Typology and Floristic Diversity of Urban Green Spaces Along an Urbanisation Gradient: Implications for Ecological Resilience in Lomé and Kara (Togo)
Bibliographic record
Abstract
Urban green spaces (UGS) are essential to the sustainability of cities through the ecosystem services they provide (climate regulation, biodiversity conservation, and improvement of the living environment).In West Africa, rapid urbanisation is compromising their development, and in Togo, data on their typology and floristic diversity remain limited.This study, conducted in Lom and Kara, aims to fill this gap by characterising the types of UGS and their floristic composition for better sustainable management.Based on field surveys, floristic inventories, and administrative data, a typology adapted from the AIVF reference system was used, and diversity was assessed using the Shannon (species richness and relative abundance) and Pielou indices (balanced distribution of individuals).The results indicate greater diversity in Lom than in Kara according to four functional categories, with a total of 14 types of EVUs and 368 species (82 species/hectare) identified in Lom , compared to 12 types and 225 species (78 species/hectare) identified in Kara.This reflects a more pronounced gradient of artificialisation in the capital.Floristic homogenisation is observed in both cities, dominated by exotic species such as Azadirachta indica (Fabaceae), Mangifera indica (Meliaceae), and Senna siamea (Poaceae).The contrasts in the distribution of biological and phytogeographical types reflect the socio-ecological specificities of the two urban contexts.These results highlight the influence of Urban and bioclimatic factors on ecosystems, underscoring the need to integrate native species to enhance cities' ecological resilience.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".