| 1. Domain | 1.1 Scope of the Domain | Boundaries | The range of phenomena the science includes and excludes. | Focuses on how allele frequencies and genotype distributions change in populations over time under mutation, selection, drift, migration, and mating systems. Includes Hardy–Weinberg equilibrium, gene-flow and structure, effective population size, inbreeding, and polymorphism dynamics. Excludes individual-level Mendelian inheritance and molecular mechanisms except where they affect population-level change. |
| | Scale | The spatial, temporal, or organizational level at which the science operates (e.g., quantum, cellular, social, cosmic). | Operates at population and metapopulation levels across generations; temporal scales from single-generation shifts to long-term evolutionary trends; spatial scales from local demes to species-wide ranges. |
| 1.2 Ontological Commitments | Entities | The kinds of things assumed to exist within the domain (particles, organisms, agents, fields, etc.). | Alleles, genotypes, gene pools, populations, demes, migrants, mutations, selection coefficients, fitness values, effective population size (Ne), drift events. |
| | Properties | The fundamental attributes these entities possess (mass, charge, genotype, preference, etc.). | Allele and genotype frequencies, fitness, selection coefficients (s, h), mutation rate, migration rate, inbreeding coefficient (F), Ne, variance in reproductive success, linkage disequilibrium. |
| | Categories | The basic ontological types used to classify domain elements (substances, processes, relations, structures). | Population types (panmictic, structured, subdivided), evolutionary forces (mutation, migration, drift, selection, nonrandom mating), selection modes (directional, stabilizing, balancing), mating systems (random, assortative, disassortative, inbreeding). |
| 1.3 State-Variables | Variables | The measurable or definable properties that describe system conditions. | Allele frequencies (p, q, etc.), genotype frequencies, fitness distributions, selection and migration parameters, mutation rates, F, Ne, LD coefficients, variance in allele-frequency change per generation. |
| | Parameterization | How variables encode and represent the system’s state. | State encoded via frequency vectors, Hardy–Weinberg equations, transition/recursion equations, Wright–Fisher or Moran model parameters, selection–mutation–migration balance equations, and LD matrices. |
| 1.4 Admissible Idealizations | Simplifications | Conceptual reductions used to make the domain tractable (point masses, rational agents, perfect gases). | Infinite population size, random mating, no selection, no mutation, no migration (Hardy–Weinberg baseline); discrete non-overlapping generations; linkage equilibrium; drift modeled as simple binomial sampling; ignoring demographic structure. |
| | Validity Conditions | The limits and contexts in which idealizations hold or break down. | Idealizations fail with finite or fluctuating population sizes, strong selection, nonrandom mating, significant structure or migration barriers, overlapping generations, strong LD, or complex demography. |
| 1.5 Domain Assumptions | Structural Assumptions | Background ontological stances such as determinism, continuity, randomness, discreteness. | Evolutionary change is driven by quantifiable stochastic and deterministic forces; allele-frequency dynamics follow probabilistic rules; fitness differences systematically affect reproductive success; populations can be approximated by sampling processes. |
| | Implicit Commitments | Unstated but necessary assumptions that shape the field’s conceptual structure. | Assumes reasonably stable generational structure, approximately constant parameters over modeled intervals, representative sampling, reliable fitness estimates, and that unmodeled ecological factors do not dominate dynamics. |
| 1.6 Internal Coherence Requirements | Consistency | The demand that domain concepts do not contradict one another. | Assumptions about mutation, migration, drift, selection, and mating must be mutually compatible; predicted allele-frequency trajectories and equilibria must not contradict each other within a given model. |
| | Compatibility | The requirement that entities, variables, and assumptions fit together into a unified descriptive framework. | Alleles, frequencies, fitness values, demographic parameters, and stochastic processes must integrate into a unified framework that coherently describes how gene pools change over time. |
| 2. Evidence Layer | 2.1 Observable Phenomena | Observables | The aspects of the domain that can produce detectable signals accessible to measurement. | Allele-frequency changes across generations, deviations from Hardy–Weinberg expectations, genotype-count distributions, recombination-derived LD patterns, signatures of drift in small populations, migration-driven allele introgression, and selection-driven changes in fitness-associated alleles. |
| | Detection Limits | The boundaries of what can be resolved or sensed by current instruments or methods. | Limited by sample size, genotyping resolution, population completeness, ability to detect rare alleles, accuracy of frequency estimation, and sensitivity to weak selection or low migration rates. |
| 2.2 Measurement Systems | Units | Standardized quantifications (meters, seconds, volts, decibels, dollars, etc.) necessary for consistent comparison. | Allele-frequency values (0–1), genotype proportions, selection coefficients (s, h), mutation rates (per generation), migration rates (m), inbreeding coefficient (F), LD values (D, r²), effective population size (Ne). |
| | Instruments | Devices and tools (microscopes, spectrometers, sensors, surveys, detectors) used to produce measurements. | Genotyping assays (SNP arrays, sequencing), allele-frequency estimation pipelines, population surveys, pedigree or census data, linkage-disequilibrium calculators, demographic model–fitting tools, structured-population sampling frameworks. |
