Influence of hybridization on niche shifts in expanding coyote populations
Bibliographic record
Abstract
Abstract Aim The degree to which niches of species change over time or space has important implications for ecology and evolutionary biology. However, conditions that give rise to niche shifts remain poorly understood. In particular, the relative influence of release from predation or competition (change in realized niche) vs. genetically based alterations (change in fundamental niche) has received little attention. We studied niche shifts in expanding coyote (Canis latrans) populations. During expansion from their historic range, coyotes experienced marked changes in competitive regimes and also genetic changes (i.e. hybridization), and these alterations occurred unevenly across the expanding front. The goal of this study was to determine the presence and degree of niche shifts in expanding coyote populations and the potential influence of hybridization on these patterns. Location North America. Methods We developed species distribution models and compared climatic niche overlap between historic and expanding coyote populations with different levels of genetic introgression with wolves. We also developed harvest models to examine differences in how coyote and coyote hybrids responded to human disturbance and land use. Results Niche differentiation is most pronounced for expanding coyote populations that have experienced substantial hybridization with wolves. An examination of response curves suggests that coyotes in the north‐east may have shifted to a more wolflike niche. Main conclusions Hybridization or other processes that promote genetic changes in populations may be associated with niche shifts. These findings help explain why hybridization often leads to increased invasiveness, and further support the notion that genetic differentiation below the level of species can lead to niche divergence. Our results also highlight the caution needed when predicting distributional changes due to invasion or climate change, particularly among populations exposed to genetic change.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".