Integrating ecosystem and contaminant models to predict the effects of ecosystem fluxes on contaminant dynamics
Bibliographic record
Abstract
ABSTRACT Pollution is one of the major drivers of ecosystem change in the Anthropocene. Toxic chemicals are not constrained to their source of origin as they cross ecosystem boundaries via biotic (e.g., animal migration) and abiotic (e.g., water flow) vectors. Meta-ecology has led to important insights on how spatial flows or subsidies of matter across ecosystem boundaries can have broad impacts on local and regional ecosystem dynamics but has not yet addressed the dynamics of pollutants. Understanding how these meta-ecosystem processes on contaminant dynamics may reverberate up a food chain is important even if they might be difficult to predict. Here we derive a modelling framework to predict how spatial ecosystem fluxes can influence contaminant dynamics and how the severity of this impact is dependent on the type of ecosystem flux leading to the spatial coupling (e.g., herbivore movement vs abiotic chemical flows). We mix an analytical and numerical approach to analyze our integrative model which couples two distinct sub-components – an ecosystem model and a contaminant model. We observe an array of dynamics for how chemical concentrations change with increasing nutrient input and loss rate across trophic levels. When we tailor our range of chemical parameter values to specific organic chemicals our results demonstrate that increasing nutrient input rates can lead to trophic dilution in pollutants such as polychlorinated biphenyls across trophic levels. Yet, increasing nutrient loss rate causes an increase in concentrations of chemicals across all trophic levels. A sensitivity analysis demonstrates that nutrient recycling is an important ecosystem process impacting contaminant concentrations, generating predictions to be addressed by future empirical studies. Importantly, our model demonstrates the utility of our framework for identifying drivers of contaminant dynamics in connected ecosystems including the importance that a) ecosystem processes, and b) movement, especially movement of lower trophic levels, have on contaminant concentrations. For example, how increasing nutrient loss rate leads to increasing contaminant concentrations, or how movement of lower trophic levels contributes to elevated herbivore contaminant concentrations. This dynamic is particularly relevant given that the flow of matter between ecosystems also serves as a vector for the transport of contaminants.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".