Opposing responses to drought shape spatial population dynamics of declining grassland birds
Bibliographic record
Abstract
Abstract Aim The joint threats of climate and land‐use change require an understanding of how environmental variation influences species abundance and distribution. However, most species distribution models use static data and methods without considering how species respond over multiple temporal and spatial scales. Using a novel analytical approach, we show how multiscalar environmental variation drives spatial population dynamics of mobile species. Location Great Plains, North America. Methods We developed a spatial hierarchical model of abundance using long‐term citizen science data for two severely declining species (Lark Bunting, Calamospiza melanocorys, Chestnut‐collared Longspur, Calcarius ornatus). Specifically, we (a) compared regional variation in range‐wide abundance and population trends, (b) evaluated the influence of short‐term and long‐term drought on range dynamics and (c) tested whether regional population dynamics are spatially autocorrelated by environmental conditions occurring in geographically separated areas. Results Both species exhibited long‐term range‐wide declines >70% with contraction towards the range core. Lark Buntings showed opposing responses to environmental variation; regional abundance increased with wetter conditions during arrival on the breeding grounds but also with longer‐term (4‐year) drought conditions. Chestnut‐collared Longspurs showed no response to drought at either temporal scale. We found strong evidence that Lark Bunting abundance in the southern portion of the range increases with favourable environmental conditions leading to subsequent declines in abundance in northern regions. Main conclusions Our results highlight how (a) species can show opposing responses to the same environmental variable at different temporal and spatial scales, (b) sympatric species vary in their propensity to track environmental conditions and (c) for latitudinal migrants, environmental conditions along the migration pathway can influence settlement patterns with conditions in southern regions impacting abundance in the north. Our analysis indicates that an understanding of how global change impacts mobile species distributions will require range‐wide assessments incorporating response to environmental conditions across temporal and spatial scales.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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; 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".