Moon phase and nocturnal activity of native Australian mammals
Bibliographic record
Abstract
Moon phase and variation in ambient light conditions can influence predator and prey behaviour. Nocturnal predators locate prey visually, and prey may adjust their activity to minimise their predation risk. Understanding how native mammals in Australia respond to varying phases of the moon and cloud cover (light) enhances knowledge of factors affecting species’ survival and inference regarding ecological and population survey data. Over a two-year period within a fenced conservation reserve, in south-eastern Australia, with reintroduced native marsupial predator and prey species (eastern barred bandicoot, southern brown bandicoot, long-nosed potoroo, rufous bettong, Tasmanian pademelon, brush-tailed rock-wallaby, red-necked wallaby, eastern quoll, spotted-tailed quoll, and naturally occurring swamp wallaby, common brushtail possum, common ringtail possum), we conducted monthly spotlight surveys during different moon phases (full, half and new moon). We found an interaction between cloud cover and moon phase, and an interaction of the two depending on the mammal size and class. Increased activity of prey species corresponded with periods of increasing cloud cover. Predators and medium-sized herbivores were more active during times of low illumination. Our findings suggest that moon phase affects the nocturnal activity of mammal species and that, for prey species, there might be trade-offs between predation risk and foraging. Our findings have implications for: ecological survey design and interpretation of results for mammal populations across moon phases, understanding predator and prey behaviour and interactions in natural and modified (artificial lighting) ecosystems, and potential nocturnal niche partitioning of 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 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 | 0.001 |
| 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".