| 1. Domain | 1.1 Scope of the Domain | Boundaries | The range of phenomena the science includes and excludes. | Focuses on the structure, composition, diversity, and interactions of multiple co-occurring species within a shared environment. Includes competition, predation, mutualism, commensalism, trophic structure, diversity patterns, and community assembly. Excludes single-species population dynamics except as components of multi-species interactions. |
| | Scale | The spatial, temporal, or organizational level at which the science operates (e.g., quantum, cellular, social, cosmic). | Operates at scales from local species assemblages to regional species pools, across spatial gradients (microhabitats to landscapes) and temporal scales from seasons to centuries for successional and long-term community change. |
| 1.2 Ontological Commitments | Entities | The kinds of things assumed to exist within the domain (particles, organisms, agents, fields, etc.). | Species, guilds, functional groups, trophic levels, interaction networks, resources, habitat patches, niches, environmental filters, and interaction-modifying environmental factors. |
| | Properties | The fundamental attributes these entities possess (mass, charge, genotype, preference, etc.). | Species abundance, interaction strength, diversity metrics, trophic position, niche breadth, resource consumption rates, recruitment success, and sensitivity to biotic and abiotic filters. |
| | Categories | The basic ontological types used to classify domain elements (substances, processes, relations, structures). | Interaction types (competition, predation, mutualism), community types (trophic, functional, phylogenetic), diversity categories (alpha, beta, gamma), successional stages, and network structures (modular, nested). |
| 1.3 State-Variables | Variables | The measurable or definable properties that describe system conditions. | Species richness, abundance distributions, interaction coefficients, trophic flows, resource availability, environmental gradients, recruitment rates, and species turnover. |
| | Parameterization | How variables encode and represent the system’s state. | Community state represented through species-abundance distributions, interaction matrices, trophic webs, diversity indices, trait distributions, ordination axes, and environmental-gradient metrics. |
| 1.4 Admissible Idealizations | Simplifications | Conceptual reductions used to make the domain tractable (point masses, rational agents, perfect gases). | Treating species as functionally identical, assuming pairwise interactions only, ignoring spatial structure, simplifying trophic webs to linear chains, representing complex communities with summary metrics, or assuming static environments. |
| | Validity Conditions | The limits and contexts in which idealizations hold or break down. | Idealizations break under strong trait differentiation, complex indirect effects, spatial heterogeneity, temporal variability, multi-trophic feedbacks, or strong environmental filtering. |
| 1.5 Domain Assumptions | Structural Assumptions | Background ontological stances such as determinism, continuity, randomness, discreteness. | Assumes species interactions follow consistent ecological principles, community patterns emerge from trait/environment matching, and diversity is shaped by deterministic and stochastic processes in predictable ways. |
| | Implicit Commitments | Unstated but necessary assumptions that shape the field’s conceptual structure. | Assumes species coexist via stable mechanisms, interactions are interpretable, communities respond coherently to environmental gradients, and diversity reflects both assembly rules and ecological opportunity. |
| 1.6 Internal Coherence Requirements | Consistency | The demand that domain concepts do not contradict one another. | Species interactions, community patterns, and diversity metrics must align without contradiction across models, observations, and environmental contexts. |
| | Compatibility | The requirement that entities, variables, and assumptions fit together into a unified descriptive framework. | Entities (species, interactions), variables (abundance, diversity, resource gradients), and assumptions (niche processes, environmental filtering) must integrate into one coherent multi-species explanatory framework. |
| 2. Evidence Layer | 2.1 Observable Phenomena | Observables | The aspects of the domain that can produce detectable signals accessible to measurement. | Species presence/absence, abundance patterns, species richness, diversity indices, trophic interactions, behavioral interactions, resource use patterns, species turnover, and spatial aggregation or dispersion. |
| | Detection Limits | The boundaries of what can be resolved or sensed by current instruments or methods. | Minimum abundance detectable by surveys, smallest measurable interaction strength, limits of acoustic/visual detection of species, minimal detectable resource-use differences, and thresholds for detecting rare or cryptic species. |
| 2.2 Measurement Systems | Units | Standardized quantifications (meters, seconds, volts, decibels, dollars, etc.) necessary for consistent comparison. | Counts of individuals, abundance per area, biomass, diversity metrics (Shannon, Simpson), interaction coefficients, trophic-flow units (energy or biomass flux), and spatial metrics (m², km²). |
