THE EFFECT OF FOREST FRAGMENTATION ON TREE SPECIES ABUNDANCE AND DIVERSITY IN THE EASTERN ARC MOUNTAINS OF TANZANIA
Bibliographic record
Abstract
Habitat fragmentation is considered a threat to biodiversity conservation.Uluguru forest block, a section of the Eastern Arc Mountains in Tanzania remains highly vulnerable to fragmentation.However, to date, fragmentation effects on species abundance and diversity have not been investigated.This study aimed at investigating effects of fragmentation on species abundance and diversity in Uluguru forest block, Morogoro region, Tanzania.A RapidEye satellite image was analyzed using the maximum likelihood classifier (MLC) to map the fragmented forest.Remotely sensed variables with data on species diversity were modelled using the Generic Algorithm for Rule-Set Prediction (GARP) algorithm while fragmentation parameters were extracted using Fragstats software, which were then linked to species and edaphic factors.Results showed that species diversity was predicted better with customized environmental variables which recorded an Area Under Curve (AUC) of 0.89.The Poisson regression results showed that individual tree species responded differently to patch area dynamics, habitat status and soil nitrogen.Generally, the abundance of dominant species like Mytenus undata Thunb (p < 0.001), Zenkerella capparidacea (Taub.)J. Leon (p < 0.001) and Oxyanthus specious DC. (p = 0.023) decreased with a reduction in patch area.The present study suggests the need to integrate comprehensive plans and other intervention measures into long-term intervention initiatives.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".