Multiple links between species diversity and temporal stability in bird communities across North America
Bibliographic record
Abstract
Background: In experimental systems, the temporal stability of entire ecological communities usually increases with the number of species (called ‘species richness’). In contrast, ecology has not discovered the pattern of stability with the complement of richness, which is evenness of species abundances. The job has been more difficult because many measures of species diversity combine richness with evenness. Questions: Does the correlation of richness with stability occur in natural systems? What is the relationship of evenness to stability? Does diversity increase or decrease the stability of individual species populations? What mechanisms explain the relationships, if any, between diversity and stability? Data: The 1966–2009 results of 1676 North American Breeding Bird (BBS) survey routes across the USA and Canada. Altogether, 617 bird species were registered with an average of 105 species observed per route. Climate variables were taken from the US National Oceanic and Atmospheric Administration, and Climate Services Canada databases. Analytical methods: Disentangle richness from evenness and study their separate effects on stability. Perform statistical analysis of biological variables developed from the BBS data. Control for biological and climatic influences. Results: Both the number of bird species and the evenness of their distributions positively affect the stability of entire bird communities. But richness and evenness do so through a contrasting set of mechanisms. Also, richness and evenness both positively affect the stability of individual populations. The link between evenness and mean population stability can be partly, but not completely, explained in terms of a previously established relationship between the mean and variance of abundance known as Taylor’s Law.
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 | 0.001 | 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 | 0.002 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".