Wilfred M. Post

Influence of climate, soil moisture, and succession on forest carbon and nitrogen cycles

The interactions between the biotic processes of reproduction, growth, and death and the abiotic processes which regulate temperature and water availability, and the interplay between the biotic and abiotic processes regulating N and light availabilities are important in the dynamics of forest ecosystems. We have developed a computer simulation that assembles a model ecosystem which links these biotic and abiotic interactions through equations that predict decomposition processes, actual evapo-transpiration, soil water balance, nutrient uptake, growth of trees, and light penetration through the canopy. The equations and parameters are derived directly from field studies and observations of forests in eastern North America, resulting in a model that can make accurate quantitative predictions of biomass accumulation, N availability, soil humus development and net primary production.

Positive feedback in natural systems

1. Introduction.- 1.1 Homeostasis.- 1.2 Positive Feedback.- 1.3 Ecological Systems with Positive Feedback.- 1.4 Generalization 1: Increasing Complexity.- 1.5 Generalization 2: Accelerating Change.- 1.6 Generalization 3: Threshold Effects.- 1.7 Generalization 4: Fragility of Complex Systems.- 1.8 Summary and Conclusions.- 2. The Mathematics of Positive Feedback.- 2.1 Graphical Analysis of a Simple Dynamic Positive Feedback System.- 2.2 A System of Two Mutualists.- 2.3 A System of Two Competitors.- 2.4 Mathematical Analysis of Positive Feedback.- 2.5 Summary and Conclusions.- 3. Physical Systems.- 3.1 The Life History of a Star.- 3.2 Geophysical Systems.- 3.3 Autocatalysis in Chemical Systems.- 3.4 Summary and Conclusions.- 4. Evolutionary Processes.- 4.1 Early Evolution of Life.- 4.2 Evolution at the Species Level.- 4.3 Coevolution.- 4.4 Summary and Conclusions.- 5. Organisms Physiology and Behaviour.- 5.1 Destructive Positive Feedback.- 5.2 Biochemical Processes in Cells and Organisms.- 5.3 Feeding and Drinking Behavior.- 5.4 Sleep.- 5.5 Movement and Motor-Sensory Relationships.- 5.6 Mind-Body Relationship.- 5.7 Summary and Conclusions.- 6. Resource Utilization by Organisms.- 6.1 Energy Allocation Tactics.- 6.2 Territorial Defense Strategies.- 6.3 Chemical Defense Strategies.- 6.4 Growth Rate Strategy.- 6.5 Summary and Conclusions.- 7. Social Behavior.- 7.1 Evolution of r- and K-strategies.- 7.2 Development of Social Strategies.- 7.3 Mating and Reproduction.- 7.4 Population Models Incorporating Sexual Reproduction.- 7.5 Small Group Dynamics.- 7.6 Castes In Insect Societies.- 7.7 Dominance Within Groups.- 7.8 Models of Group Formation and Size.- 7.9 The Schooling of Fish.- 7.10 Social Interactions and Game Theory.- 7.11 Summary and Conclusions.- 8. Mutualistic and Competitive Systems.- 8.1 Dynamics of Mutualistic Communities.- 8.2 Limits to Mutual Benefaction.- 8.3 Multi-Species Mutualism.- 8.4 Models of the Evolution of Mutualism.- 8.5 Isolation and Obligate Mutualism.- 8.6 Limited Competition.- 8.7 Summary and Conclusions.- 9. Age-Structured Populations.- 9.1 Age Structure.- 9.2 Leslie Matrices.- 9.3 Compensatory Leslie Matrices.- 9.4 Interacting Populations.- 9.5 Coexistence of Two Interacting Populations.- 9.6 Other Compensatory Models.- 9.7 Life-History Strategies.- 9.8 Intrinsic Rate of Increase.- 9.9 Reproductive Strategies.- 9.10 Summary and Conclusions.- 10. Spatially Heterogeneous Systems: Islands and Patchy Regions.- 10.1 Classical Theory of Island Biogeography.- 10.2 Island Clusters.- 10.3 Insular Reserves.- 10.4 Modeling the Patchy System.- 10.5 A Single Species in a Patchy Region.- 10.6 Time to Extinction on a Patch.- 10.7 Persistence of a Species in a Two-Patch Environment.- 10.8 Stability of a Single-Species, Two-Patch System.- 10.9 Persistence of a Species in an N-Patch Environment.- 10.10 Multi-Species, Multi-patch Systems with Competition and Mutalism.- 10.11 Persistence of a Species in a Two-Species, Two-Patch Environment.- 10.12 Persistence of a Species in an L-Species, iV-Patch Environment.- 10.13 Stability of a Two-Species, Two-Patch Model.- 10.14 Stability of an L-Species, iV-Patch Model.- 10.15 Relationship Between Reserve Design and Species Persistence.- 10.16 Summary and Conclusions.- 11. Spatially Heterogeneous Ecosystems: Pattern Formation.- 11.1 Spontaneous Emergence of Spatial Patterns.- 11.2 Diffusion Model.- 11.3 Pattern Formation Through Instability.- 11.4 Congregation of Colonial Organisms.- 11.5 Boundary Formation by Competition.- 11.6 Summary and Conclusions.- 12. Disease and Pest Outbreaks.- 12.1 Physiological Effects in the Host Species.- 12.2 Mutualistic Interactions of more than one Pathogenic Agent.- 12.3 Models of a Directly Communicated Disease or Parasite.- 12.4 Effects of Spatial Heterogeneity on Disease Outbreak Threshold Conditions.- 12.5 Design of Immunization Programs.- 12.6 Shape of the Contagion Rate Function.- 12.7 Comparison with other Spatially Heterogeneous Models.- 12.8 Host-Vector Models.- 12.9 Summary and Conclusions.- 13. The Ecosystem and Succession.- 13.1 The Ecosystem.- 13.2 Succession as a Positive Feedback Process.- 13.3 A Clementsian Model.- 13.4 Markov Chain Models.- 13.5 A Model of a Fire-Dependent System.- 13.6 Positive Feedback Loops in Ecosystems.- 13.7 Nutrient Cycling.- 13.8 Selection on the Community or Ecosystem Level.- 13.9 Summary and Conclusions.- Appendices.- Appendix A: Positive Linear Systems.- Appendix B: Stability of Positive Feedback Systems.- Appendix C: Stability of Discrete-Time Systems.- Appendix D: Positive Equilibria and Stability.- Appendix E: Comparative Statics of Positive Feedback Systems.- Appendix F: Similarity Transforms.- Appendix G: Bounds on the Roots of a Positive Linear System.- Appendix H: Relationship Between Positive Linear System Stability Criteria and the Routh-Hurwitz Criteria.- References.- Author Index.

