| 1. Domain | 1.1 Scope of the Domain | Boundaries | The range of phenomena the science includes and excludes. | Examines how ecological processes vary across space, how spatial patterns influence ecological dynamics, and how landscapes shape movement, dispersal, interactions, and ecosystem function. Includes fragmentation effects, connectivity, spatial heterogeneity, patch structure, corridors, barriers, and spatial scaling. Excludes fine-scale individual behavior or community interactions except when mediated by spatial structure. |
| | Scale | The spatial, temporal, or organizational level at which the science operates (e.g., quantum, cellular, social, cosmic). | Operates across spatial scales from habitat patches and corridors to entire landscapes and regions, with temporal scales ranging from seasonal dynamics to multi-decadal land-use change and long-term geomorphological processes. |
| 1.2 Ontological Commitments | Entities | The kinds of things assumed to exist within the domain (particles, organisms, agents, fields, etc.). | Habitat patches, matrices, corridors, barriers, landscape elements, spatial networks, dispersal pathways, species distributions, movement routes, land-use types, and environmental gradients. |
| | Properties | The fundamental attributes these entities possess (mass, charge, genotype, preference, etc.). | Patch size, shape, edge density, connectivity, fragmentation level, dispersal distance, spatial autocorrelation, heterogeneity metrics, barrier strength, and landscape permeability. |
| | Categories | The basic ontological types used to classify domain elements (substances, processes, relations, structures). | Patch types, land-cover classes, connectivity types, spatial configurations (fragmented, aggregated, linear), dispersal modes, landscape gradients, and network structures (graph nodes/edges). |
| 1.3 State-Variables | Variables | The measurable or definable properties that describe system conditions. | Patch occupancy, dispersal rate, landscape connectivity indices, spatial distribution of species, habitat-quality gradients, edge effects, corridor use intensity, and spatial-temporal turnover of landscape elements. |
| | Parameterization | How variables encode and represent the system’s state. | State represented via GIS layers, spatial matrices, connectivity graphs, dispersal kernels, landscape metrics (FRAGSTATS-type indices), remote-sensing data, and spatial-environmental covariates. |
| 1.4 Admissible Idealizations | Simplifications | Conceptual reductions used to make the domain tractable (point masses, rational agents, perfect gases). | Treating landscapes as binary habitat/matrix, simplifying patch shape, using uniform dispersal kernels, ignoring fine-scale heterogeneity, collapsing multi-species patterns into single metrics, or modeling movement as random walks. |
| | Validity Conditions | The limits and contexts in which idealizations hold or break down. | Simplifications fail with high spatial complexity, strong directional dispersal, species with specialized movement behaviors, heterogeneous barriers, complex land-use mosaics, or spatially coupled processes. |
| 1.5 Domain Assumptions | Structural Assumptions | Background ontological stances such as determinism, continuity, randomness, discreteness. | Assumes spatial structure influences ecological processes, dispersal is predictable, landscape metrics correlate with ecological function, and spatial heterogeneity shapes dynamics in systematic ways. |
| | Implicit Commitments | Unstated but necessary assumptions that shape the field’s conceptual structure. | Assumes landscapes can be discretized into meaningful patches, connectivity is measurable, movement follows interpretable rules, and spatial patterns meaningfully predict ecological processes. |
| 1.6 Internal Coherence Requirements | Consistency | The demand that domain concepts do not contradict one another. | Spatial metrics, movement data, fragmentation analyses, and connectivity models must align logically without contradicting patterns observed across scales. |
| | Compatibility | The requirement that entities, variables, and assumptions fit together into a unified descriptive framework. | Entities (patches, corridors, distributions), variables (connectivity, occupancy), and assumptions (spatial dependence, landscape effects) must integrate into a unified spatial 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 species spatial distributions, patch occupancy patterns, dispersal routes, landscape fragmentation, connectivity gradients, habitat-use mosaics, edge effects, and spatial autocorrelation. |
| | Detection Limits | The boundaries of what can be resolved or sensed by current instruments or methods. | Minimum resolvable patch size, smallest detectable dispersal distance, resolution limits of remote sensing, accuracy thresholds for GPS movement data, and detectability limits for small or rare patches in spatial sampling. |
| 2.2 Measurement Systems | Units | Standardized quantifications (meters, seconds, volts, decibels, dollars, etc.) necessary for consistent comparison. | Spatial units (m, km), area (m²–km²), connectivity indices, edge density, patch metrics, dispersal distances, landscape heterogeneity indices, and spatial autocorrelation coefficients. |
