Urban forest fragments as unexpected sanctuaries for the rare endemic ghost butterfly from the Atlantic forest
Bibliographic record
Abstract
Abstract Anthropogenic land expansion, particularly urbanization, is pervasive, dramatically modifies the environment and is a major threat to wildlife with its associated environmental stressors. Urban remnant vegetation can help mitigate these impacts and could be vital for species unable to survive in harsh urban environments. Although resembling nonurban habitats, urban vegetation remnants are subject to additional environmental stresses. Here, we evaluate the occurrence and density of the endemic ghost butterfly (Morpho epistrophus nikolajewna) that was once common, in the highly fragmented Atlantic forest of NE Brazil. We tested whether this butterfly would be found at lower densities in urban forest fragments of contrasting sizes as opposed to rural ones, given the number of environmental stressors found in urban areas. We surveyed 14 forest fragments (range 2.8 to over 3,000 ha) of semideciduous Atlantic forest in rural and urban locations using transect based distance sampling. The ghost butterflies showed strong seasonality; flying only from April to June. They were only identified in an urban fragment (515 ha), with an estimate of 720 individuals and a density 1.4 ind/ha. All forest fragments had experienced some level of logging in the past, which might have had an effect in the butterfly population. Nevertheless, rural forest fragments were subject to increased particulate matter concentrations, associated to biomass burning that we suggest might have had a more influential role driving the collapse of rural populations. Our findings show the importance of urban forest remnants to sustain population of this endangered species.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".