Conversion From Agriculture To Grassland Builds Soil Organic Matter On Decadal Timescales

Soil organic matter (SOM) often increases when agricultural fields are converted to perennial vegetation, yet decadal scale rates and the mechanisms that underlie SOM accumulation are not clear. We measured SOM accumulation and changes in soil properties on a replicated chronosequence of former agricultural fields in the midwestern United States that spanned 40 years after perennial-grassland establishment. Over this time period, soil organic carbon (SOC) in the top 10 cm of soil accumulated at a constant rate of 62.0 g x m(-2) x yr(-1), regardless of whether the vegetation type was dominated by C3 or C4 grasses. At this rate, SOC contents will be equivalent to unplowed native prairie sites within 55-75 years after cultivation ceased. Both labile (short turnover time) and recalcitrant (long turnover time) carbon pools increased linearly for 40 years, with recalcitrant pools increasing more rapidly than expected. This result was consistent across several different methods of measuring labile SOC. A model that investigates the mechanisms of SOM formation suggests that rapid formation of stable carbon resulted from biochemically resistant microbial products and plant material. Former agricultural soils of the Great Plains may function as carbon sinks for less than a century, although much of the carbon stored is stable.

MONITORING AND VERIFYING CHANGES OF ORGANIC CARBON IN SOIL

Changes in soil and vegetation management can impact strongly on the rates of carbon (C) accumulation and loss in soil, even over short periods of time. Detecting the effects of such changes in accumulation and loss rates on the amount of C stored in soil presents many challenges. Consideration of the temporal and spatial heterogeneity of soil properties, general environmental conditions, and management history is essential when designing methods for monitoring and projecting changes in soil C stocks. Several approaches and tools will be required to develop reliable estimates of changes in soil C at scales ranging from the individual experimental plot to whole regional and national inventories. In this paper we present an overview of soil properties and processes that must be considered. We classify the methods for determining soil C changes as direct or indirect. Direct methods include field and laboratory measurements of total C, various physical and chemical fractions, and C isotopes. A promising direct method is eddy covariance measurement of CO2 fluxes. Indirect methods include simple and stratified accounting, use of environmental and topographic relationships, and modeling approaches. We present a conceptual plan for monitoring soil C changes at regional scales that can be readily implemented. Finally, we anticipate significant improvements in soil C monitoring with the advent of instruments capable of direct and precise measurements in the field as well as methods for interpreting and extrapolating spatial and temporal information.

