Remotely sensed environmental data as ecological proxies for ground‐dwelling ant diversity along a subtropical forest succession gradient
Bibliographic record
Abstract
Abstract Early naturalists such as Humboldt observed that changes in topography and anthropogenic disturbances influenced vegetation structure and the composition of animal communities. This holistic view of community assembly continues to shape conservation and restoration strategies in an era of landscape degradation and biodiversity loss. Today, remote sensing affords ecologists the tools for obtaining rapid and precise measures of topography, disturbance history and vegetation structure. Nonetheless, the capacity of such measures to predict the structure of diverse and functionally important insect communities has not been fully explored. We sampled ground‐dwelling ant assemblages with pitfall traps along a successional gradient (15 grasslands, 21 shrublands and 44 forests) in subtropical Asia, and measured the taxonomic (TD) and functional diversity (FD). We used airborne Light Detection and Ranging (LiDAR) and aerial photography—to measure topography, anthropogenic‐fire history and vegetation structure at each site. Using structural equation models, we tested the hypothesis that vegetation structure mediated the effects of topography and anthropogenic‐fire history on ant assemblage TD and FD, with stronger effects on the latter. We found that low elevation and anthropogenic‐fire history promoted ant TD, and by mediating vegetation structure, these factors further controlled ant FD. Specifically, assemblages of ant species occupying more similar niches—as indicated by their lower FD—were found in secondary forests that had more structurally homogeneous vegetation. These sites also had low insolation and high water moisture content, and were not recently burned as revealed by LiDAR‐derived metrics and aerial images. Furthermore, remotely sensed vegetation structures were closely associated with individual ant traits, such as body size and eye length, which reflect species' preferences for habitat structure. Synthesis . Our study uncovers the interactive effects of topography, disturbance history and vegetation structure in determining the TD and FD of ant assemblages in subtropical landscapes. Moreover, it demonstrates that remote sensed data can be leveraged to efficiently elucidate the complex effects of environmental change and disturbances on vegetation structure and consequently insect biodiversity, representing ecological proxies to refine ground investigation plans and support appropriate conservation and restoration measures for degraded landscapes.
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 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 | 0.001 |
| 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".