| 1. Domain | 1.1 Scope of the Domain | Boundaries | The range of phenomena the science includes and excludes. | Examines the dynamics of populations: growth, regulation, age structure, density dependence, dispersal, survivorship, reproduction, and demographic patterns. Excludes individual-level physiological mechanisms and community-level interactions except when they directly influence population processes. |
| | Scale | The spatial, temporal, or organizational level at which the science operates (e.g., quantum, cellular, social, cosmic). | Operates at the level of populations, cohorts, and demographic units across spatial scales from local patches to regional landscapes, and temporal scales from seasonal cycles to evolutionary timescales. |
| 1.2 Ontological Commitments | Entities | The kinds of things assumed to exist within the domain (particles, organisms, agents, fields, etc.). | Individuals aggregated into populations, demographic classes (age, size, sex), resources influencing population growth, density-regulating factors, dispersal agents, and environmental drivers affecting population-level change. |
| | Properties | The fundamental attributes these entities possess (mass, charge, genotype, preference, etc.). | Population size, density, growth rate, carrying capacity, survival rates, fecundity, age distribution, dispersal probability, recruitment, mortality patterns, and demographic elasticity. |
| | Categories | The basic ontological types used to classify domain elements (substances, processes, relations, structures). | Population types (closed/open, stable/unstable), life-history strategies, density-dependent vs independent processes, demographic stages, reproductive strategies, and spatial population structures (patches, metapopulations). |
| 1.3 State-Variables | Variables | The measurable or definable properties that describe system conditions. | N(t) population size, birth and death rates, age-specific survival, reproductive rates, density metrics, migration/dispersal rates, resource levels, and environmental conditions affecting growth. |
| | Parameterization | How variables encode and represent the system’s state. | Population state represented through life tables, Leslie/Lefkovitch matrices, growth parameters (r, λ, K), demographic distributions, time-series counts, mark–recapture data, and spatial occupancy models. |
| 1.4 Admissible Idealizations | Simplifications | Conceptual reductions used to make the domain tractable (point masses, rational agents, perfect gases). | Assuming homogeneous populations, ignoring individual variation, treating environments as constant, modeling growth with simple equations (exponential, logistic), neglecting stochasticity, or simplifying spatial structure to uniform mixing. |
| | Validity Conditions | The limits and contexts in which idealizations hold or break down. | Simplifications fail with strong individual heterogeneity, fluctuating environments, spatial fragmentation, stochastic events, complex density dependence, or strong interspecific interactions. |
| 1.5 Domain Assumptions | Structural Assumptions | Background ontological stances such as determinism, continuity, randomness, discreteness. | Assumes populations follow consistent demographic rules, density dependence shapes long-term dynamics, reproduction and mortality are measurable processes, and environmental factors influence populations in systematic ways. |
| | Implicit Commitments | Unstated but necessary assumptions that shape the field’s conceptual structure. | Assumes individuals can be aggregated into meaningful demographic units, population-level patterns are interpretable, resource limitation affects growth, and dispersal follows predictable drivers. |
| 1.6 Internal Coherence Requirements | Consistency | The demand that domain concepts do not contradict one another. | Demographic data, population models, and observed growth patterns must align without contradiction across time, space, and environmental contexts. |
| | Compatibility | The requirement that entities, variables, and assumptions fit together into a unified descriptive framework. | Entities (populations, cohorts), variables (survival, fecundity, density), and assumptions (aggregation, density dependence) must fit together into a coherent framework for predicting population change. |
| 2. Evidence Layer | 2.1 Observable Phenomena | Observables | The aspects of the domain that can produce detectable signals accessible to measurement. | Population counts, birth and death events, age/size distribution, immigration/emigration events, density patterns, recruitment levels, survival of cohorts, and fluctuations in population abundance over time. |
| | Detection Limits | The boundaries of what can be resolved or sensed by current instruments or methods. | Minimum population size detectable with surveys, smallest measurable changes in density, detection thresholds for cryptic or low-density species, accuracy limits of mark–recapture data, and minimum viable sampling frequency. |
| 2.2 Measurement Systems | Units | Standardized quantifications (meters, seconds, volts, decibels, dollars, etc.) necessary for consistent comparison. | Number of individuals, density (individuals per area/volume), growth rate (r, λ), survival probability, fecundity per female, recruitment rate, migration probability, and time in days, seasons, or years. |
