On the potential of documenting decadal-scale avifaunal change from before-and-after comparisons of museum and observational data across North America
Bibliographic record
Abstract
Studies of biodiversity dynamics have been cast on either long (systematics) or short (ecology) time scales, leaving a gap in coverage for moderate time scales of decades to centuries. Large-scale biodiversity information resources now available offer opportunities to fill this gap for many parts of the world via detailed, quantitative comparisons of assemblage composition, particularly for regions without rich time series datasets. We explore the possibility that such changes in avifaunas across the United States and Canada before and after the first three decades of marked global change (i.e., prior to 1980 versus after 2010) can be reconstructed and characterized from existing primary biodiversity data. As an illustration of the potential of this methodology for sites even in regions not as well sampled as the United States and Canada, we also explored changes at a single site in Mexico (Chichén-Itzá). We analyzed two large-scale datasets: one summarizing bird records in the United States and Canada before 1980, and one for the same region after 2010. We used probabilistic inventory completeness analyses to identify sites that have avifaunas that have likely been inventoried more or less completely. We prepared detailed comparisons between the two time periods to analyze species showing distributional changes over the time period analyzed. We identified 139 sites on a 0.05° grid that were demonstrably well-inventoried before 1980 in the United States and Canada, of which 108 were also well-inventoried after 2010. Comparing presence/absence patterns between the two time periods for 601 bird species, we found significant spatial autocorrelation in overall avifaunal turnover (species gained and lost), but not in numbers of species lost. We noted potential northward retractions of ranges of several species with high-latitude (boreal) distributions, while other species showed dominant patterns of population loss, either rangewide (e.g., Tympanuchus cupido) or regionally (e.g., Thryomanes bewickii). We developed linear models to explore a suite of potential drivers of species loss at relatively fine-grained resolutions (<6 km), finding significant effects of precipitation increase, particularly on the eastern border of the United States and Canada. Our exploration of biotic change in Chichén-Itzá included 265 species and showed intriguing losses from the local avifauna (e.g., Patagioenas speciosa), as well as vagrant and recent invasive species in the Yucatán Peninsula. The present work documents both the potential for and the problems involved in an approach integrating primary biodiversity data across time periods. This method potentially allows researchers to assess intermediate-time-scale biodiversity dynamics that can reveal patterns of change in biodiversity-rich regions that lack extensive time-series information.
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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 | 0.001 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.019 | 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".