Modelling the Current and Future Spatial Distribution Area of Adansonia digitata L. in the Context of Climate Change in Malawi (Southern Africa)
Bibliographic record
Abstract
Climate change is likely to affect the distribution of species worldwide. Understanding how these changes affect species distribution is important for planning conservation strategies and sustainable management methods. Adansonia digitata L. is of major ecological and socio-economic importance in Malawi, South Africa, but is highly threatened in its habitat. This work aims to investigate the effect of climate change on the ecological niche of Baobab and to find suitable habitats for its conservation and cultivation in Malawi. The distribution of this species was modeled using a maximum entropy algorithm (MaxEnt) based on 21 environmental variables and the occurrence of 480 species. Habitat prioritization was performed using Zonation software. Our results show that the variable contributing most significantly was the warmest month (47%), followed by isotherms (13.9%) and precipitation of the coldest quarter (8.6%). Under the current model, 1.17% of Malawi’s territory is highly favorable for baobab development. A slight increase of 0.09% and 0.38% in highly favorable zones is predicted by 2055 under scenarios SSP370 and SSP 585, respectively. Southern Malawi and parts of the Central region should be prioritized in baobab reforestation policies to optimize conservation and value chain sustainability for baobab. Under the current model, 1.17% of Malawi will be highly favorable for baobab. A slight increase of 0.09 % and 0.38 % in highly favorable zones is predicted by 2055 under scenarios SSP 370 and SSP 585, respectively. Priority areas (98-100%) for conservation and cultivation of Baobab was a male located in the Southern region (34.51%) and central (7.62%), in contrast to the Northern region (0.21%). Our results suggest that climate change causes the reduction and shift of suitable habitats for species along a south-north gradient. These findings highlight the urgent need to incorporate climate change projections into conservation plans. Identifying and prioritizing suitable habitats in the southern and central regions is crucial for effective conservation and sustainability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".