| | Instruments | Devices and tools (microscopes, spectrometers, sensors, surveys, detectors) used to produce measurements. | Survey tools, transect frames, camera traps, acoustic monitors, drones, pitfall traps, nets, mark–recapture equipment, GPS tags, environmental sensors, and automated counting devices. |
| 2.3 Operational Definitions | Definitions | Terms defined by specific measurement procedures, ensuring empirical clarity. | Definitions of “population size,” “density,” “cohort,” “recruitment,” “mortality event,” “migrant,” and “reproductive individual,” each tied to specific measurement criteria. |
| | Procedures | The explicit steps required to perform a measurement in a reproducible way. | Standardized census methods, transect surveys, quadrat sampling, mark–recapture workflows, nest/den monitoring, cohort tracking, tagging procedures, and repeated time-series survey protocols. |
| 2.4 Data Acquisition | Protocols | Formal processes for gathering data under controlled or standardized conditions. | Systematic survey schedules, repeated sampling intervals, mark–recapture cycles, demographic data collection, spatial surveys across habitat patches, and long-term monitoring of abundance. |
| | Sampling | Rules determining which subset of the domain is measured and how representative it is. | Rules for selecting individuals, cohorts, habitats, transect locations, frequency of sampling, sample sizes, and stratified sampling across environmental gradients to ensure demographic representativeness. |
| 2.5 Data Character & Format | Data Types | The form raw evidence takes (time series, spectra, images, counts, qualitative records). | Abundance time series, life tables, survival curves, size/age distributions, spatial density maps, mark–recapture matrices, cohort datasets, and qualitative field notes. |
| | Resolution | The granularity or precision with which data is captured. | Temporal resolution (daily–annual), spatial resolution (meter–landscape scale), demographic resolution (age/size classes), and detection resolution for rare, cryptic, or migratory individuals. |
| 2.6 Reliability & Calibration | Calibration | Adjustment procedures ensuring instruments produce accurate results. | Calibration of counting methods, observer training, correction factors for detectability, calibration of camera traps and sensors, mark–recapture model validation, and standardized protocol verification. |
| | Error Characterization | Identification and quantification of noise, uncertainty, bias, and measurement error. | Sources of error include imperfect detection, observer bias, incomplete recapture data, sampling variance, environmental noise, temporal gaps, identification errors, and demographic stochasticity. |
| 3. Structural Layer | 3.1 Patterns & Regularities | Laws / Relations | Stable, repeatable patterns governing how observables behave across conditions. | Core patterns include exponential and logistic growth, density-dependent regulation, boom-bust cycles, survivorship curves, life-history tradeoffs, and stable age-structure relations. |
| | Invariants | Quantities or properties that remain constant under transformations (symmetries, conservation laws). | Conserved demographic patterns such as characteristic survivorship types, stable age distributions at equilibrium, consistent density-dependent responses, and species-specific reproductive schedules. |
| 3.2 Causal Architecture | Mechanisms | Underlying processes or structures that produce the observed regularities. | Mechanisms include birth–death processes, density-dependent feedback, resource limitation, competition for space, environmental filtering, dispersal mechanisms, and demographic stochasticity. |
| | Pathways | Organized sequences of interactions forming a causal chain or network. | Ordered sequences such as resource fluctuation → density change → altered birth/survival → new population size; or immigration/emigration → patch occupancy → metapopulation persistence. |
| 3.3 Theoretical Vocabulary | Concepts | Core terms that encode the domain’s structure (force, gene, equilibrium, field). | Key terms: carrying capacity (K), intrinsic growth rate (r), survivorship, fecundity, recruitment, density dependence, demographic stochasticity, life history, metapopulation, and cohort. |
| | Classifications | Taxonomies, categories, or typologies that organize entities and relations. | Population types (closed/open, regulated/unregulated), life-history strategies (r-selected vs K-selected), survivorship types (I/II/III), metapopulation structures, and density-response categories. |
| 3.4 Formal Representations | Equations | Mathematical constructs expressing laws, relations, or mechanisms. | Exponential growth (dN/dt = rN), logistic growth (dN/dt = rN(1–N/K)), matrix population models (Leslie/Lefkovitch), metapopulation occupancy equations, and survival/mortality functions. |
| | Models | Structured representations—mathematical, computational, or conceptual—used to predict and explain phenomena. | Deterministic and stochastic growth models, age-structured and stage-structured models, metapopulation models, density-dependent feedback models, and demographic-projection models. |
