Amazonian avian biogeography: Broadscale patterns, microevolutionary processes, and habitat-specific models revealed by multidisciplinary approaches
Bibliographic record
Abstract
Abstract Our understanding of Amazonian biogeography is quickly increasing, catalyzed by the growing use of genomic datasets, improved knowledge of species distributions, and the accumulation of new data on the geological and climatic history of the region. The high number of species in Amazonia and their intricate patterns of geographic distribution triggered the development of many diversification hypotheses that shaped historical biogeography as a discipline. Here, we present a historical overview of avian biogeographic studies in Amazonia, their recent advances, outstanding questions, and future directions. We focus on how new approaches have led to novel understandings of the distribution patterns and diversification processes that underpin avian diversity. We show how genomic tools are being used to establish new hypotheses about the drivers of diversification and highlight the importance of recent studies of birds in previously overlooked environments, such as floodplains and open vegetation enclaves. We emphasize the importance of gene flow, species traits, and habitat affinities in studying diversification processes to fully acknowledge the complexity of Amazonian ecosystems and their history. We then discuss the current gaps in Amazonian taxonomic and biogeographic knowledge, with a focus on the issues that we believe hinder our understanding of the field. Amazonia has been facing increasing levels of forest loss due to agricultural expansion, infrastructure development, mining, climate change, and illegal activities catalyzed by weak governance. To halt biodiversity loss, it is crucial to increase our knowledge of the natural history and biogeography of Amazonian species. We suggest increasing incentives for research and training at institutions based in the region, as well as the establishment of partnerships with governments, local communities, NGOs, and international institutions to bring diverse communities together to address crucial questions.
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 | 0.000 | 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 | 0.000 | 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".