Coral degradation impairs learning of non‐predators by Whitetail damselfish
Bibliographic record
Abstract
Abstract A prerequisite for effective antipredator responses is the ability of a prey to distinguish animals that pose a threat from those that do not. Prey often have efficient learning mechanisms to learn threats but learning to recognize nonpredators may be equally or more important. Moreover, the ability to generalize learned information is of key importance for prey animals. Prey take information they know about one species to make ‘educated guesses’ about the predatory/nonpredatory status of other unknown species. Here, we investigate the ability of Whitetail damselfish Pomacentrus chrysurus to learn the identity of non‐predators and then generalize their responses to other unknown animals. Our work is completed within the context of unprecedented habitat degradation in reef ecosystems. When corals die, the remaining skeleton is colonized by algae, cyanobacteria and sessile invertebrates. These opportunistic colonists change the physical and chemical landscape of the reef and hence the background odour in which predator and non‐predator recognition occurs. Our results indicated that Whitetail damselfish learn to classify Moonwrasse Thalasomma lunare as a non‐predator through the process of latent inhibition, whereby the prey are repeatedly exposed to Moonwrasse odour multiple times in the absence of negative reinforcement. These fish subsequently generalized their nonpredator recognition to other unknown wrasse, but not distantly related fish. Of key importance was our finding that the patterns and extent of non‐predator learning and generalization were dramatically altered in dead coral habitats. As predicted, prey that learned the Moonwrasse as a nonpredator in live coral environments did not subsequently respond to Moonwrasse when we tried to teach them Moonwrasse was a predator in live coral. However, this non‐predator recognition was reduced in dead coral environments. Moreover, generalization completely failed when we changed from live to dead coral environments. Juvenile damselfishes need to rapidly catalogue the identity of unknown animals when they arrive at a reef. Changing background odours, that occur with changing tides and currents, means that prey need to learn non‐predator identities separately in each water source. This cognitive challenge likely has significant survival consequence in a changing environment. A free Plain Language Summary can be found within the Supporting Information of this article.
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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 | 0.000 | 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) | 0.006 | 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".