Competitive overlap between martens<i>Martes americana</i>and<i>Martes caurina</i>and fishers<i>Pekania pennanti</i>: a rangewide perspective and synthesis
Bibliographic record
Abstract
Abstract Competition is a major determinant of where species occur and how species interact. Among carnivorans, interspecific competition is particularly apparent, as many of these species have evolved to be efficient killers. Theoretically, phylogenetically related carnivorans that occupy seasonal habitats, share common resources, and differ in body size by a factor of 2.5–10× should exhibit the most interference competition. FishersPekania pennantiand martensMartes americanaandMartes caurinaare members of the subfamily Guloninae (Mustelidae, Carnivora) that occupy forests throughout northern North America. These taxa occur sympatrically throughout much of their range, utilise similar habitats, and consume similar prey; fishers and martens also differ in body size by a factor 2–5×. Consequently, these two taxa appear to be locked in particularly strong interspecific competition and should attempt to limit competitive overlap. We review the current knowledge of this dyadic interaction in the framework of ecological niches and niche partitioning. In particular, we explore the three critical niche axes of diet, space, and time. We found that, in contrast to the traditional view of them being highly specialised, both martens and fishers are dietary generalists; however, they also appear to be specialists in complexity, at least in space and habitats. Collectively, martens and fishers exhibit high degrees of diet and habitat niche overlap across their ranges, and this overlap is likely to have the greatest fitness consequences for the smaller and subordinate martens. Nevertheless, fine‐scale habitat and prey partitioning, and especially partitioning along snow clines, seem to be the mechanisms by which these two taxa can coexist. We predict that rapid ecological change – especially from increasingly homogenised forests and prey communities, as well as from declining snow cover and snowpack due to climate warming – is likely to destabilise marten–fisher coexistence. As the climate continues to change, fishers and martens are likely to experience distributional and numerical shifts and increased isolation at their southern range boundaries, and vulnerable populations – especially of martens – will be driven to local extirpation.
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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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".