Habitat heterogeneity, trophic links, and lichen assemblages: multiscale predictors of arthropod communities in Newfoundland forests
Bibliographic record
Abstract
Arthropods, a highly diverse and abundant groups of animals, are integral to ecosystem functioning worldwide. In forest environments, they act as pollinators, decomposers, nutrient cyclers, and more. However, these arthropod populations are susceptible to environmental changes, which are intensifying due to anthropogenic disturbances. Therefore, it is imperative to understand the dynamics of these communities in response to their surroundings. The aim of this thesis is to understand the variables that affect arthropod community structure in the forest, on trees and in soil. We hypothesized that habitat heterogeneity plays an important role in influencing arthropod diversity and abundance. This study was conducted in Newfoundland during the summer of 2022. We collected monthly arthropod samples from trees and soil in 45 replicate units distributed across three landscapes settings: Salmonier Nature Reserve, Pippy Park, and Outer Cove. Our findings underscore the significance of microhabitat variability, driven by differences in lichen communities, tree characteristics, and soil attributes, in shaping arthropod communities. Furthermore, our study found trophic correlations within and between habitat types, highlighting the importance of inter-group interactions. Finally, site variation underscores how landscape-level features influence arthropod abundance and diversity. Understanding the factors that influence arthropod assemblages can help develop proxy measurements for efficient monitoring of Newfoundland arthropod populations, while providing baseline measurements for future manipulative studies.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".