Surface water connectivity drives richness and composition of Arctic lake fish assemblages
Bibliographic record
Abstract
Summary Surface water connectivity can influence the richness and composition of fish assemblages, particularly in harsh environments where colonisation factors and access to seasonal refugia are required for species persistence. Studies regarding influence of connectivity on Arctic fish distributions are limited and are rarely applied to whole assemblage patterns. To increase our understanding of how surface water connectivity and related hydrologic variables influence assemblage patterns, we investigated species richness and composition of Arctic lake fishes over a large region, 8500 km2, of the central Arctic Coastal Plain, Alaska. We collected fish presence/non‐detection data from 102 lakes and used a hierarchical multispecies occupancy framework to derive species richness and inform species composition patterns. Our mean estimate of regional richness was 12.3 (SD 0.5) species. Presence of a permanent channel connection was an overriding factor affecting species richness (mean 3.6, 95% CI 3.1–4.9), presumably driving lake colonisation potential. In lakes without a permanent channel connection, data suggest richness (mean 2.0, 95% CI 1.7–3.3) increased with the availability of in‐lake winter refugia and with the potential of ephemeral connections during spring floods. Fish species functional traits and environmental faunal filters contributed to patterns of richness and assemblage composition. Composition corresponded with richness in a coherent manner, where each successive level of richness contained several discrete assemblages that showed similar responses to the environment. Lakes with permanent channel connections contained both widespread and restricted species, while the species‐poor lakes that lacked a connection contained mainly widespread species. This work provides useful baseline information on the processes that drive the relations between patch connectivity and fish species richness and assemblage composition. The environmental processes that organise fish assemblages in Arctic lakes are likely to change in a warming climate.
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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 | 0.001 |
| 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 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".