Evaluating wildlife management by using principles of applied ecology: case studies and implications
Bibliographic record
Abstract
Context The broad concepts and generalisations that guide conduct of applied ecology, including wildlife management, have been reviewed and synthesised recently into 22 prescriptive and three empirical principles. Aims The aim of this study was to use these principles to evaluate three on-ground wildlife management programs and assess the utility of the principles themselves. Key results Case studies of long-term management of national park biodiversity impacted by feral pigs (Sus scrofa), and of conservation and harvest of red kangaroos (Macropus rufus) and mallards (Anas platyrhnchos), were selected to provide a representative range of management objectives, spatial scales and land tenures, and to include both native and introduced species. Management documents and a considerable scientific literature were available for all three programs. The results highlight similarities and differences among management activities and demonstrate the 25 principles to differing degrees. Most of the prescriptive principles were demonstrated in both the management and the scientific literature in all three programs, but almost no use was made of the three empirical principles. We propose that use of the prescriptive principles constitutes evidence that these programs meet both societal and scientific expectations. However, the limited use of the empirical principles shows gaps in the three programs. Conclusions The results suggest that evaluating other wildlife management programs against the principles of applied ecology is worthwhile and could highlight aspects of those programs that might otherwise be overlooked. Little use was made of the empirical principles, but the the Effort–outcomes principle in particular provides a framework for evaluating management programs. Implications The effort–outcomes relationship should be a focus of future applied research, and both prescriptive and empirical principles should be integrated into wildlife management programs.
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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 | 0.074 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| 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".