Bibliographic record
Abstract
Abstract The processes that drive the evolution of specialisation are still poorly understood. While theory generally invokes the assumption that generalists bear costs compared to species that have a less intense exploitation of specific resources, the existence of these costs have not received strong empirical support. Recent studies have broadened the dialogue by examining specialisation from a macroevolutionary perspective. Are specialist lineages transitioning towards a broader niche under certain environmental conditions, or are they limited in their evolutionary potential and doomed to eventual extinction? The overall finding is that context dependency is key to driving rapid transitions in niche dimensions and composition. Answers to these questions are fascinating in terms of better understanding the natural world and also have the potential to improve prioritisation of future conservation efforts. Key Concepts Many ideas about the evolution of specialisation rely on the premise that ‘Jack of all trades is master of none’, that is, generalists are locally outperformed by specialists. Genetic or physiological trade‐offs imply that populations experience reduced performance in one function when selection results in increased performance on another function (antagonistic pleiotropy). The evolution of specialisation depends on how often alternate hosts or environments are encountered, which depends on abundance and range extent of a species and its neighbours. Biogeography, functional trait distributions and range limits create ‘forbidden links’ by precluding pairs of species, or a species and a particular suite of environmental variables, from coexisting. The idea of specialisation applies equally to a species biotic and abiotic niche, yet the expectations for how specialisation may evolve for each aspect of niche may differ. Evolutionary transitions from specialisation to generalisation are common (and vice versa), yet specialists may experience greater extinction rates due to smaller geographical range. The evolutionary ecology of specialisation is emerging as a key component in studies of host–pathogen evolution and conservation biology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".