Determinants of trophic structure in ecological communities
Bibliographic record
Abstract
Ecological communities are complex, and this complexity can obscure their underlying patterns and natural laws.One way to understand communities is to summarize their most important characteristics using consistent measures.Community structure is a set of measures of composition, abundance, distribution, and interaction that describe an ecological community over space and time.Trophic structure is an important aspect of community structure, and relates to energy and nutrient flow, especially the distribution of organisms across trophic levels.Trophic level is the energetic distance of an organism from the base of production -its average position in the food chains to which it belongs.Due to energetic inefficiencies, we generally predict that organisms decrease in number and biomass with trophic level, forming trophic pyramids (known as "pyramids of numbers" and "pyramids of biomass", respectively).Other, non-pyramidal trophic structures are also common, and trophic structure is affected by variables at multiple ecological scales.The objective of this thesis is to investigate determinants of trophic and community structure, including latitude, ecosystem type, biome transition, community composition, and body size.While pyramids of numbers and pyramids of biomass are well-studied, few have investigated the trophic distribution of diversity.Using a meta-analysis approach, I found that, on average, large published food webs form pyramids of species richness, with a decrease in number of species with trophic level.The published food webs were more predator-poor, prey-rich, and hierarchical than three null models: random, niche, and cascade food web models.There was variation in trophic diversity structure amongst the food webs, and some food webs had uniform or inverse-pyramidal structure.Trophic diversity structure was correlated to centrality, latitude, ecosystem type, and study identity.Community structure varies spatially, as can be seen even by a casual observer at interfaces between biomes.One such biome shift is between boreal forest and tundra, also known as the tree line.I studied how macroinvertebrate and soil prokaryote communities changed latitudinally along the forest-tundra ecotone in the Yukon, and how the communities responded to other environmental variables.I tested several hypotheses regarding changes in Chapter 2: Pyramids of species richness: the determinants and distribution of species diversity across trophic levels 2.1
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".