized Poisson Regression Model

In this paper a generalization of the Poisson regression model indexed by a shape parameter is proposed for the analysis of life table and follow-up data with concomitant variables. The model is suitable for analysis of extra-Poisson variation data. The model is used to fit the survival data given in Holford (1980). The model parameters, the hazard and survival functions are estimated by the method of maximum likelihood. The results obtained from this study seem to be comparable to those obtained by Chen (1988). Approximate tests of the dispersion and goodness-of-fit of the data to the model are also discussed.     

Factors affecting the health of residents in China: A perspective based on the living environment

Residents’ health is an important factor affecting social development and harmony. Based on 2010 China Family Panel Studies data of the Institute of Social Science Survey, Peking University and using a multi-classification logit regression model, we analyze the factors that affect the health status of residents in China. These factors include environmental pollution, which is a particularly important factor. Our study found that the impacts of residents’ characteristic variables, external living environment, and living habits vary. As residents age, their health status deteriorates. For the General, Less healthy, and Unhealthy groups, an income of less than CNY 10,000 significantly affects health status; however, when their income is greater than CNY 10,000, it no longer has a significant effect. For the Very unhealthy group, this particular threshold value is CNY 3000. At least one of urban–rural classification and residence registration status is significant, indicating that the urban–rural dual structure as well as the household registration system significantly affects residents’ health status. However, the direction of this effect is uncertain. Cooking water significantly affects the Less healthy and Unhealthy groups, and tap water is more conducive to health. Polluting enterprises within a radius of five kilometers mainly affect the Unhealthy group, but the direction of its impact is contrary to expectations. Smoking and drinking significantly affect the health status of the General, Less healthy, and Unhealthy groups. However, the direction of their impact was contrary to expectations. For the Very unhealthy group, drinking has a significant impact on residents’ health status, but the direction of the impact was again the opposite of what we expected. Smoking has no significant impact on the health status of this group. Exercise significantly affects the Less healthy and Unhealthy groups, but its influence has no obvious trend. Our study shows that living habits have a smaller influence on residents’ health status.     

Record performances at the Boston Marathon: Biometeorological factors

Air temperature and relative humidity have long been suspected of affecting the performance of marathon runners. Though these factors are important in their extremes, we show that other factors are even more indicative of race performances. Performances of the top 3 finishers in the last 30 Boston Marathons were correlated with hourly meteorological data for each race day. These 90 individual performances were classified as: record breaking performances (31), average performances (35), and unusually slow performances (24). The factors that help predict record breaking and unusually slow performances are: (i) wet bulb temperature, (ii) percent sky cover, and (iii) presence or absence of a light precipitation. Record breaking performances are characterized by a wet bulb temperature of <7.8°C, and 100% sky cover. A light drizzle is also conducive to better performances. On the other hand, unusually low performances are accompanied by a wet bulb temperature of >7.8°C, and a sky cover of 50% or less. No light, precipitation was recorded on any of the unusually slow race days. A graphic analysis clearly shows these relationships to exist. In addition, a multiple regression analysis confirms the importance of these variables. The authors advise that these are reliable predictors; however, when considering marathon races held in various geographical regions and differing climatic regimes, the exact numerical thresholds used here may not apply.     

Island urbanization and its ecological consequences: A case study in the Zhoushan Island,East China

Islands, which provide multiple ecosystem services, are subject to increasing urbanization pressure due to the ongoing marine development, especially in developing countries. Insights into the island urbanization mechanism and its ecological consequences are essential to sustainable development. In the present paper, the satellite images, nighttime lights, and topographic data were integrated to characterize the spatially explicit urbanization process and mechanism during 1995–2011 in the Zhoushan Island, East China. Furthermore, the corresponding spatially explicit changes in ecosystem services, including net primary productivity (NPP), carbon sequestration and oxygen production (CSOP), nutrient cycling, crop production, and habitat quality, were quantified based on the Carnegie–Ames–Stanford Approach (CASA) and Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) models. The results showed that the Zhoushan Island had experienced a rapid urbanization over the years, with significant urban encroachment on the farmland and tidal flat. Moreover, the urban land expansion was positively correlated with that of the nighttime lights and negatively correlated with the elevation, slope, and the distance to shoreline. These indicated that the urban expansion was resulted from the enhancement of socioeconomic activities, and concentrated in the near-shore areas with low altitude and gentle slope. The urban encroachment on other land use types resulted in a decrease of 3.4 Gg C a⁻¹ NPP, 8.7 Gg a⁻¹ CSOP, 13.2 Gg a⁻¹ nutrient cycling, and 12.3 t a⁻¹ crop production, respectively. In addition, the habitat quality in 11% area of this island degraded substantially. Therefore, to achieve sustainable development of islands, it is urgent to implement more stringent policies, such as island spatial regulation, environmental impact assessment, intensive land use, and urban greening, etc.