Salmon-derived nutrients drive diatom beta-diversity patterns
Bibliographic record
Abstract
1. Pacific salmon are a textbook example of migratory animals that transfer nutrients between ecosystems, but little is known about how salmon-derived nutrients (SDN) affect the biodiversity of recipient freshwater ecosystems. We examined paleolimnological records from six Alaskan lakes to define how changes in SDN from sockeye salmon (Oncorhynchus nerka) influenced sedimentary diatom community structure and beta-diversity among lakes and through time. 2. Using an isotopic mixing model, we showed that SDN loading could account for >80% of the lake total nitrogen budgets and strongly regulated diatom community composition. Spatial dissimilarity in diatom communities was positively related to differences in SDN among lakes (r2 = 0.69, P < 0.01, n = 10). Likewise, temporal dissimilarity in diatom communities was positively related to differences in SDN in a sediment core with substantial variation in salmon spawner dynamics between 1700 and 1950 AD (r2 = 0.34, P < 0.01, n = 19). Finally, beta-diversity metrics quantifying temporal turnover within each lake’s sediment record were also positively related to the variance in SDN loading among lakes (r2 = 0.88, P < 0.05, n = 5). Mean SDN was only negatively correlated to temporal diatom beta-diversity. 3. Spatially subsidised systems often receive temporally variable resource inputs, and thus, it is not surprising that, unlike previous studies, we found that resource variability was the key driver of community composition and beta-diversity. In habitats that receive strongly fluctuating external nutrient loads, environment heterogeneity may overweigh stochastic community processes. In addition, freshwater diatoms are characterised by great dispersal capabilities and short life cycles and therefore may be a more sensitive indicator for evaluating the role of resource variability than previously used model organisms. These results suggest that productivity–diversity relationship vary with the nature of nutrient loading and the life history of the community studied. 4. Overall, our study highlights that the transport of nutrients by sockeye salmon across ecosystem boundaries is a significant driver of algal community and biodiversity in nursery lakes, mainly through changing the magnitude of nutrient variation. As such, freshwater species diversity in regions like the U.S. Pacific Northwest may become impoverished where there have been long-term declines in salmon populations and decreases in nutrient variability among lakes.
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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) | 0.009 | 0.001 |
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; both teacher heads agree on what is shown here.
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".