Ranking the host range of biological control agents with quantitative metrics of taxonomic specificity
Bibliographic record
Abstract
The need to prevent negative impacts of importation biological control on native biodiversity has made evaluating the specificity (host range) of natural enemies a central issue for the application of biological control programs against pest organisms. Thus, when there are a number of candidate species being considered for introduction as biological control agents against a particular target pest, it is important to compare their relative host ranges. These comparisons are usually made informally using categorical terminology (e.g., generalist, specialist, oligophagous). However, relative differences in natural enemy host range are better expressed quantitatively, with both ecological (how many host species a natural enemy is capable of exploiting) and evolutionary (how the host species are related phylogenetically) components. We propose using two previously developed quantitative taxonomic and phylogenetic metrics (taxonomic host range – STD; phylogenetic species variability – PSV), in combination with the number of associated host species (host species richness), as heuristic tools to rank the relative host specificity of candidate biological control agents. We first show that although STD and PSV were developed independently, they are directly related mathematically and convey exactly the same information except on different numerical scales. We then apply the metrics to case studies regarding the host range of candidate biological control agents for: (i) an invasive whitefly in greenhouse crops, Aleurotrachelus trachoides (Hemiptera: Aleyrodidae), and (ii) an invasive pest of grapes, Lobesia botrana (Lepidoptera: Tortricidae). We also provide open source code to easily allow the calculation of these metrics for any natural enemy for which taxonomic host range information is available. Finally, we describe potential uses of these metrics in applied biological control as well as important caveats and limitations that require further theoretical work to address. We conclude that while these metrics should not be considered as absolute or infallible measurements of host range, their application should encourage biological control practitioners to explicitly consider the phylogenetic component of host range when ranking prospective candidate biological control agents.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| 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) | 0.001 | 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".