Wildlife species benefitting from a greener Arctic are most sensitive to shrub cover at leading range edges
Bibliographic record
Abstract
Widespread expansion of shrubs is occurring across the Arctic. Shrub expansion will substantially alter arctic wildlife habitats. Identifying which wildlife species are most affected by shrubification is central to predicting future arctic community composition. Through meta-analysis, we synthesized the published evidence for effects of canopy-forming shrubs on birds and mammals in the Arctic and Subarctic. We examined variation in species behaviour, distribution and population dynamics in birds and mammals in response to shrub cover (including shrub cover indicators such as shrub occurrence, extent, density and height). We also assessed the degree of heterogeneity in wildlife responses to shrub cover and synthesized the remaining literature that did not fit the criteria for our quantitative meta-analyses. Species from higher green vegetation biomass habitats (high Normalized Difference Vegetation Index, NDVI, across their distribution) were more likely to respond positively to shrub cover, demonstrating the potential for species to expand from boreal to arctic habitats under shrubification. Wildlife populations located in the lowest vegetation biomass (low NDVI) areas of their species' range had the greatest proportion of positive responses to shrub cover, highlighting how increases in performance at leading edges of invaders distributions may be particularly rapid. This demonstrates the need to study species at these leading edges to accurately predict expansion potential. Arctic specialists were poorly represented across studies (limited to 5 bird and 0 mammal species), this knowledge gap potentially explains the few reported negative effects of shrub cover (3 of 29 species). Species responses to shrub cover showed substantial heterogeneity and varied among sites and years in all studies with sufficient replication to detect such variation. Our study highlights the importance of responses at species range edges in determining outcomes of shrubification for arctic birds and mammals and the need for greater examination of potential wildlife losers under shrubification.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.010 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".