Ornithogenic alteration of a tundra ecosystem from decades of intense herbivory and dense nesting
Bibliographic record
Abstract
Abstract Migratory animals can serve as ecological links between geographically distant ecosystems. Moreover, when seasonally linked ecosystems differ in carrying capacity of migrant species, those with high capacity may support population growth with consequences to shared ecosystems with lower capacity through trophic cascades. Agricultural production has increased carrying capacity of lesser snow ( Anser caerulescens caerulescens ) and Ross's geese ( Anser rossii , collectively, “light geese”) in southern agricultural landscapes where these species winter and stage during migration to and from northern breeding areas, resulting in population increase. In subarctic and arctic ecosystems where carrying capacity for geese is lower, high abundance and densities of light geese have caused trophic cascades during summer breeding. This has raised concern for resilience of northern ecosystems to withstand cumulative and intense pressures of above‐ and belowground herbivory and nest construction. We investigated the empirical relationship between intensity of vegetation disturbance by multidecadal foraging and nest construction by up to ~1.3 million geese and shifts in (1) plant community composition and (2) taxon richness of freshwater plant communities near Karrak Lake, in Canada's central arctic. Intense use by nesting light geese caused shifts from lowland communities dominated by grasses and sedges (collectively, graminoids), Sphagnum spp., and willows ( Salix spp.) to those comprised of exposed peat, non‐ Sphagnum mosses, marsh ragwort ( Tephroseris palustris ), mare's tail ( Hippuris vulgaris ), and particularly birch ( Betula glandulosa ). Community change was less apparent in upland regions that were naturally less vegetated even in the absence of avian herbivores, but fruticose lichens, crowberry ( Empetrum nigrum ), and white heather ( Cassiope tetragona ) dominated undisturbed plant communities, whereas crustose lichens and bearberry ( Arctostaphylos spp.) comprised disturbed communities. We did not find evidence for dominance by a limited number of species with long‐term occupancy by light geese, as taxon richness was equivocal between disturbed and undisturbed plant communities. Cessation of foraging and nesting pressure increased taxon richness and reestablishment of locally eradicated plant species. Overall, herbivory and nesting effects were not uniform across this widespread nesting colony and, together with underlying influence from abiotic gradients, increased heterogeneity in the mosaic of vegetation communities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".