Use of the Abundance Spectrum and Relative‐Abundance Distributions to Analyze Assemblage Change in Massively Altered Landscapes
Bibliographic record
Abstract
Fragmentation of natural landscapes is a pervasive process in the world. Common models predict coherent change in assemblages, with less numerous species becoming locally extinct first, then species of intermediate abundance, and so forth. Relative-abundance distributions should change systematically in landscapes characterized by greater change. Such a predictable sequence of change is not evident in the avifaunas of landscapes of central Victoria, Australia, where relative-abundance patterns in more affected landscapes bear little resemblance to reference distributions. I provide two sets of analyses of relative-abundance distributions: (1) analyses that do not depend on the identity of individual species and (2) abundance spectra, which use ordered lists of species ranked by species' commonness in reference systems. While abundance spectra change dramatically in smaller remnants, relative-abundance distributions change little, suggesting that the "reorganization" of abundances occurs over ecological time frames. The dispersal-limited multinomial is a flexible distribution that may fit many data sets yet be unrelated to assumptions (species neutrality) and processes (fixed total numbers of individuals) of the unified neutral theory. A more complete understanding of human impacts at landscape scales must include capacities to predict those species that will be advantaged by change, as well as those that will be disadvantaged.
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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".