Evaluating functional diversity conservation for freshwater fishes resulting from terrestrial protected areas
Bibliographic record
Abstract
Abstract Protected areas are one of the hammers in conservation toolkits, yet few protected areas exist that were designed to protect freshwater ecosystems. This is problematic as freshwater ecosystems are among the most threatened systems on earth. Nonetheless, terrestrial protected areas ( TPA s) may afford spill‐over benefits to freshwater ecosystems included within their boundaries, but evaluations of these potential benefits for the protection of freshwater fish diversity are lacking. Using fish community data from 175 lakes inside, outside, or bordering TPA s in Ontario, Canada, we sought to determine if TPA s preserve fish functional diversity. We focused on functional diversity because previous work indicated no taxonomic differences between these lakes, but a difference in normalised‐length size‐spectra slopes inside versus outside TPA s (indicator of unique predator–prey ratios and trophic energy transfer). We expected that communities inside TPA s would show greater functional diversity (i.e. functional dispersion and functional richness) and have more extreme trait combinations (i.e. functional divergence) than communities outside or bordering TPA s. We also tested for differences in the rarity of species‐specific functional traits between fish communities inside, outside, or bordering TPA s, between thermal guilds, and across average body size and overall prevalence of the species. Our results indicated no significant differences in functional diversity among lake fish communities inside, outside, or bordering TPA s. However, fish communities inside TPA s had more extreme trait combinations than outside TPA s because abundant species in lake communities outside TPA s had more ubiquitous trait combinations than abundant fishes inside TPA s. Small‐bodied species showed greater functional rarity than large‐bodied species, indicating that small‐bodied fishes fill functionally unique roles while the most prevalent, large‐bodied species possess a more generalist set of traits. Overall, the similarity of functional diversity metrics for lake fish communities inside, outside, or bordering TPA s in Ontario suggests that TPA s capture the functional diversity of Ontario's lake fish communities. However, we encourage similar evaluations in regions where environmental conditions and stressors are more distinct across TPA boundaries than they are in Ontario, as these types of evaluations will inform guidelines for the design of freshwater protected areas and monitoring of their effectiveness in the future.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".