THE USE OF MODELS TO INTEGRATE INFORMATION AND UNDERSTANDING OF SOIL C AT THE REGIONAL SCALE

Abstract Regional analysis of ecosystem properties, including soil C, is a rapidly developing area of research. Regional analyses are being used to quantify existing soil C stocks, predict changes in soil C as a function of changing landuse patterns, and assess possible responses to climate change. The tools necessary for such analyses are simulation models coupled with spatially-explicit databases of vegetation, soils, topography, landuse and climate. A general framework for regional analyses which integrates models with site-specific and spatially-resolved data is described. Two classes of models are currently being used for analyses at regional scales, ecosystem-level models, which were originally designed for local scale studies, and more aggregated “macro-scale” models developed for continental and global scale applications. A consideration in applying both classes of models is the need to minimize errors associated with aggregating information to apply to coarser spatial and temporal scales. For model input data, aggregation bias is most severe for variables which enter into non-linear model functions, such as soil textural effects on organic matter decomposition and water balance or the temperature response of decomposer organisms. Aggregation of model structure also needs to be considered, particularly for macro-scale models. For example, representations of litter and soil organic matter by only one or two pools may be suitable for representing equilibrium conditions but rates of change will tend to be overestimated for transient-state conditions using highly aggregated models. Geographic soils data, derived from field surveys, are a key component for regional analyses. Issues of data quality and interpretation of soil survey data are discussed in the context of regional analyses of soil C. Areas for further development of data and modeling capabilities, including refining soil C maps, developing spatial databases on landuse and management practices, using remotely sensed data in regional model applications, and linking terrestrial ecosystem models with global climate models, are discussed.

Enhancement of Carbon Sequestration in US Soils

Abstract Improved practices in agriculture, forestry, and land management could be used to increase soil carbon and thereby significantly reduce the concentration of atmospheric carbon dioxide. Understanding biological and edaphic processes that increase and retain soil carbon can lead to specific manipulations that enhance soil carbon sequestration. These manipulations, however, will only be suitable for adoption if they are technically feasible over large areas, economically competitive with alternative measures to offset greenhouse gas emissions, and environmentally beneficial. Here we present the elements of an integrated evaluation of soil carbon sequestration methods.

The potential response of terrestrial carbon storage to changes in climate and atmospheric CO2

We use a georeferenced model of ecosystem carbon dynamics to explore the sensitivity of global terrestrial carbon storage to changes in atmospheric CO2 and climate. We model changes in ecosystem carbon density, but we do not model shifts in vegetation type. A model of annual NPP is coupled with a model of carbon allocation in vegetation and a model of decomposition and soil carbon dynamics. NPP is a function of climate and atmospheric CO2 concentration. The CO2 response is derived from a biochemical model of photosynthesis. With no change in climate, a doubling of atmospheric CO2 from 280 ppm to 560 ppm enhances equilibrium global NPP by 16.9%; equilibrium global terrestrial ecosystem carbon (TEC) increases by 14.9%. Simulations with no change in atmospheric CO2 concentration but changes in climate from five atmospheric general circulation models yield increases in global NPP of 10.0–14.8%. The changes in NPP are very nearly balanced by changes in decomposition, and the resulting changes in TEC range from an increase of 1.1% to a decrease of 1.1%. These results are similar to those from analyses using bioclimatic biome models that simulate shifts in ecosystem distribution but do not model changes in carbon density within vegetation types. With changes in both climate and a doubling of atmospheric CO2, our model generates increases in NPP of 30.2–36.5%. The increases in NPP and litter inputs to the soil more than compensate for any climate stimulation of decomposition and lead to increases in global TEC of 15.4–18.2%.

