Small mammal populations in a eucalypt forest affected by fire and drought. I. Long-term patterns in an era of climate change
Bibliographic record
Abstract
This paper reports a study of ground-dwelling, small mammals in coastal eucalypt forest in south-eastern Australia from 1970 through 2005. During this time, the study area burnt in an intense fire in December 1972 and was partially burnt in November 1980. Both fires were associated with prolonged drought. The mammals studied comprised two dasyurid marsupials, Antechinus agilis and A. swainsonii, two native murid rodents, Rattus fuscipes and R. lutreolus, and the introduced house mouse Mus musculus. After intensive sampling throughout the year from 1970 through 1972 to establish basic ecological and population parameters of the small mammal community, populations were sampled annually during late autumn and early winter before the onset of breeding. There were marked differences in the annual (autumn/winter) abundances of all species; numbers of A. agilis ranged from 4 to 142 individuals; A. swainsonii 0 to 43; R. fuscipes 4 to 54; R. lutreolus 0 to 11; M. musculus 0 to 23. Following the 1972 fire, numbers fell to the lowest level recorded during the study and each population subsequently disappeared from the plot between the 1973 and 1974 winter censuses. The less intense 1980 fire did not lead to extirpation, but numbers of A. agilis, A. swainsonii and R. fuscipes declined as drought conditions persisted through 1983. R. lutreolus occurred consistently only following the fires, when a grassy ground vegetation favoured by this species developed. Similarly, M. musculus colonised within two years of the fires and persisted on the plot for 3–4 years before disappearing. Following the fires, populations of the omnivorous R. fuscipes recovered first followed by the scansorial, insectivorous A. agilis and last by the fossorial, insectivorous A. swainsonii. Two primary conclusions emerged from this study. First, the intense fire of 1972 did not kill all the animals immediately, but led to the disappearance of each species from the plot over 18 months. Thus, intense fire had a delayed but catastrophic impact on small ground-dwelling mammals. The fluctuations in population levels, covering more than an order of magnitude, demonstrate that factors other than fire, such as rainfall and drought, drive the population dynamics of these small mammals. As stability and recovery are not features of local populations, long-term studies of benchmark populations are necessary to manage forest biodiversity.
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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) | 0.000 | 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 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".