Scale and uncertainty in modeled soil organic carbon stock changes for US croplands using a process‐based model

Process‐based model analyses are often used to estimate changes in soil organic carbon (SOC), particularly at regional to continental scales. However, uncertainties are rarely evaluated, and so it is difficult to determine how much confidence can be placed in the results. Our objective was to quantify uncertainties across multiple scales in a process‐based model analysis, and provide 95% confidence intervals for the estimates. Specifically, we used the Century ecosystem model to estimate changes in SOC stocks for US croplands during the 1990s, addressing uncertainties in model inputs, structure and scaling of results from point locations to regions and the entire country. Overall, SOC stocks increased in US croplands by 14.6 Tg C yr^?1 from 1990 to 1995 and 17.5 Tg C yr^?1 during 1995 to 2000, and uncertainties were ±22% and ±16% for the two time periods, respectively. Uncertainties were inversely related to spatial scale, with median uncertainties at the regional scale estimated at ±118% and ±114% during the early and latter part of 1990s, and even higher at the site scale with estimates at ±739% and ±674% for the time periods, respectively. This relationship appeared to be driven by the amount of the SOC stock change; changes in stocks that exceeded 200 Gg C yr^?1 represented a threshold where uncertainties were always lower than ±100%. Consequently, the amount of uncertainty in estimates derived from process‐based models will partly depend on the level of SOC accumulation or loss. In general, the majority of uncertainty was associated with model structure in this application, and so attaining higher levels of precision in the estimates will largely depend on improving the model algorithms and parameterization, as well as increasing the number of measurement sites used to evaluate the structural uncertainty.     

Soil organic carbon stock change due to land use activity along the agricultural frontier of the southwestern Amazon,Brazil, between 1970 and 2002

The southwestern portion of the Brazilian Amazon arguably represents the largest agricultural frontier in the world, and within this region the states of Rondônia and Mato Grosso have about 24% and 32% of their respective areas under agricultural management, which is almost half of the total area deforested in the Brazilian Amazon biome. Consequently, it is assumed that deforestation in this region has caused substantial loss of soil organic carbon (SOC). In this study, the changes in SOC stocks due to the land use change and management in the southwestern Amazon were estimated for two time periods from 1970–1985 and 1985–2002. An uncertainty analysis was also conducted using a Monte Carlo approach. The results showed that mineral soils converted to agricultural management lost a total of 5.37 and 3.74 Tg C yr^?1 between 1970–1985 and 1985–2002, respectively, along the Brazilian Agricultural Frontier in the states of Mato Grosso and Rondônia. Uncertainties in these estimates were ±37.3% and ±38.6% during the first and second time periods, respectively. The largest sources of uncertainty were associated with reference carbon (C) stocks, expert knowledge surveys about grassland condition, and the management factors for nominal and degraded grasslands. These results showed that land use change and management created a net loss of C from soils, however, the change in SOC stocks decreased substantially from the first to the second time period due to the increase in land under no‐tillage.     

A hierarchical Bayesian approach for estimation of photosynthetic parameters of C3 plants

We describe a hierarchical Bayesian (HB) approach to fitting the Farquhar et al. model of photosynthesis to leaf gas exchange data. We illustrate the utility of this approach for estimating photosynthetic parameters using data from desert shrubs. Unique to the HB method is its ability to simultaneously estimate plant- and species-level parameters, adjust for peaked or non-peaked temperature dependence of parameters, explicitly estimate the 'critical' intracellular [CO₂] marking the transition between ribulose 1·5-bisphosphate carboxylase/oxygenase (Rubisco) and ribulose-1,5-bisphosphate (RuBP) limitations, and use both light response and CO₂ response curve data to better inform parameter estimates. The model successfully predicted observed photosynthesis and yielded estimates of photosynthetic parameters and their uncertainty. The model with peaked temperature responses fit the data best, and inclusion of light response data improved estimates for day respiration ( R _d). Species differed in R _d25 ( R _d at 25 °C), maximum rate of electron transport ( J _max25), a Michaelis–Menten constant ( K _c25) and a temperature dependence parameter (Δ S ). Such differences could potentially reflect differential physiological adaptations to environmental variation. Plants differed in R _d25, J _max25, mesophyll conductance ( g _m25) and maximum rate of Rubisco carboxylation ( V _cmax25). These results suggest that plant- and species-level variation should be accounted for when applying the Farquhar et al. model in an inferential or predictive framework.     

Quantitative studies of the post-penetration phase of infection by Puccinia graminis tritici

Data obtained during the first 120 h after several resistant and susceptible varieties of wheat and some non-host species were inoculated with uredio-spores of Puccinia graminis tritici provided further evidence in support of the suggestion that hypersensitive necrosis is a consequence, and not the cause, of resistance. No evidence was obtained that individual genes for stem-rust resistance specifically influenced colony growth or the histological changes that occurred during infection. However, combinations of major resistance genes or the presence of minor genes for resistance apparently did affect colony growth and hypersensitive cell collapse. Three groups of varieties – resistant, intermediate and susceptible – were distinguished on the basis of colony growth and the amount and proportion of necrotic tissue associated with colony development. The boundary between the intermediate and susceptible groups was not as distinct as that between the intermediate and resistant groups.     

