Temperature and trophic structure are driving microbial productivity along a biogeographical gradient
Bibliographic record
Abstract
Temperature is known to influence ecosystem processes through its direct effect on biological rates such as respiration and nutrient cycling. These changes can then indirectly affect ecologically processes by altering trophic dynamics, the persistence of a species in a given environment, and, consequently, its distribution. However, it is not known if this direct effect of temperature on biological rates is singularly the most important factor for the functioning of ecosystems, or if trophic structure and the adaptation of a species to the local environment also play an essential role. Understanding the relative importance of these factors is crucial for predicting the impact that climate change will have on species and ecosystems. To achieve a more complete understanding of the impact of changing temperatures, it is necessary to integrate perspectives from biogeography, such as the influences of species distribution and local adaptation, with ecosystem and community ecology. By using the microbial community inhabiting the water‐filled leaves of Sarracenia purpurea , we tested the importance of temperature, trophic structure, and local adaptation on ecosystem functioning. We accomplished this by collecting communities along a natural temperature gradient and maintaining these communities in a common garden, factorial experiment. To test for the importance of local adaptation and temperature, the origin of each community was crossed with the temperature from each site. Additionally, to test the importance of top‐down trophic regulation for ecosystem functioning, the presence of the mosquito larvae top predator was manipulated. We found that temperature has a greater effect on ecosystem functioning than origin, and that top‐down trophic regulation increased with temperature. Our results emphasize the synergistic effects of temperature and biotic interactions when predicting the consequences of global warming on ecosystem functioning.
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 | 0.001 |
| 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) | 0.000 | 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".