Arctic biodiversity of stream macroinvertebrates declines in response to latitudinal change in the abiotic template
Bibliographic record
Abstract
We aimed to determine which processes drive patterns of α and β diversity in Arctic river benthic macrofauna across a broad latitudinal gradient spanning the low to high Arctic of eastern Canada (58 to 81oN). Further, we examined whether latitudinal differences in taxonomic composition resulted from species replacement with organisms better adapted to northerly conditions or from the loss of taxa unable to tolerate the harsh environments of higher latitudes. We used the bioclimatic envelope concept to provide a first approximation forecast of how climate warming may modify α and β diversity of Arctic rivers and to identify potential changes in environmental variables that will drive future assemblage structure. Benthic macroinvertebrates, environmental supporting variables, and geospatial catchment data were collected to assess drivers of ecological pattern. We compared α diversity (i.e., taxonomic richness) across latitudes and partitioned β diversity into components of nestedness and species turnover to assess their relative contributions to compositional differences. We found sharp declines in taxonomic richness along a latitudinal gradient. This α diversity pattern was not associated with a change in numerical abundance. β diversity was highest when the most distant latitudes were compared, and pairwise latitudinal comparisons indicated that nestedness (loss of species) was the dominant contributor to compositional differences. Biotic–abiotic associations reflected both large-scale climatic drivers, including air temperature and prevalence of vegetated tundra, and small-scale secondary abiotic drivers of assemblage composition (e.g., substrate composition, water chemistry). The importance of nestedness to β diversity across latitudes supports the physiological tolerance hypothesis that a change in environmental tolerance is a key driver of species richness declines with increasing latitude. Distinct taxonomic assemblages among low and high Arctic latitude sites were associated with large-scale, climate-related drivers (e.g., temperature trends, terrestrial vegetation), reflecting the primary structuring of assemblages by bioclimatic envelopes. The abiotic environment was the strongest driver of assemblage structure at high latitudes because of the extreme conditions. With continued warming, biodiversity differences along latitudinal gradients are expected to become less pronounced as temperatures and vegetation become more similar from south to north, with local-scale variables becoming dominant biotic drivers.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".