Navigating the landscape of fear: Fruit flies exhibit distinct antipredator and antiparasite defensive behaviors
Bibliographic record
Abstract
Most organisms are at risk of being consumed by a predator or getting infected by a parasite at some point in their life. Theoretical constructs such as the landscape of fear (perception of risk) and nonconsumptive effects (NCEs, costly responses sans predation or infection) have been proposed to describe and quantify antipredator and antiparasite responses. How prey/host species identify and respond to these risks determines their survival, reproductive success and, ultimately, fitness. Most studies to date have focused on either predator-prey or parasite-host interactions, yet habitats and ecosystems contain both parasitic and/or predatory species that represent a complex and heterogenous mosaic of risk factors. Here, we experimentally investigated the behavioral responses of a cactophilic fruit fly, Drosophila nigrospiracula, exposed to a range of species that include parasites (ectoparasitic mite), predators (jumping spiders), as well as harmless heterospecifics (nonparasitic mites, ants, and weevils). We demonstrate that D. nigrospiracula can differentiate between threat and non-threat species, increase erratic movements and decrease velocity in the presence of parasites, but decrease erratic movements and time spent grooming in the presence of predators. Of particular importance, flies could distinguish between parasitic female mites and nonparasitic male mites of the same species, and respond accordingly. We also show that the direction of these NCEs differs when exposed to parasitic mites (i.e., risk of infection) versus spiders (i.e., risk of predation). Given the opposing effects of predation versus infection risk on fly behavior, we discuss potential trade-offs between parasite and predator avoidance behaviors. Our findings illustrate the complexity of risk assessment in a landscape of fear and the fine-tuned NCEs that arise in response. Moreover, this study is the first to examine these behavioral NCEs in a terrestrial system.
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 | 0.000 | 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 | 0.000 |
| 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 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".