Bibliographic record
Abstract
Background Interspecific competition affects species fitness, community assemblages and structure, and the geographic distributions of species. Established dominance hierarchies among species mitigate the need for fighting and contribute to the realized niche for subordinate species. This is especially important for apex predators, many of which simultaneous contend with the costs of competition with more dominant species and the costs associated with human hunting and lethal management. Methods Pumas are a widespread solitary felid heavily regulated through hunting to reduce conflicts with livestock and people. Across their range, pumas overlap with six apex predators (gray wolf, grizzly bear, American black bear, jaguar, coyote, maned wolf), two of which (gray wolf, grizzly bear) are currently expanding in North America following recovery efforts. We conducted a literature search to assess whether pumas were subordinate or dominant with sympatric apex predators, as well as with three felid mesocarnivores with similar ecology (ocelot, bobcat, Canada lynx). We also conducted an analysis of the spatial distributions of pumas and their dominant sympatric competitors to estimate in what part of their range, pumas are dominant versus subordinate. Results We used 64 sources to assess dominance among pumas and other apex predators, and 13 sources to assess their relationships with felid mesocarnivores. Evidence suggested that wolves, grizzly bears, black bears, and jaguars are dominant over pumas, but that pumas are dominant over coyotes and maned wolves. Evidence suggested that pumas are also dominant over all three felid mesocarnivores with which they share range. More broadly, pumas are subordinate to at least one other apex carnivore in 10,799,252 (47.5%) of their 22,735,268 km 2 range across North and South America. Discussion Subordinate pumas change their habitat use, suffer displacement at food sources, likely experience increased energetic demands from harassment, exhibit increased starvation, and are sometimes directly killed in competitive interactions with dominant competitors. Nevertheless, we lack research clearly linking the costs of competition to puma fitness. Further, we lack research that assesses the influence of human effects simultaneous with the negative effects of competition with other sympatric carnivores. Until the time that we understand whether competitive effects are additive with human management, or even potentially synergistic, we encourage caution among managers responsible for determining harvest limits for pumas and other subordinate, apex carnivores in areas where they are sympatric with dominant species. This may be especially important information for managers working in regions where wolves and brown bears are recolonizing and recovering, and historic competition scenarios among multiple apex predators are being realized.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".