| | Instruments | Devices and tools (microscopes, spectrometers, sensors, surveys, detectors) used to produce measurements. | GPS collars, drones, satellite imagery, GIS software, automated tracking systems, remote-sensing platforms, environmental sensors, aerial photography, and landscape-classification tools. |
| 2.3 Operational Definitions | Definitions | Terms defined by specific measurement procedures, ensuring empirical clarity. | Operational definitions for “patch,” “corridor,” “matrix,” “occupancy,” “fragmentation,” “connectivity,” “landscape heterogeneity,” and “spatial cluster” based on measurable spatial criteria. |
| | Procedures | The explicit steps required to perform a measurement in a reproducible way. | Standard spatial-survey workflows, GIS layer construction, patch mapping, remote-sensing image processing, land-cover classification, movement-track cleaning, and field validation of spatial data. |
| 2.4 Data Acquisition | Protocols | Formal processes for gathering data under controlled or standardized conditions. | Regular satellite/airborne imagery acquisition, repeated GPS tracking intervals, seasonal habitat mapping, spatial transects, and ground-truthing surveys to validate remotely sensed data. |
| | Sampling | Rules determining which subset of the domain is measured and how representative it is. | Selecting representative patches, stratified sampling across land-cover types, spatially balanced transect placement, multi-scale sampling, and repeated temporal sampling to capture dynamic landscapes. |
| 2.5 Data Character & Format | Data Types | The form raw evidence takes (time series, spectra, images, counts, qualitative records). | GIS layers, raster images, vector maps, movement tracks, landscape metric tables, spatial interaction matrices, digital elevation models, and spatially explicit habitat-quality maps. |
| | Resolution | The granularity or precision with which data is captured. | Spatial resolution from sub-meter (drone) to tens of meters (satellite), temporal resolution from days to years, and thematic resolution for land-cover classification accuracy. |
| 2.6 Reliability & Calibration | Calibration | Adjustment procedures ensuring instruments produce accurate results. | Calibration of GPS accuracy, drone and satellite imaging parameters, land-cover classification models, sensor alignment, atmospheric correction for remote sensing, and ground-truthing for spatial accuracy. |
| | Error Characterization | Identification and quantification of noise, uncertainty, bias, and measurement error. | Errors from GPS drift, misclassification of land cover, cloud interference in imagery, resolution limits, sampling bias in field validation, and uncertainty in dispersal-path reconstruction. |
| 3. Structural Layer | 3.1 Patterns & Regularities | Laws / Relations | Stable, repeatable patterns governing how observables behave across conditions. | Regularities such as distance–decay relationships, species–area curves, dispersal-distance kernels, fragmentation–connectivity relationships, edge-effect gradients, and spatial autocorrelation laws. |
| | Invariants | Quantities or properties that remain constant under transformations (symmetries, conservation laws). | Persistent spatial features including stable patch mosaics, recurring connectivity patterns, consistent edge responses, conserved dispersal-distance distributions, and predictable clustering of species or habitats. |
| 3.2 Causal Architecture | Mechanisms | Underlying processes or structures that produce the observed regularities. | Mechanisms include dispersal processes, habitat selection, landscape filtering, movement constraints, corridor facilitation, barrier effects, land-use forces, and spatial propagation of disturbances. |
| | Pathways | Organized sequences of interactions forming a causal chain or network. | Sequences such as habitat loss → fragmentation → reduced connectivity → impaired dispersal → altered population structure; or corridor creation → increased movement → enhanced gene flow → improved population persistence. |
| 3.3 Theoretical Vocabulary | Concepts | Core terms that encode the domain’s structure (force, gene, equilibrium, field). | Core terms include fragmentation, connectivity, matrix quality, edge effects, patch dynamics, spatial autocorrelation, dispersal kernel, corridor, barrier, metacommunity, and landscape mosaic. |
| | Classifications | Taxonomies, categories, or typologies that organize entities and relations. | Patch types (core, edge, stepping-stone), landscape configurations (aggregated, dispersed, linear), connectivity classes (low, moderate, high), dispersal modes, and spatial network structures (graphs, clusters, hubs). |
| 3.4 Formal Representations | Equations | Mathematical constructs expressing laws, relations, or mechanisms. | Distance–decay equations, dispersal-kernel functions, landscape-metric formulas (e.g., edge density, patch shape indices), connectivity equations (graph-theoretic metrics), and spatial autoregressive models. |
| | Models | Structured representations—mathematical, computational, or conceptual—used to predict and explain phenomena. | Spatially explicit population models, graph-theory landscape models, GIS-based habitat models, resistance-surface models, least-cost path analyses, circuit-theory connectivity models, and metacommunity models. |
