Trait‐dependent occupancy dynamics of birds in temperate forest landscapes: fine‐scale observations in a hierarchical multi‐species framework
Bibliographic record
Abstract
Abstract Silvicultural practices lead to changes in forest composition and structure and may impact species diversity from the overall regional species pool to stand‐level species occurrence. We explored to what extent fine‐scale occupancy patterns in differently managed forest stands are driven by environment and ecological traits in three regions in Germany using a multi‐species hierarchical model. We tested for the possible impact of environmental variables and ecological traits on occupancy dynamics in a joint modelling exercise while taking possible variation in coefficient estimates over years and plots into account. Bird species richness differed across regions and years, and trends in species richness across years were different in the three regions. On the species level, forest management affected occupancy of species in all regions, but only 3–5% of the total assemblage‐level variation in occurrence probability was explained by either forest type and successional stage and < 1% by forest edge. On the assemblage level, bird occurrence decreased with body mass in all regions. Species with smaller breeding ranges had lower occurrence probabilities in one region, while later spring arrival decreased occurrence probabilities in the two other regions. Spatial variation in the effect size of trait covariates such as species phylogeny and breeding strata showed that variation in patch occupancy due to fine‐scale differences in forest management is, to some extent, predictable from ecological traits. Our results show that environmental factors and ecological traits jointly predict variation in bird occupancy patterns and their response to forest management. Observations at the fine scale of forest stands, at which conservation efforts can be arranged along with forest management practices in heterogeneous environments, have been shown to provide meaningful insights despite the difficulties involved in monitoring mobile organisms such as birds at the plot level.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".