A benefit–risk analysis for biological control introductions based on the protection of native biodiversity
Bibliographic record
Abstract
The release of biological control agents has been an important means of controlling invasive species for over 150 years. While these releases have led to the sustainable control of over 250 invasive pest and weed species worldwide, a minority have caused environmental harm. A growing recognition of the risks of biological control led to a focus on risk assessment beginning in the 1990s along with a precipitous decline in releases. While this new focus greatly improved the safety of biological control, it came at the cost of lost opportunities to solve environmental problems associated with invasive species. A framework that incorporates benefits and risks of biological control is thus needed to understand the net environmental effects of biological control releases. We introduce such a framework, using native biodiversity as the common currency for both benefits and risks. The model is based on interactions among four categories of organisms: (1) the biological control agent, (2) the invasive species (pest or weed) targeted by the agent, (3) one or more native species that stand to benefit from the control of the target species, and (4) one or more native species that are at risk of being harmed by the released biological control agent. Conservation values of the potentially benefited and harmed native species are incorporated as well, and they are weighted according to three axes: vulnerability to extinction, the ecosystem services provided, and cultural significance. Further, we incorporate the potential for indirect risks to native species, which we consider will result mainly from the ecological process of agent enrichment that may occur if the agent exploits but does not control the target pest or weed. We illustrate the use of this framework by retrospectively analyzing the release of the vedalia beetle, Novius (= Rodolia) cardinalis, to control the cottony cushion scale, Icerya purchasi, in the Galapagos Islands. While the framework is particularly adaptable to biological control releases in natural areas, it can also be used in managed settings, where biological control protects native species through the reduction of pesticide use.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".