Inconsistent recovery of nitrogen cycling after feral ungulate removal across three tropical island ecosystems
Bibliographic record
Abstract
Abstract Non-native ungulates (sheep, goats, and pigs) have significant negative impacts on ecosystem biodiversity, structure, and biogeochemical function throughout the Pacific Islands. Elevated nitrogen (N) availability associated with ungulate disturbance has been shown to promote the success of resource-exploitive invasive plants. While ungulate removal is a common restoration intervention, evaluations of its efficacy typically focus on vegetation responses, rather than underlying nutrient cycling. We used multiple chronosequences of ungulate exclusion (10–24 years duration) in three Hawaiian ecosystems (montane wet forest, dry forest, and dry shrubland) to determine N cycle recovery by characterizing gross mineralization and nitrification, soil inorganic N concentrations and leaching, N 2 O emissions, and plant tissue δ 15 N. Ungulate removal led to a 1–2 ‰ decline in foliar δ 15 N in most species, consistent with a long-term decrease in N fractionation via ecosystem N losses, or a shift in the relative turnover of N forms. This interpretation was supported by significant (dry forest) or trending (wet forest) increases in mineralization and decreases in nitrification, but conflicts with lack of observed change in inorganic N pool sizes or gaseous losses, and increased leaching in the dry forest. While results could indicate that ungulate invasions do not strongly impact N cycling in the first place (no uninvaded control sites exist in Hawai’i to test this hypothesis), this would be inconsistent with observations from other sites globally. Instead, impacts may be spatially patchy across the landscape, or ungulate invasions (possibly in combination with other disturbances) may have permanently shifted biogeochemical function or decoupled elemental cycles. We conclude that eliminating ungulate disturbance alone may not achieve restoration goals related to N cycling within the timeframe examined here.
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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 | 0.000 | 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) | 0.001 | 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".