Life-history traits affect vulnerability of butterflies to habitat fragmentation in urban remnant forests
Bibliographic record
Abstract
Abstract: In natural ecosystems, species assemblages of isolated ecological communities frequently exhibit a nested pattern. The rapid urbanization that has occurred in Tokyo, central Japan, has resulted in the formation of extensive isolated forest remnants. We examined how geographic factors and the life-history traits of butterflies affected the occurrence of nested distribution patterns in butterflies from 20 forest remnants in the city. The species inhabiting each remnant were surveyed using transect counts, and the geographic attributes of the forest remnants, such as remnant shape, isolation, and distance to a region of contiguous forest were characterized.The species life-history traits that were considered included host plant type, host plant range, voltinism, and adaptability of the butterflies to the matrix (i.e., areas outside forest remnants). Butterfly species with host plants that were cultivated within the matrix were defined as highly adaptable species. The results showed that the butterfly assemblages in the surveyed area were significantly nested. In addition, the nested rankings (NR) of remnants, which are used as indicators of extinction vulnerability, were correlated with remnant area but not with remnant shape, isolation, or distance to the continuous forest. The best model based on AICc revealed that species with short flying periods and a narrow host plant range consisting of woody plants that were not cultivated in the matrix had low associated NR values. Our findings showed that selective local extinction may contribute to the nestedness of butterflies in forest remnants, and that host plant type, host plant range, voltinism, and adaptability to the matrix appear to affect butterfly vulnerability to habitat fragmentation. From a conservation perspective, understanding the factors that influence extinction vulnerability has important implications, because it allows us to predict why some butterfly species are more susceptible to extinction than others.
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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) | 0.000 | 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".