| 1. Domain | 1.1 Scope of the Domain | Boundaries | The range of phenomena the science includes and excludes. | Focuses on the flows of energy, matter, and nutrients through biological communities and their physical environment. Includes productivity, decomposition, trophic energy transfer, nutrient cycling, carbon/water fluxes, and biogeochemical processes. Excludes individual behavior, species-level interactions, and fine-scale physiology except when directly influencing ecosystem-scale fluxes. |
| | Scale | The spatial, temporal, or organizational level at which the science operates (e.g., quantum, cellular, social, cosmic). | Operates across spatial scales from local ecosystems (lakes, forests, reefs) to landscapes and biomes, and temporal scales from diel cycles to centuries. Integrates biological, chemical, and physical processes across whole systems. |
| 1.2 Ontological Commitments | Entities | The kinds of things assumed to exist within the domain (particles, organisms, agents, fields, etc.). | Energy pools, nutrient pools, primary producers, consumers, decomposers, detritus, soil organic matter, abiotic reservoirs, flux pathways, water and carbon flows, and ecosystem compartments. |
| | Properties | The fundamental attributes these entities possess (mass, charge, genotype, preference, etc.). | Productivity, energy flow rates, nutrient concentrations, decomposition rates, assimilation efficiency, turnover time, stoichiometry, storage capacity, and stability/resilience of pools and fluxes. |
| | Categories | The basic ontological types used to classify domain elements (substances, processes, relations, structures). | Trophic levels, biogeochemical cycles, ecosystem types, energy pathways, nutrient pools (organic/inorganic), flux types (input/output/internal), and physical compartments (soil, water, atmosphere). |
| 1.3 State-Variables | Variables | The measurable or definable properties that describe system conditions. | Biomass, primary productivity (GPP/NPP), respiration rates, nutrient pool sizes, carbon and nitrogen fluxes, water availability, soil moisture, decomposition rates, and trophic transfer efficiencies. |
| | Parameterization | How variables encode and represent the system’s state. | State encoded through ecosystem budgets, carbon/nutrient-flow models, productivity measurements, stoichiometric ratios, mass-balance equations, and continuous environmental monitoring data. |
| 1.4 Admissible Idealizations | Simplifications | Conceptual reductions used to make the domain tractable (point masses, rational agents, perfect gases). | Treating trophic levels as homogeneous, linearizing nutrient fluxes, assuming steady-state conditions, reducing complex food webs to simplified pathways, or treating abiotic pools as well-mixed. |
| | Validity Conditions | The limits and contexts in which idealizations hold or break down. | Simplifications fail under strong temporal variability, spatial heterogeneity, extreme disturbances, nonlinear feedbacks, or context-dependent recycling processes. |
| 1.5 Domain Assumptions | Structural Assumptions | Background ontological stances such as determinism, continuity, randomness, discreteness. | Assumes conservation of energy and matter, predictable biogeochemical pathways, consistent trophic transfer rules, and measurable ecosystem boundaries for flux accounting. |
| | Implicit Commitments | Unstated but necessary assumptions that shape the field’s conceptual structure. | Assumes ecosystems can be treated as integrated units, fluxes are quantifiable and traceable, trophic pathways are interpretable, and mass-balance principles apply across scales. |
| 1.6 Internal Coherence Requirements | Consistency | The demand that domain concepts do not contradict one another. | Energy-flow models, nutrient budgets, productivity measurements, and flux observations must align without contradiction across space, time, and environmental contexts. |
| | Compatibility | The requirement that entities, variables, and assumptions fit together into a unified descriptive framework. | Entities (pools, fluxes, trophic groups), variables (productivity, storage, turnover), and assumptions (mass/energy conservation, predictable cycling) must fit together into a unified whole-system explanatory framework. |
| 2. Evidence Layer | 2.1 Observable Phenomena | Observables | The aspects of the domain that can produce detectable signals accessible to measurement. | Detectable signals include biomass levels, primary productivity, respiration rates, nutrient concentrations, decomposition activity, carbon/water fluxes, trophic-flow metrics, and changes in pool sizes across time. |
| | Detection Limits | The boundaries of what can be resolved or sensed by current instruments or methods. | Minimum detectable change in biomass, lowest measurable nutrient concentration, sensitivity thresholds for CO₂ or O₂ flux sensors, decomposition-rate detection limits, and spatial/temporal limits of remote sensing. |
| 2.2 Measurement Systems | Units | Standardized quantifications (meters, seconds, volts, decibels, dollars, etc.) necessary for consistent comparison. | Biomass (g/m²), productivity (g C/m²/yr), nutrient concentration (mg/L or ppm), flux rates (g C/m²/day), stoichiometric ratios (C:N:P), energy units (kJ), and environmental units (temperature, moisture). |
