Causal effects of latitude, disturbance and dispersal limitation on richness in a recovering temperate, subtropical and tropical forest
Bibliographic record
Abstract
Abstract Question Do regional differences in latitude, and local factors of disturbance and distance from mature forest, influence dispersal syndromes, rate of accumulation of species and total richness in a recovering temperate, subtropical and tropical forest? Location Temperate old‐growth red pine forest, Canada; subtropical Araucaria Atlantic forest, southern Brazil; tropical Gallery forest, central Brazil. Methods We used path analysis to determine causal relationships of regional (latitude) and local (disturbance intensity, distance of recovering site from forest) factors on percentage zoochory, exponent z from species–area relationships and total richness. Results Path results showed linear decreases in percentage zoochory, z and richness with increasing latitude. Disturbance and distance from mature forest each reduced richness by similar amounts; however responses of percentage zoochory and z were inconsistent between the two local factors. A second path model, using only the subtropical and tropical forest, greatly increased model significance, yielded insignificant regional effects and strengthened effects of local factors on the three richness responses. Important results from this path model were linear decreases of z (0.1) and percentage zoochory (10%) and a log‐decrease of richness (five‐fold) 100 m from the mature subtropical and tropical forest. Conclusions Regional differences in latitude and local effects of dispersal limitation can influence forest richness as strongly as disturbance alone. Isolated, recovering subtropical and tropical forest fragments of southern and central Brazil may therefore be recovering at much slower rates than estimates made from those adjacent to mature forest stands.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 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".