| 3.5 Idealized Structures | Simplified Models | Purposeful abstractions that capture essential dynamics while omitting irrelevant detail. | Homogeneous population models, constant environment assumptions, simplified density dependence, uniform survival/fecundity across individuals, and absence of spatial structure. |
| | Limit Conditions | Regimes where specific models or approximations hold (classical vs. quantum, linear vs. nonlinear). | Valid under stable environments, limited heterogeneity, moderate population sizes, and weak stochasticity; break down with strong environmental variability, spatial fragmentation, or high demographic noise. |
| 3.6 Integrative Frameworks | Unifying Theories | Higher-order structures that connect disparate laws or mechanisms under a coherent whole. | Includes demographic theory, life-history theory, logistic regulation, metapopulation theory, density-dependent control, and unified models linking survival, reproduction, and growth. |
| | Interdisciplinary Links | Points where the theory connects to adjacent sciences or larger explanatory systems. | Links to evolutionary biology, conservation biology, community ecology, climate science, epidemiology, and resource management through shared principles of demographic change and environmental interaction. |
| 4. Method Layer | 4.1 Inquiry Design | Experimental Design | Structured plans for manipulating variables to test causal claims. | Manipulating population density, resource levels, predation pressure, or habitat structure; conducting controlled introductions/removals; imposing experimental environmental fluctuations to test demographic responses. |
| | Observational Design | Systematic approaches for gathering non-manipulated data (surveys, field studies, natural experiments). | Long-term population monitoring, repeated census surveys, natural experiments from climate variation, tracking demographic shifts, migration patterns, and density changes without direct manipulation. |
| 4.2 Testing & Validation | Hypothesis Testing | Procedures for evaluating whether evidence supports or contradicts specific claims. | Evaluating predictions about density dependence, survivorship, reproductive output, carrying capacity, and dispersal by comparing observed demographic patterns against model expectations. |
| | Replication | The requirement that results be independently reproducible under similar conditions. | Replication through repeated surveys across sites, seasons, years, independent populations, or parallel demographic studies to ensure reliability and generality of population estimates and trends. |
| 4.3 Inference & Evaluation | Statistical Inference | Rules for drawing conclusions from noisy or incomplete data. | Using regression models, GLMs, mixed models, survival analysis, time-series models, Bayesian inference, and bootstrapping to interpret demographic data and account for uncertainty. |
| | Model Comparison | Criteria (fit, simplicity, predictive accuracy, robustness) used to evaluate competing models. | Comparing exponential vs logistic growth, density-dependent vs density-independent models, structured vs unstructured models, stochastic vs deterministic models, and alternative metapopulation frameworks. |
| 4.4 Error Management | Error Analysis | Identification and quantification of random and systematic errors. | Identifying errors from imperfect detection, census undercounting, mark–recapture misidentification, sampling variance, environmental noise, demographic stochasticity, and model-parameter uncertainty. |
| | Bias Control | Methods for minimizing subjective, instrumental, or procedural biases. | Reducing bias through standardized survey protocols, randomized transect placement, double-observer methods, detection-correction models, calibration of equipment, and consistency in mark–recapture practices. |
| 4.5 Adjudication & Revision | Peer Scrutiny | Collective evaluation of claims through critique, review, and debate. | Independent evaluation of demographic analyses, growth models, census methods, survivorship estimates, and density dependence interpretations through peer review and cross-study comparisons. |
| | Theory Revision | Procedures for modifying, replacing, or discarding models based on new evidence. | Revising growth models, survivorship frameworks, density-regulation theories, or dispersal assumptions when new data contradict predictions or reveal hidden demographic complexity. |
| 4.6 Integrity Conditions | Transparency | Requirements to disclose methods, data, assumptions, and limitations. | Full disclosure of survey designs, sampling intervals, detection assumptions, population-model parameters, data-processing steps, and limitations of demographic inference. |
| | Ethical Standards | Norms ensuring responsible conduct in experimentation, data handling, and publication. | Ensuring humane handling of wildlife, adherence to permitting requirements, minimizing stress during tagging or capturing, honest reporting of demographic data, and ethical application of population-management recommendations. |