| | Instruments | Devices and tools (microscopes, spectrometers, sensors, surveys, detectors) used to produce measurements. | Gas-exchange chambers, eddy covariance towers, soil probes, nutrient analyzers, mass spectrometers, remote-sensing satellites, drones, lysimeters, chlorophyll meters, and continuous environmental monitoring stations. |
| 2.3 Operational Definitions | Definitions | Terms defined by specific measurement procedures, ensuring empirical clarity. | Operational definitions for NPP, GPP, ecosystem respiration, nutrient turnover, carbon sequestration, trophic efficiency, soil organic matter fraction, and detrital decomposition rates. |
| | Procedures | The explicit steps required to perform a measurement in a reproducible way. | Standardized workflows such as gas flux measurements, biomass harvesting, water/soil sampling, nutrient-extraction protocols, satellite-derived productivity metrics, and decomposition-bag deployments. |
| 2.4 Data Acquisition | Protocols | Formal processes for gathering data under controlled or standardized conditions. | Repeated flux monitoring, seasonal biomass surveys, long-term nutrient budget sampling, remote-sensing data collection, soil-profile sampling, and continuous monitoring of environmental drivers. |
| | Sampling | Rules determining which subset of the domain is measured and how representative it is. | Selecting representative plots, stratified sampling across ecosystems, repeated temporal sampling, multi-depth soil sampling, and spatially distributed monitoring across environmental gradients. |
| 2.5 Data Character & Format | Data Types | The form raw evidence takes (time series, spectra, images, counts, qualitative records). | Flux time series, biomass tables, nutrient concentration matrices, soil profiles, remote-sensing imagery, stoichiometric datasets, trophic-flow diagrams, and environmental time series. |
| | Resolution | The granularity or precision with which data is captured. | Spatial resolution (plot-scale to landscape-scale), temporal resolution (hourly to annual), chemical resolution for nutrient species, and flux-sensor precision for gas-exchange or water-flow measurements. |
| 2.6 Reliability & Calibration | Calibration | Adjustment procedures ensuring instruments produce accurate results. | Calibration of gas sensors, nutrient analyzers, remote-sensing reflectance values, soil probes, environmental sensors, mass-spec instruments, and biomass-harvesting standards. |
| | Error Characterization | Identification and quantification of noise, uncertainty, bias, and measurement error. | Sources of error include sensor drift, soil heterogeneity, sampling variance, weather-driven noise, remote-sensing classification errors, nutrient extraction inefficiencies, and flux-tower processing artifacts. |
| 3. Structural Layer | 3.1 Patterns & Regularities | Laws / Relations | Stable, repeatable patterns governing how observables behave across conditions. | Consistent relationships such as energy-flow pyramids, mass-balance equations, trophic-transfer efficiencies, nutrient cycling patterns, productivity–respiration dynamics, and stoichiometric constraints. |
| | Invariants | Quantities or properties that remain constant under transformations (symmetries, conservation laws). | Conservation of mass and energy, stable trophic hierarchies, persistent nutrient-pool ratios, characteristic decomposition pathways, and long-term carbon-turnover patterns under equilibrium conditions. |
| 3.2 Causal Architecture | Mechanisms | Underlying processes or structures that produce the observed regularities. | Mechanisms include primary production, respiration, decomposition, mineralization, nutrient uptake, trophic transfer, hydrologic transport, and biogeochemical feedback loops. |
| | Pathways | Organized sequences of interactions forming a causal chain or network. | Sequential processes such as sunlight → primary production → herbivory → decomposition → nutrient recycling; or precipitation → soil moisture → plant uptake → evapotranspiration → atmosphere. |
| 3.3 Theoretical Vocabulary | Concepts | Core terms that encode the domain’s structure (force, gene, equilibrium, field). | Key terms include energy flow, nutrient cycling, biogeochemical flux, primary productivity, ecological stoichiometry, trophic efficiency, ecosystem respiration, turnover time, and ecosystem stability/resilience. |
| | Classifications | Taxonomies, categories, or typologies that organize entities and relations. | Ecosystem types (forest, grassland, desert, aquatic), trophic structures (producer/consumer/decomposer), nutrient pools (organic/inorganic), flux types (input/output/internal), and biogeochemical cycle components. |
| 3.4 Formal Representations | Equations | Mathematical constructs expressing laws, relations, or mechanisms. | Productivity equations (GPP, NPP), mass-balance equations for nutrients, flux equations (NEE = GPP – Reco), stoichiometric constraints (C:N:P ratios), and trophic-transfer models. |
| | Models | Structured representations—mathematical, computational, or conceptual—used to predict and explain phenomena. | Ecosystem-budget models, nutrient-cycling models, food-web flow models, stoichiometric models, hydrologic models, global carbon-cycle models, and ecosystem process simulations. |
