Four decades of research and monitoring the populations of kangaroos in New South Wales: one of the best long-term datasets in Australia
Bibliographic record
Abstract
ABSTRACT The aim of this paper is to highlight long-term (four decades) research and monitoring the populations of the four species of large kangaroos in New South Wales (NSW). Kangaroos are counted by aerial surveys using two types of aircraft: fixed-wing and helicopters. The NSW Commercial Kangaroo Harvest Management Plan 2017–21 states that harvest quotas are set at between 15 and 17% of the estimated kangaroo populations. As an example of the scale and intensity of kangaroo distribution and harvesting in NSW, the total number of kangaroos estimated to be present in the 14 commercial zones in 2017 was 17,457,257. The number present in each commercial zone varies widely across the state, with the Narrabri zone leading the list with an estimated 2,215,589 kangaroos in 2017. More than half of the kangaroos in NSW were eastern grey kangaroos (9,298,261 in 2017). The zone with the highest number of red kangaroos (1,567,598 in 2017) was in the far west of the State, in the Tibooburra commercial zone. The total number in the commercial take in 2017 was 454,626, representing 16% of the quota allowed to be taken, and 2.6% of the total number of kangaroos in the commercial zone. The four decades of records of the population sizes show that the numbers vary between about 5 million and 18 million for the western plains, where the counting has been consistent over the same area since the beginning of the surveys in 1975. The periods of decline correspond with periods of drought, with the Millennium drought showing a considerable dip to a low point in kangaroo numbers in 2005. Initial assessments from data dominated by drought found recent rainfall was the best predictor of rate of increase in kangaroo numbers, but analyses of a longer time series found rainfall with a longer time-lag was most influential. In the Tibooburra commercial zone, there has been a 4-fold change, with the highest number (1,567,598) recorded in 1998, the lowest number in 2006 (361,506), and equally high numbers (1,567,589) again in 2016. The short answers to regular questions of kangaroo resilience in relation to commercial harvesting, or about allowing culling where rural landholders are adversely affected by kangaroo numbers, are that the data show the kangaroo populations of NSW to be large and widespread and not declining because of either the commercial harvest or culling. While harvesting and culling remain as political matters, our view is that the debate needs to be based on long-term datasets that are readily accessible and reliable. In our view, these datasets not only fulfil that requirement, but are textbook material; they can be summarised into one graph that covers decades, or expanded to show the fluctuations in the numbers of each species in each zone for each year.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".