Prey‐capture techniques and prey preferences of <i>Zenodorus durvillei, Z. metallescens</i> and <i>Z. orbiculatus</i> , tropical ant‐eating jumping spiders (Araneae: Saiticidae) from Australia
Bibliographic record
Abstract
Abstract Capture techniques and preferences of Zenodorus durvillei (Walckenaer), Z. metallescens (L. Koch) and Z. orbiculatus , Australian salticids that feed on ants in nature, were studied in the laboratory using a wide variety of ants and other insects. Each species adopted three prey‐capture modes: ambush, active pursuit in the open, and gleaning from spider webs. Large ants were sometimes stabbed several times before holding on. A variety of methods were used for testing preference. The potential of using this assortment of methods for assessing strength of preferences is discussed. Each species took dolichoderine, formicine, myrmecine, myrmicine and pseudomyrmecine ants in preference to a variety of other insects (aphids, bugs, caterpillars, crickets, flies, lacewings, mantises, mayflies, midges, mosquitoes, moths, plant and leaf hoppers, and termites). Testing with laboratory‐reared spiders showed that the development of preference for ants and ant‐specific prey‐capture behaviour did not depend on prior experience with ants. Tests with dead, motionless lures showed that each species could distinguish between ants and other types of prey independent of the different movement patterns of the prey. Preferences were intact after 7‐day and 14‐day fasts, but not after 21‐day fasts when prey were outside webs. When prey were in webs, preference for ants persisted even after 21‐day fasts. Findings are discussed in relation to other studies on specialised salticids and in relation to the structure and function of the salticid eye.
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 | 0.001 | 0.000 |
| 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 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".