Anthropogenic Change and the Process of Speciation
Bibliographic record
Abstract
Anthropogenic impacts on the environment alter speciation processes by affecting both geographical contexts and selection patterns on a worldwide scale. Here we review evidence of these effects. We find that human activities often generate spatial isolation between populations and thereby promote genetic divergence but also frequently cause sudden secondary contact and hybridization between diverging lineages. Human-caused environmental changes produce new ecological niches, altering selection in diverse ways that can drive diversification; but changes also often remove niches and cause extirpations. Human impacts that alter selection regimes are widespread and strong in magnitude, ranging from local changes in biotic and abiotic conditions to direct harvesting to global climate change. Altered selection, and evolutionary responses to it, impacts early-stage divergence of lineages, but does not necessarily lead toward speciation and persistence of separate species. Altogether, humans both promote and hinder speciation, although new species would form very slowly relative to anthropogenic hybridization, which can be nearly instantaneous. Speculating about the future of speciation, we highlight two key conclusions: (1) Humans will have a large influence on extinction and "despeciation" dynamics in the short term and on early-stage lineage divergence, and thus potentially speciation in the longer term, and (2) long-term monitoring combined with easily dated anthropogenic changes will improve our understanding of the processes of speciation. We can use this knowledge to preserve and restore ecosystems in ways that promote (re-)diversification, increasing future opportunities of speciation and enhancing biodiversity.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".