A review of the current (1992) state of our knowledge concerning reproduction in open thelycum Penaeid shrimp with emphasis on Penaeus vannamei

Summary

The current state of knowledge relating to the reproduction of the commercially important open thelycum shrimps (Crustacea: Penaeidae) is reviewed with particular reference to Penaeus vannamei. Industry standard procedures for the breeding of shrimps in captivity are reviewed against a background of knowledge concerning the maturation processes in males and females. The factors controlling maturation are not completely understood, and it is not clear whether maturation of females is size-or age-dependent. Although unilateral eyestalk ablation is routinely used to induce maturation, the physiological mechanism of this operation is not properly understood. Further research on environmental conditions for successful maturation is required. Nutrition is an important component in successful artificial breeding programs that still rely on the provision of natural foods (squid and polychaeta). The processes of oocyte formation, mating, spawning and hatching are discussed in relation to observations made at the Gulf Coast Research Laboratory.     

Bias and variance in model results associated with spatial scaling of measurements for parameterization in regional assessments

Models are central to global change analyses, but they are often parameterized using data that represent only a portion of heterogeneity in a region. This creates uncertainty in the results and constrains the reliability of model inferences. Our objective was to evaluate the uncertainty associated with differential scaling of parameterization data to model soil organic carbon stock changes as a function of US agricultural land use and management. Specifically, we compared analyses in which model parameters were derived from field experimental data that were scaled to the entire US vs. the same data scaled to climate regions within the country. We evaluated the effect of differential scaling on both bias and variance in model results. Model results had less variance by scaling data to the entire country because of a larger sample size for deriving individual parameter values, although there was a relatively large bias associated with this parameterization, estimated at 2.7 Tg C yr^?1. Even with the large bias, resulting confidence intervals from the two parameterizations had considerable overlap for the estimated national rate of SOC change (i.e. 77% overlap in those intervals). Consequently, the results were relatively similar when focusing on the uncertainty rather than solely on the mean estimate. In contrast, large biases created less overlap in confidence intervals for the change rates within individual climate regions, compared with the national estimates. For example, the overlap in resulting intervals from the two parameterizations was only 32% for the warm temperate moist region, with a corresponding bias of 3.1 Tg C yr^?1. These findings demonstrate that there is a greater risk of making erroneous inferences because of large biases if models are parameterized with broader scale information, such as an entire country, and then used to address impacts at a finer spatial scale, such as sub‐regions within a country. In addition, the study demonstrates a trade‐off between variance and bias in model results that depends on the scaling of data for model parameterization.     

Physiological responses of two contrasting desert plant species to precipitation variability are differentially regulated by soil moisture and nitrogen dynamics

Alterations in global and regional precipitation patterns are expected to affect plant and ecosystem productivity, especially in water‐limited ecosystems. This study examined the effects of natural and supplemental (25% increase) seasonal precipitation on a sotol grassland ecosystem in Big Bend National Park in the Chihuahuan Desert. Physiological responses – leaf photosynthesis at saturating light (A_sat), stomatal conductance (g_s), and leaf nitrogen [N] – of two species differing in their life form and physiological strategies (Dasylirion leiophyllum, a C₃ shrub; Bouteloua curtipendula, a C₄ grass) were measured over 3 years (2004–2006) that differed greatly in their annual and seasonal precipitation patterns (2004: wet, 2005: average, 2006: dry). Precipitation inputs are likely to affect leaf‐level physiology through the direct effects of altered soil water and soil nitrogen. Thus, the effects of precipitation, watering treatment, soil moisture, and nitrogen were quantified via multivariate hierarchical Bayesian models that explicitly linked the leaf and soil responses. The two species differed in their physiological responses to precipitation and were differentially controlled by soil water vs. soil nitrogen. In the relatively deeply rooted C₃ shrub, D. leiophyllum, A_sat was highest in moist periods and was primarily regulated by deep (16–30 cm) soil water. In the shallow‐rooted C₄ grass, B. curtipendula, A_sat was only coupled to leaf [N], both of which increased in dry periods when soil [N] was highest. Supplemental watering during the wet year generally decreased A_sat and leaf [N] in D. leiophyllum, perhaps due to nutrient limitation, and physiological responses in this species were influenced by the cumulative effects of 5 years of supplemental watering. Both species are common in this ecosystem and responded strongly, yet differently, to soil moisture and nitrogen, suggesting that changes in the timing and magnitude of precipitation may have consequences for plant carbon gain, with the potential to alter community composition.