| | Instruments | Devices and tools (microscopes, spectrometers, sensors, surveys, detectors) used to produce measurements. | Quadrat frames, transects, camera traps, acoustic sensors, eDNA sampling tools, pitfall traps, nets, vegetation survey equipment, drones, environmental monitoring devices, and community-sampling kits (soil, water). |
| 2.3 Operational Definitions | Definitions | Terms defined by specific measurement procedures, ensuring empirical clarity. | Operational definitions for “species richness,” “species interaction,” “guild membership,” “trophic position,” “functional group,” “diversity index,” and “community composition” based on standardized criteria. |
| | Procedures | The explicit steps required to perform a measurement in a reproducible way. | Standardized steps for transect surveys, quadrat sampling, visual encounter surveys, camera trap protocols, eDNA collection, vegetation plots, interaction observations, and trophic network sampling. |
| 2.4 Data Acquisition | Protocols | Formal processes for gathering data under controlled or standardized conditions. | Systematic sampling schedules, repeated community censuses, multi-season surveys, trophic interaction recording, structured eDNA sampling, environmental-gradient sampling, and standardized community-assessment protocols. |
| | Sampling | Rules determining which subset of the domain is measured and how representative it is. | Rules for selecting species, habitats, microhabitats, transect locations, sampling frequency, and number of replicate plots to ensure representative community characterization across space and time. |
| 2.5 Data Character & Format | Data Types | The form raw evidence takes (time series, spectra, images, counts, qualitative records). | Species-abundance matrices, presence/absence tables, diversity index values, community ordination datasets, interaction networks, spatial distribution maps, and qualitative ecological field notes. |
| | Resolution | The granularity or precision with which data is captured. | Temporal resolution (seasonal–annual), spatial resolution (plot-scale to landscape-scale), taxonomic resolution (species/guild/functional group), and detection resolution for rare species or weak interactions. |
| 2.6 Reliability & Calibration | Calibration | Adjustment procedures ensuring instruments produce accurate results. | Calibration of survey methods, observer training, detection-correction models, sensor calibration (acoustic, camera), eDNA contamination controls, and repeated verification of plot boundaries and measurement units. |
| | Error Characterization | Identification and quantification of noise, uncertainty, bias, and measurement error. | Errors include observer bias, misidentification, imperfect detection, environmental noise, variation in sampling effort, spatial heterogeneity, stochastic species turnover, and incomplete detection of rare or transient species. |
| 3. Structural Layer | 3.1 Patterns & Regularities | Laws / Relations | Stable, repeatable patterns governing how observables behave across conditions. | Recurring patterns such as species–area relationships, competitive exclusion, niche partitioning, trophic pyramids, succession trajectories, and predictable diversity–productivity relationships. |
| | Invariants | Quantities or properties that remain constant under transformations (symmetries, conservation laws). | Persistent features like stable trophic structures, consistent guild roles, conserved interaction motifs (e.g., nested mutualisms), recurrent species-abundance distributions, and enduring dominance hierarchies. |
| 3.2 Causal Architecture | Mechanisms | Underlying processes or structures that produce the observed regularities. | Mechanisms include competition, predation, facilitation, resource partitioning, environmental filtering, trophic cascades, priority effects, and disturbance-driven turnover shaping community composition. |
| | Pathways | Organized sequences of interactions forming a causal chain or network. | Processes such as disturbance → colonization → competition → succession; resource enrichment → altered competition → changes in diversity; predator removal → trophic cascade → community reorganization. |
| 3.3 Theoretical Vocabulary | Concepts | Core terms that encode the domain’s structure (force, gene, equilibrium, field). | Key terms: niche differentiation, competitive exclusion, keystone species, trophic level, mutualism, facilitation, community assembly, alpha/beta/gamma diversity, resilience, and functional redundancy. |
| | Classifications | Taxonomies, categories, or typologies that organize entities and relations. | Categories of interactions (competition, predation, mutualism), community types (forest, grassland, reef), successional stages (early, mid, late), functional groups, guilds, and trophic compartments. |
| 3.4 Formal Representations | Equations | Mathematical constructs expressing laws, relations, or mechanisms. | Lotka–Volterra competition/predation equations, species–area power functions, diversity indices, interaction-coefficient matrices, trophic-flow equations, and community stability metrics. |