Climate controls on forest soil C isotope ratios in the Southern Appalachian Mountains

A large portion of terrestrial carbon (C) resides in soil organic carbon (SOC). The dynamics of this large reservoir depend on many factors, including climate. Measurements of {sup 13}C:{sup 12}C ratios, C concentrations, and C:N ratios at six forest sites in the Southern Appalachian Mountains (USA) were used to explore several hypotheses concerning the relative importance of factors that control soil organic matter (SOM) decomposition and SOC turnover. Mean {delta}{sup 13}C values increased with soil depth and decreasing C concentrations along a continuum from fresh litter inputs to more decomposed soil constituents. Data from the six forest sites, in combination with data from a literature review, indicate that the extent of change in {delta}{sup 13}C values from forest litter inputs to mineral soil (20 cm deep) is significantly associated with mean annual temperature. The findings support a conceptual model of vertical changes in forest soil {delta}{sup 13}C values, C concentrations, and C:N ratios that are interrelated through climate controls on decomposition. We hypothesize that, if other environmental factors (like soil moisture) are not limiting, then temperature and litter quality indirectly control the extent of isotopic fractionation during SOM decomposition in temperate forest ecosystems.

Nitrogen attenuation of terrestrial carbon cycle response to global environmental factors

[1] Nitrogen cycle dynamics have the capacity to attenuate the magnitude of global terrestrial carbon sinks and sources driven by CO2 fertilization and changes in climate. In this study, two versions of the terrestrial carbon and nitrogen cycle components of the Integrated Science Assessment Model (ISAM) are used to evaluate how variation in nitrogen availability influences terrestrial carbon sinks and sources in response to changes over the 20th century in global environmental factors including atmospheric CO2 concentration, nitrogen inputs, temperature, precipitation and land use. The two versions of ISAM vary in their treatment of nitrogen availability: ISAM-NC has a terrestrial carbon cycle model coupled to a fully dynamic nitrogen cycle while ISAM-C has an identical carbon cycle model but nitrogen availability is always in sufficient supply. Overall, the two versions of the model estimate approximately the same amount of global mean carbon uptake over the 20th century. However, comparisons of results of ISAM-NC relative to ISAM-C reveal that nitrogen dynamics: (1) reduced the 1990s carbon sink associated with increasing atmospheric CO2 by 0.53 PgC yr 1 (1 Pg = 10 15 g), (2) reduced the 1990s carbon source associated with changes in temperature and precipitation of 0.34 PgC yr 1 in the 1990s, (3) an enhanced sink associated with nitrogen inputs by 0.26 PgC yr 1 , and (4) enhanced the 1990s carbon source associated with changes in land use by 0.08 PgC yr 1 in the 1990s. These effects of nitrogen limitation influenced the spatial distribution of the estimated exchange of CO2 with greater sink activity in high latitudes associated with climate effects and a smaller sink of CO2 in the southeastern United States caused by N limitation associated with both CO2 fertilization and forest regrowth. These results indicate that the dynamics of nitrogen availability are important to consider in assessing the spatial distribution and temporal dynamics of terrestrial carbon sources and sinks.

Development of microbial-enzyme-mediated decomposition model parameters through steady-state and dynamic analyses

We developed a microbial-enzyme-mediated decomposition (MEND) model, based on the Michaelis-Menten kinetics, that describes the dynamics of physically defined pools of soil organic matter (SOC). These include particulate, mineral-associated, dissolved organic matter (POC, MOC, and DOC, respectively), microbial biomass, and associated exoenzymes. The ranges and/or distributions of parameters were determined by both analytical steady-state and dynamic analyses with SOC data from the literature. We used an improved multi-objective parameter sensitivity analysis (MOPSA) to identify the most important parameters for the full model: maintenance of microbial biomass, turnover and synthesis of enzymes, and carbon use efficiency (CUE). The model predicted that an increase of 2 degrees C (baseline temperature 12 degrees C) caused the pools of POC-cellulose, MOC, and total SOC to increase with dynamic CUE and decrease with constant CUE, as indicated by the 50% confidence intervals. Regardless of dynamic or constant CUE, the changes in pool size of POC, MOC, and total SOC varied from -8% to 8% under +2 degrees C. The scenario analysis using a single parameter set indicates that higher temperature with dynamic CUE might result in greater net increases in both POC-cellulose and MOC pools. Different dynamics of various SOC pools reflected the catalytic functions of specific enzymes targeting specific substrates and the interactions between microbes, enzymes, and SOC. With the feasible parameter values estimated in this study, models incorporating fundamental principles of microbial-enzyme dynamics can lead to simulation results qualitatively different from traditional models with fast/slow/passive pools.