Temporal dynamics in animal community assembly during post-logging succession in boreal forest
Bibliographic record
Abstract
Species assemblages can result from deterministic processes, such as niche differentiation and interspecific interactions, and from stochastic processes, such as random colonisation and extinction events. Although changes in animal communities following disturbances have been widely examined, few studies have investigated the mechanisms structuring communities during ecological succession. We assessed the impact of logging on small mammal and beetle assemblages in landscapes dominated by old-growth boreal forests. Our objectives were to 1) characterize variations in communities during the first 66 years of post-harvest forest succession, 2) determine if there are non-random patterns of species co-occurrence (i.e., deterministic processes), and if there are, 3) establish whether non-random co-occurrences are best explained by habitat attributes or by interspecific interactions. We captured small mammals and beetles along a gradient of forest succession (5-66 years) and in old-growth forest, and characterized key vegetation attributes. First, we tested whether community compositions in clear-cut stands became similar to those in natural stands after 66 years. We then used null models, which were either unconstrained or constrained by habitat attributes, to address the last two objectives and distinguish effects of vegetation attributes from interspecific interactions on community assembly. We showed that beetle assemblages differed in stands 21-30 years post-harvest compared to old-growth forests. In contrast, harvesting did not influence the composition of small mammal communities. Overall, our results suggest that community assembly during forest succession is driven by both stochastic and deterministic processes, the latter being linked to interspecific interactions more strongly than to vegetation attributes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".