| 2.3 Operational Definitions | Definitions | Terms defined by specific measurement procedures, ensuring empirical clarity. | Allele frequency defined as proportion of alleles in the population; Hardy–Weinberg equilibrium defined by stable genotype frequencies under ideal conditions; selection coefficient defined by relative fitness differences; LD defined by non-random allele associations. |
| | Procedures | The explicit steps required to perform a measurement in a reproducible way. | Sampling individuals, genotyping loci, estimating allele/genotype frequencies, computing HW expectations, calculating selection or migration parameters, measuring LD, applying maximum-likelihood or Bayesian inference to demographic models. |
| 2.4 Data Acquisition | Protocols | Formal processes for gathering data under controlled or standardized conditions. | Standardized population sampling, consistent genotyping methods, appropriate geographic/temporal sampling intervals, replicated measurements, control of sample contamination, use of representative population subsets. |
| | Sampling | Rules determining which subset of the domain is measured and how representative it is. | Choosing representative individuals across demes, ensuring sufficient sample size, avoiding related individuals when estimating population parameters, sampling across multiple generations or timepoints to detect change. |
| 2.5 Data Character & Format | Data Types | The form raw evidence takes (time series, spectra, images, counts, qualitative records). | Allele-frequency tables, genotype matrices, HW-equilibrium tests, LD matrices, time-series allele trajectories, demographic-parameter datasets, selection-coefficient estimates, migration-rate models. |
| | Resolution | The granularity or precision with which data is captured. | Determined by genotyping accuracy, number of sampled loci, sample size, temporal sampling density, sequencing depth, and the ability to resolve rare-variant dynamics or subtle frequency shifts. |
| 2.6 Reliability & Calibration | Calibration | Adjustment procedures ensuring instruments produce accurate results. | Validation of genotyping accuracy, reference-sample controls, calibration of allele-frequency estimation pipelines, cross-validation of demographic models, error-rate estimation for sequencing/microarray platforms. |
| | Error Characterization | Identification and quantification of noise, uncertainty, bias, and measurement error. | Identifying sampling bias, genotyping error, stochastic noise in allele counts, uncertainties in demographic inference, deviations from model assumptions, and distinguishing random drift from measurement error; quantifying both systematic and random error sources. |
| 3. Structural Layer | 3.1 Patterns & Regularities | Laws / Relations | Stable, repeatable patterns governing how observables behave across conditions. | Hardy–Weinberg equilibrium establishes stable genotype-frequency relationships under ideal conditions; allele-frequency change follows predictable recursion equations; drift variance scales with 1/Ne; migration moves allele frequencies toward population averages; selection shifts allele frequencies proportional to fitness differences. |
| | Invariants | Quantities or properties that remain constant under transformations (symmetries, conservation laws). | Stable mathematical relationships between allele and genotype frequencies; conserved forms of selection dynamics (directional, stabilizing, balancing); invariant expectations for drift variance; fixed relationships between recombination and LD decay; predictable equilibrium states under specific force combinations. |
| 3.2 Causal Architecture | Mechanisms | Underlying processes or structures that produce the observed regularities. | Mutation introduces new alleles; selection alters reproductive success; drift changes frequencies via random sampling; migration mixes gene pools; nonrandom mating shifts genotype frequencies; recombination reshapes LD patterns; demographic structure modulates all forces. |
| | Pathways | Organized sequences of interactions forming a causal chain or network. | Mutation → allele introduction → frequency shift; migration → admixture → equilibrium; selection → differential survival/reproduction → allele-frequency change; drift → stochastic sampling → variance accumulation; recombination → LD breakup → new allele combinations. |
| 3.3 Theoretical Vocabulary | Concepts | Core terms that encode the domain’s structure (force, gene, equilibrium, field). | Allele frequency, genotype frequency, Hardy–Weinberg equilibrium, fitness, selection coefficient, mutation rate, migration rate, genetic drift, effective population size, linkage disequilibrium, inbreeding coefficient, equilibrium states. |
| | Classifications | Taxonomies, categories, or typologies that organize entities and relations. | Forces (mutation, drift, selection, migration, nonrandom mating); population types (panmictic, structured, subdivided); selection modes (directional, stabilizing, disruptive, balancing); mating systems (random, assortative, disassortative, inbreeding). |
| 3.4 Formal Representations | Equations | Mathematical constructs expressing laws, relations, or mechanisms. | HW equilibrium equations (p² + 2pq + q²); selection recursion equations (p′ = p·wA / w̄); mutation–selection balance formulas; migration equations (p′ = (1−m)p + m pmig); drift variance formulas (Var(p) = p(1–p)/2Ne); LD decay equations (D′ = D(1−r)). |
| | Models | Structured representations—mathematical, computational, or conceptual—used to predict and explain phenomena. | Wright–Fisher model, Moran model, selection–mutation models, island/stepping-stone migration models, coalescent models, LD-block models, structured-population models, inbreeding and assortative-mating models. |
