Fine‐scale movement behaviors of calopterygid damselflies are influenced by landscape structure: an experimental manipulation
Bibliographic record
Abstract
We explore the effect of differences in landscape structure, arising from habitat loss, on the fine‐scale movement behaviors of two congeneric damselflies – Calopteryx aequabilis and C. maculata . Both species require streams for breeding and naiad development and both often use forest for foraging. We compare movement behaviors across three types of landscape: forested landscapes, where stream and forest habitat are adjacent; partially forested landscapes, where streams and forest habitat are disjunct, and non‐forested landscapes, where little to no forest habitat is available. We employ a reciprocal transplant experiment to determine the extent to which movement along and away from streams is influenced by landscape structure and historical behavior or morphological adaptations. For both species, we show that both the propensity to move away from streams and rates of net displacement differ among landscape types. Both species move away from streams on landscapes with high or moderate levels of forest cover but neither moves away from streams on landscapes with little or no forest. Furthermore, C. maculata native to predominantly forested landscapes are more likely to move away from streams, regardless of the landscape structure they encounter, than are individuals native to moderately forested or non‐forested landscapes. There was no effect of natal landscape on C. aequabilis . Comparisons with microlandscape studies suggest that there may be some general similarities among the different systems but these are clouded by uncertainty regarding the similarity of the underlying processes responsible for observed behavioral responses to landscape structure. Despite this uncertainty, animal movement behaviors are contingent upon the structure of the broader landscape, regardless of the absolute scale of the landscape.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".