Urbanisation of floodplain ecosystems: Weight-of-evidence and network meta-analysis elucidate multiple stressor pathways
Bibliographic record
Abstract
Freshwater floodplains are dynamic, diverse ecosystems that represent important transition zones between terrestrial, riparian, subsurface and aquatic habitats. Given their historic importance in human development, floodplains have been exposed to a variety of pressures, which in combination have been instrumental in driving changes within these ecosystems. Here, we present an evidence-based framework to explore direct and indirect effects of pressures and stressors on floodplain ecosystems and test this structure within the urban landscape. Evidence was obtained from peer-reviewed scientific literature, focusing on effects of key pressures and stressors on receptors, including species composition (e.g., species presence-absence, diversity) and ecosystem function (e.g., biomass, decomposition). The strength of direct and indirect effects of individual and multiple stressors on biological receptors was quantified using two separate analyses: an evidence-weighted analysis and a quantitative network meta-analysis using data extracted from 131 studies. Results demonstrate the power of adopting a systematic framework to advance quantitative assessment of floodplain ecosystems affected by multiple stressors. While direct pathways were generally stronger and provided the core network skeleton, there were many more significant indirect pathways indicating evidence gaps in our mechanistic understanding of these processes. Indeed, the importance of indirect pathways (e.g. increase in impervious surface → increase in the accumulation rate of sediment nutrients) suggests that embracing complexity in network meta-analysis is a necessary step in revealing a more complete snapshot of the network. Results from the weight-of-evidence approach generally mirrored the direct pathway structure and demonstrated the strength of incorporating study quality alongside data sufficiency. Networks illustrated novel disturbance pathways (e.g., decrease in habitat structure → decrease in structure and function of aquatic and riparian assemblages) that can be used for hypothesis generation for future scientific enquiries. Our results highlight the broader applicability of adopting the proposed framework for assessing complex environments, such as floodplains.
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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 | 0.040 | 0.087 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.017 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".