Invasive species increase biodiversity and, therefore, services: An argument of equivocations
Bibliographic record
Abstract
Abstract Some critics of invasion biology have argued the invasion of ecosystems by nonindigenous species can create more valuable ecosystems. They consider invaded communities as more valuable because they potentially produce more ecosystem services. To establish that the introduction of nonindigenous species creates more valuable ecosystems, they defend that value is provisioned by ecosystem services. These services are derived from ecosystem productivity, the production and cycling of resources. Ecosystem productivity is a result of biodiversity, which is understood as local species richness. Invasive species increase local species richness and, therefore, increase the conservation value of local ecosystems. These views are disseminating to the public via a series of popular science books. Conservationists must respond to these views, and I outline a method of rejecting such arguments against controlling invasive species. Ecological systems are valuable for more than local productivity and biodiversity is not accurately described by a local species count.
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 | 0.013 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.046 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.008 | 0.010 |
| 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".