Arctic Migrations Shape Global Meta‐Communities: Contrasting Insights From Species Occurrence, Abundance and Biomass
Bibliographic record
Abstract
ABSTRACT Aim Seasonally migratory species generate large movements of organisms and biomass between distant breeding and non‐breeding grounds. However, our understanding of how migratory species shape global networks of interconnected communities (meta‐communities) remains limited. Migratory links between communities can be measured in different ways (e.g., species occurrence, abundance or biomass), each providing complementary information by modulating the relative importance of species in meta‐communities. We aim at investigating to what extent measuring migratory links using species occurrence, abundance or biomass can reveal alternative structures (i.e., topology) in a meta‐community linking an Arctic breeding ground to remote non‐breeding grounds. Location We use as a study case the High‐Arctic vertebrate community of Bylot Island (Nunavut, Canada), along with ecoregions of North and South America, Europe and Africa. Time Period Present. Major Taxa Studied Terrestrial Arctic birds (30 species) and mammals (5 species). Methods We first consider species occurrence at the non‐breeding grounds to define migratory links within the meta‐community. Secondly, we measure the number of individuals and the amount of biomass travelling along those links. Finally, we compare the meta‐community structure under each scenario using a migration network representation. Results Patterns of species occurrence, abundance and biomass reveal that temperate ecoregions of South and especially North America maintain strong ecological connections with the vertebrate community of Bylot Island. However, the structural role of species within the network can vary substantially depending on how migratory links are measured (i.e., contrasting topological anomalies). Using abundance or biomass to measure migratory links results in a finer partitioning of the network into modules compared to using species occurrence alone. Main Conclusions We highlight that using different metrics of migratory links reveals unique, yet complementary structural features of meta‐communities. These findings contribute to assessing the vulnerability of communities to perturbations occurring in distant but connected environments through migration.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".