On the importance of accounting for intraspecific genomic relatedness in multi-species studies
Bibliographic record
Abstract
Abstract Analyses in many fields of ecology are increasingly considering multiple species and multiple individuals per species. Premises of statistical tests are often violated with such datasets because of the non-independence of residuals due to phylogenetic relationships or intraspecific population structure. If comparative approaches that account for the phylogenetic relationships of species are well developed and their benefits demonstrated, the importance of considering the intraspecific genetic structure, especially in combination with the phylogenetic structure, has rarely been addressed. We investigated whether it is beneficial to account for intraspecific genomic relatedness in multi-species studies. For this, we used a Phylogenetic Mixed Model to analyze first a suite of simulated data and then results from one example ecological study—a budburst experiment where clippings of 10 tree and shrub species were subjected to different temperatures and photoperiods. We found that accounting for intraspecific genetic structure yields more accurate and precise fixed effects as well as increased statistical power, but more so when the relative importance of the intraspecific to the phylogenetic genetic structure is greater. Analysis of the budburst experiment further showed that accounting for intraspecific and phylogenetic structures yields improved estimates of warming and photoperiod effects and their interaction in explaining the time to budburst. Our results show that statistical gains can be made by incorporating information on the intraspecific genomic relatedness of individuals in multi-species studies. This is relevant for investigations that are interested in intraspecific variation and that plan to include such observations in statistical tests.
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 | 0.116 | 0.223 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".