Landscape consequences of aggregation rules for functional equivalence in compensatory mitigation programs
Bibliographic record
Abstract
Mitigation and offset programs designed to compensate for ecosystem function losses due to development must balance losses from affected ecosystems with gains in restored ecosystems. Aggregation rules applied to ecosystem functions to assess site equivalence are based on implicit assumptions about the substitutability of functions among sites and can profoundly influence the distribution of restored ecosystem functions on the landscape. We investigated the consequences of rules applied to the aggregation of ecosystem functions for wetland offsets in the Beaverhill watershed in Alberta, Canada. We considered the fate of 3 ecosystem functions: hydrology, water purification, and biodiversity. We set up an affect-and-offset algorithm to simulate the effect of aggregation rules on ecosystem function for wetland offsets. Cobenefits and trade-offs among functions and the constraints posed by the quantity and quality of restorable sites resulted in a redistribution of functions between affected and offset wetlands. Hydrology and water purification functions were positively correlated with one another and negatively correlated with biodiversity function. Weighted-average rules did not replace functions in proportion to their weights. Rules prioritizing biodiversity function led to more monofunctional wetlands and landscapes. The minimum rule, for which the wetland score was equal to the worst performing function, promoted multifunctional wetlands and landscapes. The maximum rule, for which the wetland score was equal to the best performing function, promoted monofunctional wetlands and multifunctional landscapes. Because of implicit trade-offs among ecosystem functions, no-net-loss objectives for multiple functions should be constructed within a landscape context. Based on our results, we suggest criteria for the design of aggregation rules for no net loss of ecosystem functions within a landscape context include the concepts of substitutability, cobenefits and trade-offs, landscape constraints, heterogeneity, and the precautionary principle.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".