Disappearance of an ecosystem engineer, the white-lipped peccary (Tayassu pecari), leads to density compensation and ecological release
Bibliographic record
Abstract
Given the rate of biodiversity loss, there is an urgent need to understand community-level responses to extirpation events, with two prevailing hypotheses. On one hand, the loss of an apex predator leads to an increase in primary prey species, triggering a trophic cascade of other changes within the community, while density compensation and ecological release can occur because of reduced competition for resources and absence of direct aggression. White-lipped peccary (Tayassu pecari-WLP), a species that typically co-occurs with collared peccary (Pecari tajacu), undergo major population crashes-often taking 20 to 30-years for populations to recover. Using a temporally replicated camera trapping dataset, in both a pre- and post- WLP crash, we explore how WLP disappearance alters the structure of a Neotropical vertebrate community with findings indicative of density compensation. White-lipped peccary were the most frequently detected terrestrial mammal in the 2006-2007 pre-population crash period but were undetected during the 2019 post-crash survey. Panthera onca (jaguar) camera trap encounter rates declined by 63% following the WLP crash, while collared peccary, puma (Puma concolor), red-brocket deer (Mazama americana) and short-eared dog (Atelocynus microtis) all displayed greater encounter rates (490%, 150%, 280%, and 500% respectively), and increased in rank-abundance. Absence of WLP was correlated with ecological release changes in habitat-use for six species, with the greatest increase in use in the preferred floodplain habitat of the WLP. Surprisingly, community-weighted mean trait distributions (body size, feeding guild and nocturnality) did not change, suggesting functional redundancy in diverse tropical mammal assemblages.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".