Trophic and non-trophic seasonal interaction network for boreal forest tetrapods
Bibliographic record
Abstract
Aim: Understanding the organization of the wide variety of ecological interactions is crucial to advancing our understanding and management of real ecosystems. We aimed to compile a “complete” network of tetrapod trophic and non-trophic interactions for the entire North American boreal forest biome that could be analyzed to gain insights into community organization and function. In particular, we aimed to identify functionally important units (modules) and species within the boreal network, and to compare how these changed seasonally and with the inclusion of non-trophic interactions. Location: Boreal North America Time period: 1950 – present day Major taxa studied: Tetrapods Methods: We compiled published ecological interactions for boreal tetrapods into a food web (trophic interactions) and inclusive network (trophic and non-trophic interactions). We partitioned interactions by season, creating four networks representing the two network types per season. We examined how the modular structure, composition of modules, assortativity of traits within modules, and importance of different species compared across these networks. Results: We compiled a network of 5037 ecological interactions between 421 boreal tetrapod species. Most of these interactions (87%) occur in summer. The summer and winter boreal food webs and inclusive networks are modular (i.e., contain subsets of species interacting more with each other than with species outside of the subset). Several species attributes explain which species assort together into modules, including physical and behavioural traits, taxonomic class, and trophic niche. A small set of species come out as most functionally important (central, module hubs, or responsible for the greatest network change when non-trophic interactions are included) across all versions of the network, and other species are important within a certain season or interaction context. Main conclusions: Potential conservation management units (modules) exist in the boreal forest network, and considering species’ function at the community level highlights new priorities for species-level management.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".