Modelling the impact of climate change on Tanzanian forests
Bibliographic record
Abstract
Abstract Aim Climate change is pressing extra strain on the already degraded forest ecosystem in Tanzania. However, it is mostly unknown how climate change will affect the distribution of forests in the future. We aimed to model the impacts of climate change on natural forests to help inform national‐level conservation and mitigation strategies. Location Tanzania. Methods We conducted maximum entropy (MaxEnt) modelling to simulate forest habitat suitability using the Tanzanian national forest inventory survey (1,307 occurrences) and environmental data. Changes in forest habitats were simulated under two Representative Concentration Pathways (RCPs) emission scenarios RCP 4.5 and RCP 8.5 for 2055 and 2085. Results The results indicate that climate change will threaten forest communities, especially fragmented strips of montane forests. Even under optimistic emission scenario, the extent of montane forest is projected to almost halve by 2085, intersecting many biodiversity hotspots across the Eastern Arc Mountains. Similarly, climate change is predicted to threaten microhabitat forests (i.e. thickets), with losses exceeding 70% by 2085 (RCP8.5). Other forest habitats are predicted to decrease (lowland forest and woodland) representing essential ecological networks, whereas suitable habitats for carbon‐rich mangroves are predicted to expand by more than 40% at both scenarios. Conclusions Climate change will impact forests by accelerating habitat loss, and fragmentation and the remaining land suitable for forests will also be subject to pressures associated with rising demand for food and biofuels. These changes are likely to increase the probability of adverse impacts to the country's indigenous flora and fauna. Our findings, therefore, call for a shift in conservation efforts, focusing on (i) the enhanced management of existing protected areas that can absorb the impacts of future climate change, and (ii) expanding conservation efforts into newly suitable regions through effective land use planning and land reclamation, helping to preserve and enhance forest connectivity between fragmented patches.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".