An empirical test of the temperature dependence of carrying capacity
Bibliographic record
Abstract
Abstract Predicting population persistence and dynamics in the context of global change is a major challenge for ecology. A widely held prediction is that population abundance at carrying capacity decreases with warming, assuming no change in resource supply, due to increased individual resource demands associated with higher metabolic rates. However, this prediction, which is based on metabolic scaling theory (MST), has not been tested empirically. Here we experimentally tested whether effects of temperature on short-term metabolic performance (rates of photosynthesis and respiration) translate directly to effects of temperature on population rates in a phytoplankton species. We found that effects of temperature on organismal metabolic rates matched theoretical predictions, and that the temperature dependence of individual metabolic performance translated to population abundance. Population abundance at carrying capacity, K , decreased with temperature less than expected based on the temperature dependence of photosynthesis. Concurrent with declines in abundance, we observed a linear decline in cell size of approximately 2.3% °C −1 , which is consistent with broadly observed patterns in unicellular organisms, known as the temperature-size rule. When theoretical predictions include higher densities allowed by shifts toward smaller individual size, observed declines in K were quantitatively consistent with theoretical predictions. Our results indicate that outcomes of population dynamics across a range of temperatures reflect organismal responses to temperature via metabolic scaling, providing a general basis for forecasting population responses to global change.
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 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 | 0.002 | 0.020 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".