Rethinking landscape ecological risk assessment and its applicability: Counterintuitive findings from coastal areas
Bibliographic record
Abstract
Abstract Landscape ecological risk assessment (LERA) serves as a crucial tool for guiding effective environmental management. However, the conventional approach of LERA suffers from two notable drawbacks: the utilization of low‐resolution land‐use data (e.g., 30 × 30 m) and the application of arbitrary evaluation units (e.g., uniformly‐sized grids), both of which introduce uncertainty and inaccuracies into the assessment outcomes. Moreover, the extent to which the traditional LERA accurately reflects the true ecological risk level remains unexplored. To address these limitations, this study presents a modified LERA conducted in Xiapu, a coastal county in China, spanning the years 2013–2015. Fine‐grained land‐use data were employed to overcome the shortcomings of low‐resolution data. Additionally, spatial correlations between land‐use changes and ecological risk alterations were analyzed to unravel the mechanisms behind land‐use changes' impact on ecological risk, while also testing the accuracy of LERA results. Major findings can be summarized as follows: (1) Ecological risk changes in Xiapu during 2013–2015 were relatively minor, with high‐risk areas predominantly concentrated along the coast. (2) A total of 1137 ha of land in Xiapu County experienced changes, with construction land witnessing the most substantial increase. (3) Counterintuitive and unreasonable LERA outcomes were identified, particularly pertaining to illogical ecological risk changes arising from transformations between construction and non‐construction land. (4) Based on the counterintuitive findings, potential factors affecting the limitations and applicability of LERA were discussed. This study represents the first critical examination of the limitations of LERA, offering valuable insights to stimulate future researchers to rethink LERA and emphasize the importance of validating assessment outcomes during its application.
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".