| 3.5 Idealized Structures | Simplified Models | Purposeful abstractions that capture essential dynamics while omitting irrelevant detail. | Infinite population size; random mating; constant selection parameters; ignoring epistasis; assuming no structure; uniform recombination; discrete non-overlapping generations; ignoring environmental effects on fitness. |
| | Limit Conditions | Regimes where specific models or approximations hold (classical vs. quantum, linear vs. nonlinear). | Idealizations fail with small or fluctuating Ne, strong selection, assortative mating, migration barriers, overlapping generations, strong LD, or demographic complexity; classical assumptions break in rapidly changing or highly structured populations. |
| 3.6 Integrative Frameworks | Unifying Theories | Higher-order structures that connect disparate laws or mechanisms under a coherent whole. | Evolutionary change described as interplay of deterministic (selection, migration) and stochastic (drift, mutation) forces; HW equilibrium as a baseline null model; coalescent theory linking genealogies with allele-frequency dynamics; unified models connect demography, mutation, and selection into a coherent evolutionary framework. |
| | Interdisciplinary Links | Points where the theory connects to adjacent sciences or larger explanatory systems. | Connects to ecology (population structure, dispersal), molecular evolution (substitution models), quantitative genetics (trait variance components), conservation biology (inbreeding, Ne), epidemiology (pathogen evolution), and anthropology (human population history). |
| 4. Method Layer | 4.1 Inquiry Design | Experimental Design | Structured plans for manipulating variables to test causal claims. | Manipulating allele frequencies through controlled breeding, introducing known migrants, altering selection pressures in experimental populations, adjusting mutation rates via environmental stress, or constructing synthetic populations with defined structure to test causal predictions about allele-frequency dynamics. |
| | Observational Design | Systematic approaches for gathering non-manipulated data (surveys, field studies, natural experiments). | Surveying natural populations, collecting allele-frequency data across time or geography, observing natural selection gradients, tracking drift in isolated demes, and documenting migration or admixture events without intervention. |
| 4.2 Testing & Validation | Hypothesis Testing | Procedures for evaluating whether evidence supports or contradicts specific claims. | Using HW equilibrium tests to validate null assumptions; evaluating selection models against observed allele-frequency trajectories; testing migration hypotheses through clinal patterns; testing drift expectations in small populations; validating LD decay predictions. |
| | Replication | The requirement that results be independently reproducible under similar conditions. | Repeating sampling in independent populations, collecting multiple temporal datasets, replicating genotyping runs, validating demographic inferences with separate loci or datasets, and confirming allele-frequency shifts across generations. |
| 4.3 Inference & Evaluation | Statistical Inference | Rules for drawing conclusions from noisy or incomplete data. | Estimating allele frequencies with confidence intervals; inferring selection coefficients; calculating Ne; inferring mutation and migration rates; fitting demographic models; using Bayesian or ML frameworks to quantify uncertainty in evolutionary parameter estimates. |
| | Model Comparison | Criteria (fit, simplicity, predictive accuracy, robustness) used to evaluate competing models. | Comparing drift-only vs drift+selection models, panmictic vs structured-population models, island vs stepping-stone migration models, HW vs non-HW fits, coalescent vs forward-time simulations, and evaluating robustness and predictive power. |
| 4.4 Error Management | Error Analysis | Identification and quantification of random and systematic errors. | Identifying genotyping errors, sampling bias, allele dropout, sequencing noise, misestimated population boundaries, model misfit, and deviations caused by unmodeled ecological factors; partitioning random vs systematic error. |
| | Bias Control | Methods for minimizing subjective, instrumental, or procedural biases. | Standardizing sampling protocols, using random sampling within demes, validating genotyping platforms, accounting for relatedness, correcting for population structure, running blinded analyses, and applying quality filters to sequencing datasets. |
| 4.5 Adjudication & Revision | Peer Scrutiny | Collective evaluation of claims through critique, review, and debate. | Reviewing allele-frequency analyses, checking HW tests, re-evaluating demographic models, validating selection inferences, replicating drift measurements, and comparing model predictions to independent datasets. |
| | Theory Revision | Procedures for modifying, replacing, or discarding models based on new evidence. | Updating models when new data reveal unexpected selection forces, migration patterns, demographic histories, or deviations from classical assumptions; revising Ne estimates or LD models when real populations violate simplifying assumptions. |
| 4.6 Integrity Conditions | Transparency | Requirements to disclose methods, data, assumptions, and limitations. | Full disclosure of sampling frames, genotyping platforms, filtering steps, demographic assumptions, statistical models, and sources of uncertainty; clear reporting of deviations from HW or model expectations. |
| | Ethical Standards | Norms ensuring responsible conduct in experimentation, data handling, and publication. | Ensuring ethical acquisition of biological samples, respecting human-population data privacy, honest reporting of allele-frequency data, avoiding fabrication or selective exclusion, and adhering to standards for handling sensitive genetic information. |