Potential environmental influences on biological control : can drought improve success and do congeners preferentially exploit different habitats?
Bibliographic record
Abstract
Climate change is a major threat to global biodiversity, and will alter species ranges, plant and animal phenology, and species interactions within ecosystems. Climate mediates plant-insect interactions, and consequently has the potential for positive or negative effects on biological control systems. Observational evidence suggests that a recent dramatic reduction in the density of diffuse knapweed, Centaurea diffusa Lamarck, in sites in British Columbia, Canada is attributed to the biological agent Larinus minutus Gyllenhal (Coleoptera: Curculionidae). This decline took place over several years of late spring and summer drought which suggested an association between dry conditions and successful biological control. To explore this, I conducted field experiments using rain shelters and watering treatments to assess the effectiveness of plant attack by L. minutus under moist and dry conditions. I found that L. minutus reduced seed production regardless of moisture conditions, with a trend towards greater seed reduction under dry conditions. Two or more species of insects in the same genera have been introduced in some weed biocontrol programs. If the species are ecological equivalents and compete the introduction of both species may be detrimental and reduce their impacts. If however the species vary in their distributions, the introduction of congeners may be advantageous. I review the following cases of species pair releases: the beetles Chrysolina quadrigemina (Suffrian) and Chrysolina hyperici (Forster) for St. John’s wort (Hypericum perforatum L.); the gallflies Urophora affinis Frfld. and Urophora quadrifasciata (Meig.) for Centaurea species; the weevils Neochetina bruchi Hustache and Neochetina eichhorniae Warner for water hyacinth (Eichhornia crassipes (Mart.) Solms.) and the beetles Galerucella pusilla Duftschmidt and Galerucella calmariensis L. for purple loosestrife (Lythrum salicaria L.). I found that congeneric agents can offer complementary control of target weeds in slightly different habitats. Given the increasing focus on possible non-target effects of biological control introductions, I recommend that greater care be taken to avoid mixed species introductions and that judicious use be made of controlled field experimentation to determine species impacts. Molecular studies of species before introduction could help prevent the accidental introduction of multiple species.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".