Lifetime productivity of tree cavities used by cavity‐nesting animals in temperate and subtropical forests
Bibliographic record
Abstract
Tree cavities are a critical multi-annual resource that can limit populations and structure communities of cavity-nesting vertebrates. We examined the regional and local factors influencing lifetime productivity (number and richness of occupants) of individual tree cavities across two divergent forest ecosystems: temperate mixed forest in Canada and subtropical Atlantic Forest, Argentina. We predicted that (1) species would accumulate more rapidly within cavities in the species-rich system (Argentina: 76 species) than the poorer system (Canada: 31 species), (2) cavity characteristics associated with nest-site selection in short-term studies would predict lifetime cavity productivity, and (3) species would accumulate more rapidly across highly used cavities than across cavities used only once, and in Argentina than in Canada. We monitored and measured nesting cavities used by birds and mammals over 22 breeding seasons (1995-2016) in Canada and 12 breeding seasons (2006-2017) in Argentina. Cavities were used an average of 3.1 times by 1.7 species in Canada and 2.2 times by 1.4 species in Argentina. Species richness within cavities increased with number of nesting events at similar rates in Canada and Argentina, in both cases much slower than expected if within-cavity species assemblages were random, suggesting that lifetime richness of individual cavities is more strongly influenced by local ecological factors (nest site fidelity, nest niche) than by the regional species pool. The major determinant of lifetime cavity productivity was the cavity's life span. We found only weak or inconsistent relationships with cavity characteristics selected by individuals in short-term nest-site selection studies. Turnover among (vs. within) cavities was the primary driver of diversity at the landscape scale. In Canada, as predicted, species accumulation was fastest when sampling across high-use cavities. In Argentina, the rates of species accumulation were similar across high- and low-use cavities, and fastest when both high- and low-use cavities were pooled. These findings imply that biodiversity of cavity nesters is maintained by a mix of long-lived (highly productive, legacy trees) and many high-turnover (single-use, fast decaying) tree cavities. Conservation of both long-lasting and single-use cavities should be incorporated into decisions about stand-level forest management, regional land use policies, and reserve networks.
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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 | 0.000 |
| 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 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".