Data from: Multi-decadal changes in phytoplankton biomass in northern temperate lakes as seen through the prism of landscape properties
Bibliographic record
Abstract
Ecologists collectively predict that climate change will enhance phytoplankton biomass in northern lakes. Yet there are unique variations in the structures and regulating functions of lakes to make this prediction challengeable and, perhaps, inaccurate. We used archived Landsat TM/ETM+ satellite products to estimate epilimnetic chlorophyll-a concentration (Chl-a) as a proxy for phytoplankton biomass in 281 northern temperate lakes over 28 years. We explored the influence of climate (air temperature, precipitation) and landscape proxies for nutrient sources (proportion of wetlands in a contributing catchment, size of the littoral zone, potential for wind-driven sediment resuspension as estimated by the dynamic ratio) or nutrient sinks (lake volume) in a random forest model to explain heterogeneity in peak Chl-a. Lakes with higher Chl-a (median Chl-a = 2.4 μg L-1, n = 40) had smaller volumes (< 44 × 104 m3) and were more sensitive to increases in temperature. In contrast, lakes with lower Chl-a (median Chl-a = 0.6 μg L-1, n = 241) had larger volumes (≥ 44 × 104 m3), contributing catchments with smaller proportions of wetlands (< 4.5% of catchment area, n = 70), smaller littoral zones (< 16.4 ha, n = 137), minimal wind-driven sediment resuspension (as defined by the dynamic ratio; < 0.45, n = 232), and were more sensitive to increases in precipitation. Lakes with larger volumes were generally less responsive to climate factors; however, large volume lakes with a significant proportion of wetlands and larger littoral zones behaved similarly to lakes with smaller volumes. Our finding that lakes with different landscape properties respond differently to climate factors may help predict the susceptibility of lakes to eutrophication under changing climate conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| 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".