| 3.5 Idealized Structures | Simplified Models | Purposeful abstractions that capture essential dynamics while omitting irrelevant detail. | Treating ecosystems as well-mixed boxes, assuming steady-state conditions, simplifying food webs to linear chains, collapsing nutrient pools, or ignoring spatial heterogeneity. |
| | Limit Conditions | Regimes where specific models or approximations hold (classical vs. quantum, linear vs. nonlinear). | Valid under stable environmental conditions, moderate spatial homogeneity, and weak nonlinear feedbacks; break down in highly variable climates, extreme disturbances, or complex spatial mosaics. |
| 3.6 Integrative Frameworks | Unifying Theories | Higher-order structures that connect disparate laws or mechanisms under a coherent whole. | Includes ecosystem energetics, biogeochemical cycle theory, stoichiometric ecology, systems ecology, and mass-balance frameworks integrating biological, chemical, and physical processes. |
| | Interdisciplinary Links | Points where the theory connects to adjacent sciences or larger explanatory systems. | Strong links to global change science, biogeochemistry, hydrology, atmospheric science, geology, community ecology, and landscape ecology through shared focus on fluxes, pools, and large-scale system behavior. |
| 4. Method Layer | 4.1 Inquiry Design | Experimental Design | Structured plans for manipulating variables to test causal claims. | Manipulating nutrient inputs, altering resource availability, controlling light/water additions, imposing disturbance regimes, excluding trophic levels (exclosure studies), or modifying ecosystem compartments to test causal effects on fluxes and productivity. |
| | Observational Design | Systematic approaches for gathering non-manipulated data (surveys, field studies, natural experiments). | Long-term monitoring of flux towers, remote sensing, nutrient budgets, environmental gradients, natural disturbances, and ecosystem responses across space and time without direct manipulation. |
| 4.2 Testing & Validation | Hypothesis Testing | Procedures for evaluating whether evidence supports or contradicts specific claims. | Evaluating predictions about nutrient limitation, productivity drivers, decomposition rates, carbon balance, hydrologic dynamics, or trophic impacts by comparing observed ecosystem responses to mechanistic models. |
| | Replication | The requirement that results be independently reproducible under similar conditions. | Repeating flux measurements, nutrient sampling, biomass surveys, decomposition trials, and ecosystem manipulations across multiple plots, seasons, years, and contrasting ecosystem types to ensure robustness. |
| 4.3 Inference & Evaluation | Statistical Inference | Rules for drawing conclusions from noisy or incomplete data. | Using regression, mixed models, time-series analysis, structural equation models, mass-balance uncertainty analysis, Bayesian ecosystem modeling, and spatial-statistical approaches to interpret flux and pool data. |
| | Model Comparison | Criteria (fit, simplicity, predictive accuracy, robustness) used to evaluate competing models. | Comparing alternative biogeochemical models, nutrient-cycling frameworks, productivity models, hydrologic models, and carbon-balance models based on predictive accuracy, stability, parsimony, and empirical fit. |
| 4.4 Error Management | Error Analysis | Identification and quantification of random and systematic errors. | Quantifying errors from sensor drift, incomplete flux capture, heterogeneous sampling, environmental noise, remote-sensing misclassification, nutrient-extraction inefficiencies, and uncertainty in pool-turnover estimates. |
| | Bias Control | Methods for minimizing subjective, instrumental, or procedural biases. | Using calibration standards, standardized protocols, repeated instrument checks, randomization of sampling locations, correction for detection biases, and cross-validation of remote-sensing and ground-based measurements. |
| 4.5 Adjudication & Revision | Peer Scrutiny | Collective evaluation of claims through critique, review, and debate. | External evaluation of ecosystem budgets, flux measurements, nutrient-cycle interpretations, and modeling assumptions through peer review, reanalysis, and multi-site collaborative comparison. |
| | Theory Revision | Procedures for modifying, replacing, or discarding models based on new evidence. | Updating nutrient limitation theory, productivity frameworks, stoichiometric models, or carbon-balance concepts when new data contradict established assumptions or reveal overlooked feedback mechanisms. |
| 4.6 Integrity Conditions | Transparency | Requirements to disclose methods, data, assumptions, and limitations. | Full reporting of flux-tower settings, calibration files, sampling procedures, nutrient-extraction methods, model parameters, environmental metadata, and uncertainty quantification. |
| | Ethical Standards | Norms ensuring responsible conduct in experimentation, data handling, and publication. | Maintaining minimal ecosystem disturbance, respecting protected habitats, honest reporting of data, responsible use of remote sensing, and ethical interpretation of ecosystem manipulation results. |