Human and Environmental Factors Shape Tree Species Assemblages in West African Tropical Forests
Bibliographic record
Abstract
Human activities exert a pronounced influence on forest ecosystems, impacting both biodiversity and function across multiple scales. Despite this, the consequences of low-intensity human activities on tropical forest ecosystems are difficult to assess and, therefore, remain poorly explored. Here, the influence of human activities and other site-specific variables on forest tree assemblages in central-west Africa was investigated. By dividing forest tree species into edible (from the perspective of humans) and inedible species, we aimed to assess the differential impacts of human resource use on different species; in particular, the greatest impact of human activity was expected to be seen on edible tree species. Tree data from 66 plots across Nigeria and Cameroon collected between 2002 and 2019 and Generalized Dissimilarity Models (GDMs) were used to assess pairwise beta-diversity between plots, accounting for candidate factors including proximity to human presence, elevation, and stem density. The analysis revealed that human activity significantly affects beta-diversity within the Nigeria-Cameroon forest region. The key variables that shape total beta-diversity included geographical distance between plots, plot elevation, stem density, proximity to human presence, and forest species composition. The forest species composition (monodominant or mixed forest) appeared to influence dissimilarity in beta-diversity, specifically for edible tree species. This pattern was not observed for inedible species, likely linked to the cultural practices in the region. While stem density contributed to the edible species models, elevation was more relevant for inedible species. These findings underscore the critical role of human influence in shaping tree species assemblages in African tropical forests and stress the necessity for further research in this area.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".