Hunting of sika deer over six decades does not restore forest regeneration
Bibliographic record
Abstract
Abstract High densities of native and introduced deer hamper the regeneration of temperate forests worldwide. Sport hunting is often the sole means of deer control, but whether it can restore forest regeneration remains uncertain. We assessed the potential to restore forest regeneration using unrestricted sport hunting alongside commercial harvesting and government‐funded culling of introduced sika deer ( Cervus nippon ) across a 594 km 2 landscape in North Island, New Zealand. We used six decades of repeated measurements of forest regeneration and deer presence, alongside monitoring of tagged stems in a 20‐year paired exclosure experiment, to determine whether deer control restored regeneration. In our exclosure experiment, mountain beech ( Fuscospora cliffortioides ) seedling and sapling density, growth and survival were variable but consistently higher when deer were excluded by fencing. Sapling counts in unfenced plots were ≈3–10 times lower after 60 years of deer control compared with unfenced plots, before sika colonisation and other mountain beech forests without sika deer. This result suggests canopy replacement remains at risk despite government‐funded culling and encouragement of sport hunting. Individual‐based demographic models show that mountain beech is unlikely to regenerate following canopy gap formation in our study landscape unless deer impacts are reduced from current levels. These demographic models predicted present‐day forest regeneration far better than two widely used proxies of deer impact: plot‐based counts of saplings and estimates of deer densities from faecal pellet counts. Synthesis and applications . Here, we show that intensive culling beyond that achievable by sport hunting is needed to reduce deer densities enough to assure canopy regeneration. These interventions will be necessary in the many places worldwide where sport hunting is being relied upon to protect forests but appears to be failing. Given the controversy associated with deer culling, and the need for clear evidence to justify its implementation, we suggest managers could strengthen the evidence base for their interventions by collecting data to build demographic models.
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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 | 0.001 | 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) | 0.001 | 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 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".