Diversity of canopy spiders in north-temperate hardwood forests
Bibliographic record
Abstract
The objective of this thesis was to understand the spatial patterns and processes responsible for canopy and understorey spider (Arachnida: Araneae) diversity at multiple spatial scales in north-temperate hardwood forests. I sampled tree trunks (sticky traps) and foliage (beating) of sugar maple and American beech tree canopies and their understorey saplings in old growth forests near Montreal, Quebec. Results show the composition of canopy and understorey assemblages differed significantly, and so did sugar maple and American beech canopy assemblages. Each stratum was also dominated by different species. The rank-abundance distribution of species from each habitat wsa also verticaly stratified because it fit different distribution models. Different factors likely structure assemblages in both habitats, particularly since the canopy is a less stable environment. Spiders from canopy and understorey foliage were tested in a laboratory for their propensity to balloon. General linear models indicated that small sized web-building spiders of the RTA and Orbicularia clades have the highest propensity to balloon. Small bodied species initiated ballooning regardless of the habitat they were collected in or their developmental stage. My results support the mixed evolutionarily stable strategy theory and indicate the absence of risk-spreading in the dispersal strategy of canopy spiders. My last chapter focused on dispersal capacity and diversity patterns of spiders at multiple spatial scales. Analyses of the species diversity of limited and high dispersal capacity species subsets through nested-multivariate ANOVA, additive diversity partitioning, and species-abundance distribution curves all point towards species-sorting processes as the main driver of local community spider diversity at the tree and stand spatial scales. Mass-effects and patch-dynamic processes drive site and regional scale diversity patterns. This thesis demonstrates that spiders provide good models to test many biological hypotheses. The research chapters of this thesis test hypotheses on the vertical stratification of forest spider diversity, the evolution of local dispersal adaptations, and the importance of dispersal capacity on species diversity patterns through a metacommunity framework.
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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 | 0.001 |
| 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) | 0.002 | 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".