Oleogustus: The Unique Taste of Fat

Considerable mechanistic data indicate there may be a sixth basic taste: fat. However, evidence demonstrating that the sensation of non-esterified fatty acids (the proposed stimuli for “fat taste”) differs qualitatively from other tastes is lacking. Using perceptual mapping, we demonstrate that medium and long-chain non-esterified fatty acids have a taste sensation that is distinct from other basic tastes (sweet, sour, salty, and bitter). While some overlap was observed between these NEFA and umami taste, this overlap is likely due to unfamiliarity with umami sensations rather than true similarity. Shorter chain fatty acids stimulate a sensation similar to sour, but as chain length increases this sensation changes. Fat taste oral signaling, and the different signals caused by different alkyl chain lengths, may hold implications for food product development, clinical practice, and public health policy

What does Remote Sensing Do for Ecology?

The phrase “remote sensing” sounds like a theoretician’s delight—a way to get data while sitting in an armchair. Unfortunately, while some remote sensing activities can be done in a chair, substantial legwork is also needed to ensure accurate interpretation o f remotely sensed signals. Even for the work done from the armchair, remote sensing analysis is far from sim ple and straightforward

A Physiology-Based Gap Model of Forest Dynamics

A computer model of forest growth and ecosystem processes is presented. The model, HYBRID, is derived from a forest gap model, an ecosystem process model, and a photosynthesis model. In HYBRID individual trees fix and respire carbon, and lose water daily; carbon partioning occurs at the end of each year. HYBRID obviates many of the linitations of both gap models and ecosystem process models. The growth equations of gap models are replaced with functionally realistic equations and processes for carbon fixation and partitioning, resulting in a dynamic model in which competition and physiology play important roles. The model is used to predict ecosystem processes and dynamics in oak forests in Knoxville, Tennessee (USA), and pine forests in Missoula, Montana (USA) between the years 1910 and 1986. The simulated growth of individual trees and the overall ecosystems—level processes are very similiar to observations. A sensitivity analysis performed for these sites showed that predictions of net primary productivity by HBRID are most sensitive to the ratio of CO2 partial pressure between inside the leaf and the air, relative humidity, ambient CO2 partial pressure, precipitation, air temperature, tree allometry, respiration parametes, site soil water capacity, and a carbon storage parameter

BIOME-BGC simulations of stand hydrologic process for BOREAS

BIOME-BGC is a general ecosystem model designed to simulate hydrologic and biogeochemical processes across multiple scales. The objectives of this investigation were to compare BIOME-BGC estimates of hydrologic processes with observed data for different boreal forest stands and investigate factors that control simulated water fluxes. Model results explained 62 and 98% of the respective variances in observed daily evapotranspiration and soil water; simulations of the onset of spring thaw and the dates of snowpack disappearance and accumulation also generally tracked observations. Differences between observed and simulated evapotranspiration were attributed to model assumptions of constant, growing season, overstory leaf areas that did not account for phenological changes and understory effects on stand daily water fluxes. Vapor pressure deficit and solar radiation accounted for 58–74% of the variances in simulated daily evapotranspiration during the growing season, though low air temperature and photosynthetic light levels were found to be the major limiting factors regulating simulated canopy conductances to water vapor. Humidity and soil moisture were generally not low enough to induce physiological water stress in black spruce stands, though low soil water potentials resulted in approximate 34% reductions in simulated mean daily canopy conductances for aspen and jack pine stands. The sensitivity of evapotranspiration simulations to leaf area (LAI) was less than expected because of opposing responses of transpiration and evaporation to LAI. The results of this investigation identify several components within boreal forest stands that are sensitive to climate change

A continental phenology model for monitoring vegetation responses to interannual climatic variability

Regional phenology is important in ecosystem simulation models and coupled biosphere/atmosphere models. In the continental United States, the timing of the onset of greenness in the spring (leaf expansion, grass green-up) and offset of greenness in the fall (leaf abscission, cessation of height growth, grass brown-off) are strongly influenced by meteorological and climatological conditions. We developed predictive phenology models based on traditional phenology research using commonly available meteorological and climatological data. Predictions were compared with satellite phenology observations at numerous 20 km × 20 km contiguous landcover sites. Onset mean absolute error was 7.2 days in the deciduous broadleaf forest (DBF) biome and 6.1 days in the grassland biome. Offset mean absolute error was 5.3 days in the DBF biome and 6.3 days in the grassland biome. Maximum expected errors at a 95% probability level ranged from 10 to 14 days. Onset was strongly associated with temperature summations in both grassland and DBF biomes; DBF offset was best predicted with a photoperiod function, while grassland offset required a combination of precipitation and temperature controls. A long-term regional test of the DBF onset model captured field-measured interannual variability trends in lilac phenology. Continental application of the phenology models for 1990–1992 revealed extensive interannual variability in onset and offset. Median continental growing season length ranged from a low of 129 days in 1991 to a high of 146 days in 1992. Potential uses of the models include regulation of the timing and length of the growing season in large-scale biogeochemical models and monitoring vegetation response to interannual climatic variability

Parameterization and Sensitivity Analysis of the BIOME-BGC Terrestrial Ecosystem model: Net Primary Production Controls