| | Models | Structured representations—mathematical, computational, or conceptual—used to predict and explain phenomena. | Interaction-network models, community-assembly models, niche-based models, neutral models, successional dynamic models, trophic-web simulations, and multivariate ordination models. |
| 3.5 Idealized Structures | Simplified Models | Purposeful abstractions that capture essential dynamics while omitting irrelevant detail. | Representing communities with pairwise interactions only, ignoring indirect effects, treating environments as static, collapsing species into functional groups, or using uniform species traits. |
| | Limit Conditions | Regimes where specific models or approximations hold (classical vs. quantum, linear vs. nonlinear). | Valid under low complexity, moderate environmental stability, weak indirect effects, simple trophic structures, and limited spatial heterogeneity; break down with complex webs, high stochasticity, or strong context dependence. |
| 3.6 Integrative Frameworks | Unifying Theories | Higher-order structures that connect disparate laws or mechanisms under a coherent whole. | Includes the niche framework, community assembly theory, trophic-network theory, metacommunity theory, diversity–stability relationships, and unified models linking interactions, environment, and diversity patterns. |
| | Interdisciplinary Links | Points where the theory connects to adjacent sciences or larger explanatory systems. | Connections to ecosystem ecology, evolutionary biology, conservation science, biogeography, climate science, and landscape ecology through shared principles of diversity, interaction, and environmental structure. |
| 4. Method Layer | 4.1 Inquiry Design | Experimental Design | Structured plans for manipulating variables to test causal claims. | Manipulating species presence/absence, resource levels, disturbance regimes, habitat complexity, or predator densities to test causal effects on community composition, diversity, and interaction strength. |
| | Observational Design | Systematic approaches for gathering non-manipulated data (surveys, field studies, natural experiments). | Using community surveys, long-term monitoring, natural experiments from environmental gradients, opportunistic events (wildfires, floods), and non-manipulative tracking of interaction networks. |
| 4.2 Testing & Validation | Hypothesis Testing | Procedures for evaluating whether evidence supports or contradicts specific claims. | Evaluating predictions about competition, predation, niche partitioning, trophic cascades, community assembly rules, and diversity–stability relationships using controlled tests or comparative datasets. |
| | Replication | The requirement that results be independently reproducible under similar conditions. | Replicating community experiments across plots, habitats, seasons, environmental gradients, and independent locations to ensure robustness of interaction and diversity results. |
| 4.3 Inference & Evaluation | Statistical Inference | Rules for drawing conclusions from noisy or incomplete data. | Applying multivariate analyses, network statistics, diversity metrics, regression and GLMs, mixed models, ordination techniques (PCA, NMDS), and Bayesian inference to interpret complex community data. |
| | Model Comparison | Criteria (fit, simplicity, predictive accuracy, robustness) used to evaluate competing models. | Comparing niche vs neutral models, alternative interaction networks, different community assembly models, trophic-structure models, and successional dynamic models based on fit, parsimony, and predictive accuracy. |
| 4.4 Error Management | Error Analysis | Identification and quantification of random and systematic errors. | Identifying errors from species misidentification, inconsistent sampling effort, detection bias for rare species, environmental noise, temporal variability, and uncertainty in interaction estimates. |
| | Bias Control | Methods for minimizing subjective, instrumental, or procedural biases. | Reducing bias through standardized survey protocols, double-observer verification, randomized plot selection, balanced sampling designs, detection-correction models, and rigorous taxonomic vetting. |
| 4.5 Adjudication & Revision | Peer Scrutiny | Collective evaluation of claims through critique, review, and debate. | Community analyses, interaction networks, diversity metrics, and assembly inferences are evaluated through peer review, reanalysis, cross-site comparisons, and collaborative ecological assessments. |
| | Theory Revision | Procedures for modifying, replacing, or discarding models based on new evidence. | Updating theories of niche partitioning, community assembly, trophic dynamics, or diversity–stability relationships when new empirical evidence contradicts classical frameworks or reveals overlooked interactions. |
| 4.6 Integrity Conditions | Transparency | Requirements to disclose methods, data, assumptions, and limitations. | Full reporting of sampling protocols, survey intervals, detection assumptions, statistical methods, interaction matrices, diversity index formulas, and model-selection criteria. |
| | Ethical Standards | Norms ensuring responsible conduct in experimentation, data handling, and publication. | Ethical sampling of communities, minimizing habitat disturbance, avoiding harm to species, honest reporting of interaction data, and responsible interpretation of community-level ecological outcomes. |