Temporal change in urban fish biodiversity—Gains, losses, and drivers of change
Bibliographic record
Abstract
Abstract Our aim was to examine temporal change in alpha and beta diversity of freshwater fish communities in rivers that have urbanized over the same period to understand the influence of changes in land use and river connectivity on community change. We used biological (2001–2018), land use (2000–2015), and connectivity data (1987–2017) from Toronto, Ontario, Canada. We used linear mixed effects models to determine the strength of upstream land use, connectivity, and their changes over time to explain temporal change in alpha and beta diversity indices. We examined beta diversity using the temporal beta diversity index (TBI) to assess site‐specific community change. The TBI was partitioned into gains and losses, and species‐specific changes in abundance were assessed using paired t‐tests. There were more gains than losses across the study sites as measured by TBI. We found little to no significant differences in species‐specific abundances at aggregated spatial scales (study region, watershed, stream order). We found different relationships between landscape and connectivity variables with the biodiversity indices tested; however, almost all estimated confidence intervals overlapped with zero and had low goodness‐of‐fit. More fish biodiversity gains than losses were found across the study region, as measured by TBI. We found TBI to be a useful indicator of change as it identifies key sites to further investigate. We found two high value TBI sites gained non‐native species, and one site shifted from a cool‐water to warm‐water species dominated community, both of which have management implications. Upstream catchment land use and connectivity had poor explanatory power for change in the measured biodiversity indices. Ultimately, such spatial–temporal datasets are invaluable and can reveal trends in biodiversity useful for environmental management when considering competing interests involved with urban sprawl in the ongoing “Decade on Restoration.”
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".