A geospatial approach to identifying and mapping areas of relative environmental pressure on ecosystem integrity
Bibliographic record
Abstract
The influence of increasing anthropogenic pressure on ecosystem integrity, such as land use change, is resulting in many ecosystems experiencing a decline in their ability to maintain balanced functions and services. Identifying and quantifying these pressures over different scales is challenging and thus impacting the achievement or maintenance of key environmental outcomes. In this study, a GIS-based and scalable tool was developed, the Relative Environmental Pressure (REP) Tool, to address these challenges. The REP tool combines an ecosystem integrity conceptual framework with a weighted linear combination analysis to quantify and rank relative environmental pressure across the scale of interest. The REP Tool was developed as an automated Python-based model in a PyCharm working environment using ArcGIS Pro Arcpy scripting. The REP Tool was applied to spatially contiguous geospatial data for the Province of Alberta, Canada and dynamically scaled relative to Hydrologic Unit Codes at level 8 (HUC8) along with regional and sub-regional scale sub-watersheds. Both cumulative and individual relative pressure levels were calculated and mapped for specific ecosystem integrity framework-derived Environmental Pressure Groups (EPGs) including Atmospheric Alteration, Sedimentation, Habitat Alteration, Hydrologic Alteration, and Social Pressure. Data driven Jenks natural breaks were then applied to classify the relative environmental pressures into a nine-level ranking system. The resulting visualization and data outputs from the REP Tool clearly show that the highest cumulative relative environmental pressure values align with the distribution of major population centres, zones of intense agriculture and major industrial activity. These regions reflect the physiography of Alberta with the Rocky Mountain and Boreal natural regions dominated by low relative environmental pressure. As scales become smaller and more refined, the location of the higher relative environmental pressure levels typically become more subdivided with greater spatial precision where higher pressured areas are located. These patterns are repeated when looking at individual EPGs but with enhanced differentiation of pressure as scales are refined. The framework and geospatial science driven approach behind the REP Tool can be universally applied to support enhanced understanding of relative environmental pressures in, or between regions, as well as informing adaptive environmental resource management and monitoring activities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".