Ecosystem simulation models use descriptive input parameters to establish the physiology, biochemistry, structure, and allocation patterns of vegetation functional types, or biomes. For single-stand simulations it is possible to measure required data, but as spatial resolution increases, so too does data unavailability. Generalized biome parameterizations are then required. Undocumented parameter selection and unknown model sensitivity to parameter variation for larger-resolution simulations are currently the major limitations to global and regional modeling. The authors present documented input parameters for a process-based ecosystem simulation model, BIOME–BGC, for major natural temperate biomes. Parameter groups include the following: turnover and mortality; allocation; carbon to nitrogen ratios (C:N); the percent of plant material in labile, cellulose, and lignin pools; leaf morphology; leaf conductance rates and limitations; canopy water interception and light extinction; and the percent of leaf nitrogen in Rubisco (ribulose bisphosphate-1,5-carboxylase/oxygenase) (PLNR). Using climatic and site description data from the Vegetation/Ecosystem Modeling and Analysis Project, the sensitivity of predicted annual net primary production (NPP) to variations in parameter level of ± 20% of the mean value was tested. For parameters exhibiting a strong control on NPP, a factorial analysis was conducted to test for interaction effects. All biomes were affected by variation in leaf and fine root C:N. Woody biomes were additionally strongly controlled by PLNR, maximum stomatal conductance, and specific leaf area while nonwoody biomes were sensitive to fire mortality and litter quality. None of the critical parameters demonstrated strong interaction effects. An alternative parameterization scheme is presented to better represent the spatial variability in several of these critical parameters. Patterns of general ecological function drawn from the sensitivity analysis are discussed

3-PG Productivity Modeling of Regenerating Amazon Forests: Climate Sensitivity and Comparison with MODIS-Derived NPP

Potential forest growth predicted by the Physiological Principles in Predicting Growth (3-PG) model was compared for forest and deforested areas in the Legal Amazon to assess potential differing regeneration associated with climate. Historical deforestation and regeneration have occurred in environmentally marginal areas that influence regional carbon sequestration estimates. Effects of El Niño–induced drought further reduce simulated production by decreasing soil water availability in areas with shallow soils and high transpiration potential. The model was calibrated through comparison of literature biomass and with satellite-based estimates. Net primary productivity (NPP) for mature Amazonian forests from the 3-PG model was positively correlated (r 2 = 0.77) with a Moderate Resolution Imaging Spectroradiometer (MODIS)-derived algorithm, though with some bias. Annual total NPP for the study area using a 1961–90 average climatology was 4.6 Pg C yr−1, which decreased to 4.2 Pg C yr−1 when simulated with climate from the severe 1997/98 El Niño event. From a regional analysis, results showed that biomass accumulation is almost entirely controlled by the availability of soil water. Also, areas currently forested in the eastern Amazon are more sensitive to extreme El Niño–induced drought than southern areas with the greatest deforestation extent

Remote Sensing of Coniferous Forest Leaf Area

Many important ecological questions concern ecosystem processes occurring over large areas. However, our understanding o f ecosystem functions is derived primarily from research executed on small, intensively studied sites, and extrapolation to large areas is difficult

Global vegetation cover changes from coarse resolution satellite data

Land cover plays a key role in various biophysical processes related to global climate and terrestrial biogeochemistry. Although global land cover has dramatically changed over the last few centuries, until now there has been no consistent way of quantifying the changes globally. In this study we used long-term climate and soils data along with coarse resolution satellite observations to quantify the magnitude and spatial extent of large-scale land cover changes attributable to anthropogenic processes. Differences between potential leaf area index (LAI), derived from climate-soil-leaf area equilibrium, and actual leaf area index obtained from satellite data are used to estimate changes in land cover. Further, changes in LAI between potential and actual conditions are linked to climate by expressing them as possible changes in radiometric surface temperatures (Tr) resulting from changes in surface energy partitioning. As expected, areas with high population densities, such as India, China, and western Europe showed large reductions in LAI. Changes in global land cover expressed as summer, midafternoon Tr, ranged from −8° to +16°C. Deforestation resulted in an increase in Tr, while irrigated agriculture reduced the Tr. Many of the current general circulation models (GCMs) use potential vegetation maps to represent global vegetation. Our results indicate that there are widespread changes in global land cover due to deforestation and agriculture below the resolution of many GCMs, and these changes could have a significant impact on climate. Potential and actual LAI data sets are available for climate modelers at 0.5° × 0.5° resolution to study the possible impacts of land cover changes on global temperatures and circulation patterns

Satellite assessment of land surface evapotranspiration for the pan-Arctic domain

Regional evapotranspiration (ET), including water loss from plant transpiration and soil evaporation, is essential to understanding interactions between land-atmosphere surface energy and water balances. Vapor pressure deficit (VPD) and surface air temperature are key variables for stomatal conductance and ET estimation. We developed an algorithm to estimate ET using the Penman-Monteith approach driven by Moderate Resolution Imaging Spectroradiometer (MODIS)-derived vegetation data and daily surface meteorological inputs including incoming solar radiation, air temperature, and VPD. The model was applied using alternate daily meteorological inputs, including (1) site level weather station observations, (2) VPD and air temperature derived from the Advanced Microwave Scanning Radiometer (AMSR-E) on the EOS Aqua satellite, and (3) Global Modeling and Assimilation Office (GMAO) reanalysis meteorology-based surface air temperature, humidity, and solar radiation data. Model performance was assessed across a North American latitudinal transect of six eddy covariance flux towers representing northern temperate grassland, boreal forest, and tundra biomes. Model results derived from the three meteorology data sets agree well with observed tower fluxes (r \u3e 0.7; P \u3c 0.003; root mean square error of latent heat flux \u3c30 W m−2) and capture spatial patterns and seasonal variability in ET. The MODIS-AMSR-E–derived ET results also show similar accuracy to ET results derived from GMAO, while ET estimation error was generally more a function of algorithm parameterization than differences in meteorology drivers. Our results indicate significant potential for regional mapping and monitoring daily land surface ET using synergistic information from satellite optical IR and microwave remote sensing