Ecosystem Health and Its Measurement at Landscape Scale: Toward the Next Generation of Quantitative Assessments
Bibliographic record
Abstract
ABSTRACT The purpose of this paper is twofold: (A) to describe the challenges of reporting on changes in ecosystem health at landscape scales, and (B) to review the statistical and mathematical techniques that allow the derivation of landscape health assessments from a variety of data consisting of remote sensing imagery, demographic and socioeconomic censuses, natural resource surveys, long‐term ecological research, and other geospatial information that is site specific. We draw upon seven innovative and integrative concepts and tools that together will provide the next generation of ecosystem health assessments at regional scales. The first is the concept of ecosystem health, which integrates across the social, natural, physical, and health sciences to provide the basis for comprehensive assessments of regional environments. The second consists of innovative stochastic techniques for representing human disturbance and ecosystem response in landscapes, and the corresponding statistical tools for analyzing them. The third constitutes representation of spatial biocomplexity in landscapes through application of echelon analysis to assessment. The fourth concerns innovative combination techniques of upper‐echelon‐based spatial scan statistic to detect, delineate, and prioritize critical study areas for evaluating and prioritizing causal factors and effects. The fifth involves the capability of comparing and prioritizing a collection of entities in light of multiple criteria, using poset mathematics of partial order with rank frequency statistics, to provide multicriterion decision support. The sixth lies in extending data mining and visualization techniques to determine associations between geospatial patterns and ecosystem degradation at landscape scales. The seventh encompasses comprehensive studies conducted on different types of regional ecosystems. Our focus is to show how the integration of recent advances in quantitative techniques and tools will facilitate the evaluation of ecosystem health and its measurement at a variety of landscape scales. The challenge is to characterize, evaluate, and validate linkages between socioeconomic drivers, biogeochemical indicators, multiscale landscape pattern metrics, and quality of human life indicators. Initial applications of these quantitative techniques and tools have been with respect to regions in the eastern United States, including the U.S. Atlantic Slope and mid‐Atlantic region.
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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 | 0.032 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".