Intrapopulation diversity in isotopic niche over landscapes: Spatial patterns inform conservation of bear–salmon systems
Bibliographic record
Abstract
Abstract Intrapopulation variability in resource acquisition (i.e., niche variation) influences population dynamics, with important implications for conservation planning. Spatial analyses of niche variation within and among populations can provide relevant information about ecological associations and their subsequent management. We used stable isotope analysis and kernel‐weighted regression to examine spatial patterns in a keystone consumer–resource interaction: salmon ( Oncorhynchus spp.) consumption by grizzly and black bears ( Ursus arctos horribilis , n = 886; and Ursus americanus , n = 557) from 1995 to 2014 in British Columbia ( BC ), Canada. In a region on the central coast of BC (22,000 km 2 ), grizzly bears consumed far more salmon than black bears (median proportion of salmon in assimilated diet of 0.62 and 0.06, respectively). Males of both species consumed more salmon than females (median proportions of 0.63 and 0.57 for grizzly bears and 0.06 and 0.03 for black bears, respectively). Black bears showed considerably more spatial variation in salmon consumption than grizzlies. Protected areas on the coast captured no more habitat for bears with high‐salmon diets (i.e., proportions >0.5 of total diet) than did unprotected areas. In a continental region (~692,000 km 2 ), which included the entire contemporary range of grizzlies in BC , males had higher salmon diets than females (median proportions of 0.41 and 0.04, respectively). High‐salmon diets were concentrated in coastal areas for female grizzly bears, whereas males with high‐salmon diets in interior areas were restricted to areas near major salmon watersheds. To safeguard this predator–prey association that spans coastal and interior regions, conservation planners and practitioners can consider managing across ecological and jurisdictional boundaries. More broadly, our approach highlights the importance of visualizing spatial patterns of dietary niche variation within populations to characterize ecological associations and inform management.
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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 | 0.002 | 0.002 |
| 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".