Can metabolic traits explain animal community assembly and functioning?
Bibliographic record
Abstract
All animals on Earth compete for free energy, which is acquired, assimilated, and ultimately allocated to growth and reproduction. Competition is strongest within communities of sympatric, ecologically similar animals of roughly equal size (i.e. horizontal communities), which are often the focus of traditional community ecology. The replacement of taxonomic identities with functional traits has improved our ability to decipher the ecological dynamics that govern the assembly and functioning of animal communities. Yet, the use of low-resolution and taxonomically idiosyncratic traits in animals may have hampered progress to date. An animal's metabolic rate (MR) determines the costs of basic organismal processes and activities, thus linking major aspects of the multifaceted constructs of ecological niches (where, when, and how energy is obtained) and ecological fitness (how much energy is accumulated and passed on to future generations). We review evidence from organismal physiology to large-scale analyses across the tree of life to propose that MR gives rise to a group of meaningful functional traits - resting metabolic rate (RMR), maximum metabolic rate (MMR), and aerobic scope (AS) - that may permit an improved quantification of the energetic basis of species coexistence and, ultimately, the assembly and functioning of animal communities. Specifically, metabolic traits integrate across a variety of typical trait proxies for energy acquisition and allocation in animals (e.g. body size, diet, mobility, life history, habitat use), to yield a smaller suite of continuous quantities that: (1) can be precisely measured for individuals in a standardized fashion; and (2) apply to all animals regardless of their body plan, habitat, or taxonomic affiliation. While integrating metabolic traits into animal community ecology is neither a panacea to disentangling the nuanced effects of biological differences on animal community structure and functioning, nor without challenges, a small number of studies across different taxa suggest that MR may serve as a useful proxy for the energetic basis of competition in animals. Thus, the application of MR traits for animal communities can lead to a more general understanding of community assembly and functioning, enhance our ability to trace eco-evolutionary dynamics from genotypes to phenotypes (and vice versa), and help predict the responses of animal communities to environmental change. While trait-based ecology has improved our knowledge of animal communities to date, a more explicit energetic lens via the integration of metabolic traits may further strengthen the existing framework.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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 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".