Spatial stoichiometry: cross‐ecosystem material flows and their impact on recipient ecosystems and organisms
Bibliographic record
Abstract
Cross‐ecosystem material flows, in the form of inorganic nutrients, detritus and organisms, spatially connect ecosystems and impact food web dynamics. To date research on material flows has focused on the impact of the quantity of these flows and largely ignored their elemental composition, or quality. However, the ratios of elements like carbon, nitrogen and phosphorus can influence the impact material flows have on food web interactions through stoichiometric mismatches between resources and consumers. The type and movement of materials likely vary in their ability to change stoichiometric constraints within the recipient ecosystem and materials may undergo changes in their own stoichiometry during transport. In this literature review we evaluate the importance of cross‐ecosystem material flows within the framework of ecological stoichiometry. We explore how movement in space and time impacts the stoichiometry of material flow, as these transformations are essential to consider when assessing the ability of these flows to impact food web productivity and ecosystem functioning. Our review suggests that stoichiometry of cross‐ecosystem material flows are highly dynamic and undergo changes during transport across the landscape or from human influence. These material flows can impact recipient organisms if they change stoichiometry of the abiotic medium, or provide resources that have a different stoichiometry to in situ resources. They might also alter consumer excretion rates, in turn altering the availability of nutrients in the recipient ecosystem. These alterations in stoichiometric constraints of recipient organisms can have cascading trophic effects and shape food web dynamics. We highlight significant gaps in the literature and suggest new avenues for research that explore how cross‐ecosystem material flows impact recipient ecosystems when considering differences in stoichiometric quality, their movement through the landscape and across ecosystem boundaries, and the nutritional constraints of the recipient organisms.
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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 | 0.001 | 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) | 0.002 | 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; 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".