The effects of roadways on lakes and ponds: a systematic review and assessment of knowledge gaps
Bibliographic record
Abstract
As the global population increases, the expansion of road networks has led to the destruction and disturbance of terrestrial and aquatic habitats. Road-related stressors have significant effects on both lotic and lentic habitats. While there are several systematic reviews that evaluate the effects of roads on lotic environments, there are none that consider their effects on lentic habitats only. We conducted a literature review to achieve two objectives: (1) to summarize the effects of roads on the physical, chemical, and biological properties of lentic environments; and (2) to identify biases and gaps in our current knowledge of the effects of roads on lentic habitats, so that we could find promising areas for future research. Our review found 172 papers published between 1970 and 2020. The most frequently studied stressors associated with roads included road salt and heavy metal contamination (67 and 43 papers, respectively), habitat fragmentation (37 papers), and landscape change (14 papers). These stressors can lead to alterations in conductivity and chloride levels, changes in lake stratification patterns, increases in heavy metal concentrations in water and organisms, and significant mortality as amphibians disperse across roadways. We also identified a variety of other stressors that may be understudied based on their frequency of appearance in our search results, including polycyclic aromatic hydrocarbons, road dust, increased accessibility, hydrological changes, noise pollution, dust suppressants, sedimentation, invasive species introductions, and water withdrawal. Our review indicated that there are strong geographic biases in published studies, with 57.0% examining North American sites and 30.2% examining European sites. Furthermore, there were taxonomic biases in the published literature, with most studies focusing on amphibians (41.7%), fish (15.6%), and macroinvertebrates (14.6%), while few considered zooplankton (8.3%), diatoms (7.3%), amoebas (5.2%), water birds (3.1%), reptiles (2.1%), and macrophytes (1.0%). Based on our review, we have identified promising areas for future research for each of the major stressors related to roadways. However, we speculate that rectifying the geographic and taxonomic bias of our current knowledge could significantly advance our understanding of the impacts of roads on lentic environments, thereby better informing environmental management of these important habitats.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".