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Models represent our primary method for integration of small-scale, process-level phenomena into a comprehensive description of forest-stand or ecosystem function. They also represent a key method for testing hypotheses about the response of forest ecosystems to multiple changing environmental conditions. This paper describes the evaluation of 13 stand-level models varying in their spatial, mechanistic, and temporal complexity for their ability to capture intra- and interannual components of the water and carbon cycle for an upland, oak-dominated forest of eastern Tennessee. Comparisons between model simulations and observations were conducted for hourly, daily, and annual time steps. Data for the comparisons were obtained from a wide range of methods including: eddy covariance, sapflow, chamber-based soil respiration, biometric estimates of stand-level net primary production and growth, and soil water content by time or frequency domain reflectometry. Response surfaces of carbon and water flux as a function of environmental drivers, and a variety of goodness-of-fit statistics (bias, absolute bias, and model efficiency) were used to judge model performance.A single model did not consistently perform the best at all time steps or for all variables considered. Intermodel comparisons showed good agreement for water cycle fluxes, but considerable disagreement among models for predicted carbon fluxes. The mean of all model outputs, however, was nearly always the best fit to the observations. Not surprisingly, models missing key forest components or processes, such as roots or modeled soil water content, were unable to provide accurate predictions of ecosystem responses to short-term drought phenomenon. Nevertheless, an inability to correctly capture short-term physiological processes under drought was not necessarily an indicator of poor annual water and carbon budget simulations. This is possible because droughts in the subject ecosystem were of short duration and therefore had a small cumulative impact. Models using hourly time steps and detailed mechanistic processes, and having a realistic spatial representation of the forest ecosystem provided the best predictions of observed data. Predictive ability of all models deteriorated under drought conditions, suggesting that further work is needed to evaluate and improve ecosystem model performance under unusual conditions, such as drought, that are a common focus of environmental change discussions.Read More: http://www.esajournals.org/doi/abs/10.1890/03-4049

We investigated the foraging distribution of 2 species of murres (Uria lomvia and U. aalge) in relation to Acoustically Determined Biomass (ADB) and hydrographic structure surrounding the Pribilof Islands, Bering Sea, Alaska, USA. The distribution and abundance of both murre species were similar; therefore, we combined data for thick-billed and common murres in the analysis. We found that murre densities were higher in frontal regions than in non frontal regions. Maximum ADB was also found to be elevated in the frontal zone. Within frontal regions, high densities of murres were associated with regions containing relatively high levels of ADB. In contrast, we did not find significant correlations between murre density and ADB in the study area as a whole. We postulate that murres restrict their foraging to the frontal region because prey can be located more predictably within this zone than in other areas in the vicinity of the Pribilof Islands.

This article proposes a simple new logic for classifying global vegetation. The critical features of this classification are that 1) it is based on simple, observable, unambiguous characteristics of vegetation structure that are important to ecosystem biogeochemistry and can be measured in the field for validation, 2) the structural characteristics are remotely sensible so that repeatable and efficient global reclassifications of existing vegetation will be possible, and 3) the defined vegetation classes directly translate into the biophysical parameters of interest by global climate and biogeochemical models. A first test of this logic for the continental United States is presented based on an existing 1 km AVHRR normalized difference vegetation index database. Procedures for solving critical remote sensing problems needed to implement the classification are discussed. Also, some inferences from this classification to advanced vegetation biophysical variables such as specific leaf area and photosynthetic capacity useful to global biogeochemical modeling are suggested.

Soil temperature is a necessary component for estimating below-ground processes for continental and global carbon budgets; however, there are an insufficient number of climatic stations monitoring soil temperature. We used a n 11-day running average of daily mean air temperature to estimate daily mean soil temperature at a depth of 10 cm using linear regression. This model was tested using data from 6 climate reg~ons across the United States. Frequency analyses for 17 of 19 data sets showed that the number of days which were w~thin a k3.5OC range centered on the measured sol1 temperature va r~ed from 77 to 96%. The values of R2 between observed and final predicted soil temperatures ranged from 0.85 to 0.96 with standard errors from 1.5 to 2.9"C for all 19 simulations. Changes of soil temperature under snow cover were smaller than those without snow cover. Soil temperature under vegetation cover was also simulated assuming the rate of soil warming under vegetation cover would be reduced with increasing leaf area index according to the Beer-Lambert Law. Annual soil respiration can be estimated from the predicted soil temperature with reasonable accuracy. Daily soil temperature may be predicted from daily air temperature once regional equations have been established, because weather stations in the United States can be generalized into a few regions and sites wthin each region may use the same equation.

Global environmental change, which includes climate change, biodiversity loss, changes in hydrological and biogeochemical cycles, and intensive exploitation of natural resources, is having significant impacts on the world's oceans. This book advances knowledge of the structure and functioning of marine ecosystems, and their past, present, and future responses to physical and anthropogenic forcing. The book illustrates how climate and humans impact marine ecosystems by a comprehensive review of the physical and ecological processes that structure marine ecosystems and the observation, experimentation, and modelling approaches required for their study. Recognizing the interactive roles played by humans in using marine resources and in responding to global changes in marine systems, the book includes chapters on the human dimensions of marine ecosystem changes and on effective management approaches in the era of change. Part IV reviews the state of the art in predicting the responses of marine ecosystems to future global change scenarios. The book provides a synthesis of the work conducted under the auspices of the Global Ocean Ecosystems Dynamics (GLOBEC) project over the last two decades, as the largest, multi-disciplinary, international effort focused on understanding the impacts of external forcing on the structure and dynamics of global marine ecosystems.