A test of the umbrella species approach in restored floodplain ponds
Bibliographic record
Abstract
Summary The umbrella species approach, where conservation actions targeted for one or a group of species should benefit the broader community, may provide an effective framework to guide habitat restoration. This requires congruence in the response of umbrella and co‐occurring species to environmental stress and recovery, and the identification of potential mechanisms by which co‐occurring species benefit from conservation of an umbrella species. Past evaluations of this approach have considered only the presence/absence of umbrella species. In addition to the presence/absence, we quantified abundance and biomass of both umbrella and co‐occurring species to support a more quantitative evaluation of species co‐occurrence. Floodplain ponds are restored in B ritish C olumbia and the P acific N orthwest to benefit coho salmon O ncorhynchus kisutch, a presumptive umbrella species. To test its effectiveness as an umbrella species, we assessed the relationships between species richness, abundance and biomass of aquatic vertebrates, including vertebrates of conservation concern (‘listed’), and benthic invertebrates and the abundance and biomass of juvenile coho. We used ordination to evaluate relationships between species' abundance and biomass and environmental attributes. Positive relationships were identified between coho abundance and biomass and species richness, abundance and biomass of fish and listed species. These relationships were negative for benthic invertebrates. Listed species were located close to coho in ordinations, suggesting they respond to similar environmental features while benthic invertebrates clustered away from coho. Synthesis and applications . We found that where our umbrella species coho is most productive, so are other listed species and fish in general, a relationship that would not have been evident had evaluated species richness alone. We also reported strong relationships between some environmental features manipulated in the habitat restoration and the presence and productivity of coho and co‐occurring species. Our study demonstrates the umbrella species approach has potential to guide habitat restoration when there is congruence in the response of umbrella and co‐occurring species to environmental attributes that can be manipulated in the restoration. Where this is possible, one can use restoration designed for one or several umbrella species and successfully restore habitats that are viable for other species, including listed species.
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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) | 0.000 | 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 | 0.000 |
| 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".