Human and Environmental Factors Shape Tree Species Assemblages in West African Tropical Forests
Bibliographic record
Abstract
Human activities exert pronounced influence on forest ecosystems, impacting biodiversity and functions across multiple scales. However, the consequences of low-intensity human activities on tropical forest ecosystems are difficult to assess and remain poorly explored. The influence of human activities and other site-specific variables on forest tree assemblages in central-west Africa was investigated. The greatest impact of human activity was expected to be seen on edible tree species. Tree species in the forest were divided into edible (consumed by humans) and inedible species to assess the differential impacts of human resource use on species. Tree data from 66 plots in Nigeria and Cameroon (collected between 2002 and 2019) were analysed using Generalized Dissimilarity Models (GDMs) to assess pairwise beta-diversity between plots. Human activity significantly affected beta-diversity within the Nigeria-Cameroon forest region. Total beta-diversity was shaped by 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 for edible tree species only, likely linked to cultural practices in the region. Influence of elevation was significant for inedible species only, due to access restriction. These findings underscore the role of human influence in shaping tree species assemblages in African tropical forests and stress the necessity for further research in this area.
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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 | 0.000 | 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".