People, Plants, and Pollinators: The Conservation of Beargrass Ecosystem Diversity in the Western United States
Bibliographic record
Abstract
Biodiversity conservation often focuses on strategies that aim to protect a species from extinction and to preserve its functional role within an ecosystem. In this chapter we adopt a broader view of conserving biodiversity that calls for conservation of the ecological and social roles of a species within an ecosystem, which we understand to include humans. Viewed as such, biodiversity conservation entails sustaining ecosystem diversity to support both a species and the web of interdependent social and ecological relations in which it is embedded. Hence, if one component of the ecosystem diversity associated with a species is threatened, conservation interventions may be warranted, even if the species itself is not (yet) threatened or endangered. Thus, biodiversity conservation is not only about preventing the extinction of a species, but also about preserving the diversity of its functional roles -both ecological and social -to sustain biocultural diversity. Conservation strategies based on knowledge about how people affect and interact with the natural disturbance processes that influence ecosystem diversity are more likely to be successful than strategies that focus on only one or the other (e.g., anthropogenic or natural disturbance). Because the niche (both social and ecological) of a species may vary across its range depending on local disturbance regimes and local sociocultural practices, conservation needs and strategies are also likely to vary across its range. For this reason, traditional and local ecological knowledge can make an important contribution to the conservation of ecosystem diversity. We selected beargrass (Xerophyllum tenax (Pursh) Nutt) to illustrate these points. Beargrass is a perennial monocot with distinctive slim, evergreen leaves and a tall flower spike (Fig. Its range lies in the western United States and southwestern Canada, with two disjunct distributions: (1) from the Coast and Sierra Nevada mountain ranges in California north through Oregon's Coast and Cascade mountain range, to the Olympic Peninsula and Cascade Mountains in northwestern Washington; and (2) from the Rocky Mountains in Idaho, Montana, and northwestern Wyoming north to southeastern British Columbia and southwestern Alberta Provinces in Canada The coastal portion of this range is influenced by a maritime or mediterranean climate, while the interior portion is continental. Throughout its entire range, beargrass provides food and habitat for several animals and pollinating insects. Beargrass also has www.intechopen.com
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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 | 0.000 |
| 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".