Aligning conservation status, vulnerability factors, and ecological and evolutionary uniqueness to produce integrated assessments of the world’s birds
Bibliographic record
Abstract
ABSTRACT A growing awareness, now enshrined in the Kunming-Montreal Global Biodiversity Framework, of the need to monitor biodiversity effectively at scale has led to a proliferation of novel solutions for doing so. Although global biodiversity encompasses all life, from tiny nitrogen-fixing bacteria to emergent rainforest trees, birds have several characteristics that make them a frequent focus of such monitoring efforts. In particular, birds frequently give diagnostic, species-specific vocalizations that simplify monitoring, they perform a number of critical ecosystem services, they are widely distributed in most ecosystems with strong representation on all continents, and the basic ecology, conservation status, populations, and distributions of many species is well known; birds thus provide a window into the underlying health and habitats of the systems under study. How best to summarize biodiversity monitoring results is a research question that has led to the development of approaches that incorporate species’ IUCN Red List threat assessments into site-level biodiversity scores. Notably, birds’ vocal behavior means that they can be effectively surveyed at scale with passive acoustic monitoring, and the potential to link such monitoring with automated identification and therefore quickly generate site-level biodiversity scores is an appealing approach to implement rigorous evaluations of global biodiversity. Yet, while many of the world’s birds are suffering worrisome population declines, the vast majority of species (78%) are still ranked “Least Concern” by the Red List. In an effort to develop a species scoring system that would be more conducive to such site-level valuations, we integrated key databases of species’ population status assessments, exposure to known vulnerability factors, and their functional and phylogenetic uniqueness to provide quantitative summaries of their conservation significance. We augmented these databases with two novel data sets available for most of the world’s birds: quantitative measurements of migration distances, and species-level phylogenetic and functional uniqueness values comparing each species to those it co-occurs with throughout its range. While the resulting BirdsPlus species scores also inherently reflect our own scientific expertise and judgement, our approach is transparent, dynamic, easily updated, and readily modified by users with different goals or values.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".