| 3.5 Idealized Structures | Simplified Models | Purposeful abstractions that capture essential dynamics while omitting irrelevant detail. | Binary habitat–matrix models, uniform patch-quality assumptions, simplified dispersal kernels, isotropic movement models, static landscape configurations, or reduced spatial dimensionality. |
| | Limit Conditions | Regimes where specific models or approximations hold (classical vs. quantum, linear vs. nonlinear). | Valid under moderate landscape simplicity, homogeneous movement environments, predictable dispersal behavior, and stable land-use patterns; break down in highly heterogeneous, dynamic, or anisotropic landscapes. |
| 3.6 Integrative Frameworks | Unifying Theories | Higher-order structures that connect disparate laws or mechanisms under a coherent whole. | Spatial ecological theory, metapopulation and metacommunity frameworks, landscape mosaic theory, connectivity theory, and spatial-scaling theory integrating ecological processes with spatial structure. |
| | Interdisciplinary Links | Points where the theory connects to adjacent sciences or larger explanatory systems. | Strong ties to conservation biology, GIScience, remote sensing, landscape planning, population ecology, ecosystem ecology, and climate science via shared focus on spatial structure and environmental dynamics. |
| 4. Method Layer | 4.1 Inquiry Design | Experimental Design | Structured plans for manipulating variables to test causal claims. | Manipulating patch structure, altering habitat configuration, introducing/removing corridors or barriers, modifying land-use patterns at controlled scales, and imposing spatially explicit disturbances to test spatial effects on movement and ecological processes. |
| | Observational Design | Systematic approaches for gathering non-manipulated data (surveys, field studies, natural experiments). | Monitoring spatial patterns through remote sensing, GIS mapping, landscape-change time series, movement tracking, natural experiments (wildfire, land-use shifts), and longitudinal observation of patch dynamics. |
| 4.2 Testing & Validation | Hypothesis Testing | Procedures for evaluating whether evidence supports or contradicts specific claims. | Evaluating predictions about fragmentation effects, connectivity benefits, dispersal routes, edge impacts, spatial autocorrelation, and spatial scaling using spatially explicit data and model comparison. |
| | Replication | The requirement that results be independently reproducible under similar conditions. | Replicating spatial analyses across multiple landscapes, habitat types, temporal windows, independent regions, and using repeated remote-sensing imagery to confirm spatial patterns and metrics. |
| 4.3 Inference & Evaluation | Statistical Inference | Rules for drawing conclusions from noisy or incomplete data. | Using spatial regression, variograms, geostatistics, spatial autoregressive models, landscape-network statistics, multiscale analyses, and Bayesian spatial models to interpret spatial patterns and processes. |
| | Model Comparison | Criteria (fit, simplicity, predictive accuracy, robustness) used to evaluate competing models. | Comparing alternative connectivity models, dispersal-kernel models, resistance-surface models, graph-theoretic representations, spatial regression structures, and landscape-classification algorithms. |
| 4.4 Error Management | Error Analysis | Identification and quantification of random and systematic errors. | Quantifying errors from GPS drift, remote-sensing misclassification, spatial interpolation uncertainty, patch-boundary errors, scale mismatch, atmospheric distortion, and temporal mismatch between data sources. |
| | Bias Control | Methods for minimizing subjective, instrumental, or procedural biases. | Reducing bias through ground-truthing, sensor calibration, standardized land-cover classifications, cross-validation with independent datasets, randomized sampling grids, and consistent spatial-resolution selection. |
| 4.5 Adjudication & Revision | Peer Scrutiny | Collective evaluation of claims through critique, review, and debate. | Independent evaluation of spatial models, GIS layers, classification schemes, connectivity analyses, and landscape interpretations through peer review and cross-study comparison. |
| | Theory Revision | Procedures for modifying, replacing, or discarding models based on new evidence. | Updating theories of fragmentation, connectivity, spatial scaling, and dispersal when new data or improved models reveal unrecognized patterns, nonlinearities, or misinterpreted spatial processes. |
| 4.6 Integrity Conditions | Transparency | Requirements to disclose methods, data, assumptions, and limitations. | Full reporting of spatial-resolution choices, GIS procedures, classification rules, ground-truthing sites, model parameters, movement-tracking protocols, and all assumptions used in spatial analyses. |
| | Ethical Standards | Norms ensuring responsible conduct in experimentation, data handling, and publication. | Responsible use of spatial data, respect for privacy when tracking movement of organisms (including humans where relevant), minimizing disturbance during field validation, and honest reporting of spatial uncertainties. |