Human and Environmental Factors Shape Tree Species Assemblages in West African Tropical Forests
Bibliographic record
Abstract
ABSTRACT Aim This study investigated how human activities and local environmental variables shape tree assemblages (species composition in a defined location), comparing their effects on edible and inedible tree species. Three hypotheses were tested: (1) Environmental filtering impacts spatial beta‐diversity more than dispersal limitation; (2) human activities significantly influence regional tree beta‐diversity; and (3) predictors of beta‐diversity differ between edible and inedible species. Location Tropical forest in Nigeria and Cameroon in West and Central Africa. Methods Tree data were collected between 2002 and 2019 from 66 forest plots. Species were categorised as edible and inedible by humans using interviews and online databases. Pairwise beta‐diversity (partitioned into total beta‐diversity and turnover) between plots was analysed using Generalised Dissimilarity Models (GDMs) with geographical distance, plot‐specific variables (forest composition, climate, elevation, stem density, human influence indicators), and human influence indicators (distance to closest human presence [DCHP], and nearest anthropogenic edges [DNAE]) as predictors. Results The dataset included 236 edible species (11,097 stems) and 472 inedible species (17,202 stems), with high species turnover (> 90%) dominating beta‐diversity patterns. Due to local plot‐level factors, environmental filtering (deviance explained for all species: 37.4%, edible: 18.9% and inedible: 31.4%) exerted greater influence on species assemblages than geographical distance alone. Beta‐diversity drivers differed between edible and inedible species: elevation strongly influenced turnover in inedible species, whereas forest composition significantly shaped the assemblage of edible species, reflecting patterns of human‐mediated species selection and species dominance. Human presence impacted the overall beta‐diversity of inedible species but only influenced the turnover component of edible species. Main Conclusions Tree assemblages in the Nigeria–Cameroon forest region were primarily structured by local environmental conditions and human activities rather than by dispersal limitation. Effective conservation should incorporate sustainable human activities and traditional ecological knowledge, with further research needed to explore the long‐term anthropogenic impacts on these forests.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".