Stochastic and deterministic drivers of spatial and temporal turnover in breeding bird communities
Bibliographic record
Abstract
Abstract Aim A long‐standing challenge in ecology is to identify the suite of factors that lead to turnover in species composition in both space and time. These factors might be stochastic (e.g. sampling and priority effects) or deterministic (e.g. competition and environmental filtering). While numerous studies have examined the relationship between turnover and individual drivers of interest (e.g. primary productivity, habitat heterogeneity, or regional – ‘gamma’ – diversity), few studies have disentangled the simultaneous influences of multiple stochastic and deterministic processes on both temporal and spatial turnover. If turnover is governed primarily by stochastic sampling processes, removing the sampling effects of gamma diversity should result in non‐significant relationships between turnover and environmental variables. Conversely, if deterministic processes govern turnover patterns, removing sampling effects will have little influence on turnover gradients. Here, we test these predictions. Location The United States. Methods Continental‐scale, multidecadal data were used to quantify spatial and temporal turnover in avian community composition within 295 survey routes. A series of regression and structural equation models were coupled with a null model to construct statistical models describing turnover patterns. Results Examining explanatory variables alone or in combination showed that spatial and temporal turnover increased together, decreased with primary productivity and increased with habitat heterogeneity. The relationships between turnover and all variables became weaker when sampling effects were removed, but relationships with primary productivity and habitat heterogeneity remained relatively strong. In addition, spatial turnover increased strongly with spatial gamma diversity after sampling effects were removed. Main conclusions Our results show that spatial and temporal turnover are related to each other through a stochastic sampling process, but that each type of turnover is further influenced by deterministic processes. The relative influence of deterministic processes appears, however, to decrease with primary productivity and increase with habitat heterogeneity across an east–west longitudinal gradient in North America.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".