Patterns of nutrients and algal biomass in an intermittent Mediterranean river under intense human activity
Bibliographic record
Abstract
• Random forest and SHAP reveal the different spatial influence of drivers along the river. • Agriculture and point-source effluents are key predictors of N and P concentrations. • Precipitation, temperature and TP dictate suspended chlorophyll-a dynamics. • Human activities, not natural hydrology, dominate nutrient patterns in the intermittent stream. • Local, site-specific strategies are needed for intermittent river health. Non-perennial streams are often affected by human activities such as sewage water disposal and agriculture. The limited capacity of these systems to dilute pollutants, along with the extent of water flow interruption, leads to a high variability of nutrient concentrations and algal biomass, with relevant implications for water quality. The temporal and spatial patterns of nutrients—Total Nitrogen (TN), Nitrates ( N O 3 - ), Total Phosphorus (TP), and Phosphates ( P O 4 3 - )—and chlorophyll (as a surrogate of algal biomass) were characterized in a Mediterranean river system to determine their relationship to water flow intermittency and human activities. To achieve this, monthly measurements of water quality, physicochemical characteristics and algal biomass were collected at 23 different locations over 14 months, coinciding with the onset of drought conditions in the region. Random forest regression and the model-agnostic SHapley Additive exPlanations (SHAP) enabled us to identify the most influential hydrological and land use features for nutrient concentrations and map the spatial variation of their effects across the basin. The highest variability in nutrient concentrations occurred in the headwaters, while high values consistently occurred in the middle and lower segments. While TN and N O 3 - sources were diffuse, mainly agriculture, TP and P O 4 3 - levels were determined by the number of effluent point sources. Overall, water flow intermittency was not a strong predictor of nutrient patterns; however, its influence was more pronounced at specific sites. Applying the same modeling approach, we identified that suspended chlorophyll levels (but not benthic chlorophyll) correlated with nutrient concentrations, showing a notable association to TP and seasonal variations in precipitation and temperature. Our analysis underscores the complex interactions between hydrology, human activities, and nutrient dynamics in river basins subjected to water flow intermittency and highlights the need for management strategies focused on local polluted points.
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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) | 0.001 | 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 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".