Tanzania dedicates a substantial proportion (38%) of its territory to conservation, with a large number of Protected Areas (PAs) managed under various regimes. Nevertheless, the country still experiences high rates of deforestation, which threaten the ecological integrity and socio-economic benefits of its forests. We utilized the Global Forest Change Dataset (2012–2022) and implemented a Propensity Score Matching (PSM) approach followed by a series of binomial logit regression modeling. Our objectives were to evaluate (1) the likelihood of PAs in avoiding deforestation compared with unprotected forest landscapes, (2) the variability in effectiveness among the different PA management regimes in avoiding deforestation, (3) evidence of leakage, defined here as the displacement of deforestation beyond PA boundaries as a result of protection inside PAs. Our findings reveal that, despite ongoing deforestation within and outside of PAs, conservation efforts are, on average, three times more likely to avoid deforestation compared with unprotected landscapes. However, the effectiveness of avoiding deforestation significantly varies among the different management regimes. National Parks and Game Reserves are nearly ten times more successful in avoiding deforestation, likely because of the stringent set of regulations and availability of resources for implementation. Conversely, Nature Forest Reserves, Game Controlled Areas, and Forest Reserves are, on average, only twice as likely to avoid deforestation, indicating substantial room for improvement. We found little evidence of the overall leakage as a consequence of protection. These results highlight the mixed success of Tanzania’s conservation efforts, suggesting opportunities to enhance the effectiveness of many less protected PAs. We conclude by proposing potential strategic pathways to enhance further the climate and ecosystem benefits of conservation in Tanzania.

This study assessed the structure and composition of a Miombo woodland stand subjected to selective logging through a forest inventory, measuring all trees with DBH ≥ 10 cm across 34 plots (1 ha each) for diameter, height, stem quality, and health status. The stand had a mean stem density of 255 stems/ha, basal area of 15 m²/ha, above ground biomass of 110 Mg/ha, and total volume of 145 m³/ha. The Fabaceae family, particularly Brachystegia spiciformis, dominated the composition. Diversity indices revealed moderate diversity (Shannon = 2.3, Simpson = 0.8, Pielou = 0.6), with a few dominant species. The diameter distribution followed a reverse J-shaped pattern typical of Miombo woodlands. The study (LevasFlor. (2024). Plano De Maneio Da LevasFlor, LDA) highlighted common features of selectively logged woodlands, including a low occurrence of large-diameter individuals from high-value commercial species, prevalence of disturbance-tolerant species, and limited regeneration for some species. These findings underscore the need for management strategies that balance ecological and socio-economic factors, mitigate logging impacts, promote regeneration, and ensure long-term sustainability. Effective policies are crucial for maintaining the ecological integrity and economic value of Miombo woodlands while addressing climate resilience and biodiversity conservation.

The Miombo woodlands are declining in both area and value, primarily due to over-harvesting of commonly preferred species. These forests, however, still contain several other species that are potentially of commercial importance. This study aimed to address the need for improved volume and biomass estimates for the sustainable management and utilization of two of the most abundant timber species in Mozambique’s Miombo woodlands: Brachystegia spiciformis (common name: Messassa) and Julbernardia globiflora (common name: red Messassa). Non-linear models were developed to estimate the merchantable wood volume under bark, heartwood volume, and biomass. The volume and biomass models for wood and heartwood volume, which included both diameter at breast height (DBH) and tree height as predictor variables, outperformed single-predictor models. However, the performance of some ratio models using DBH as the only predictor variable surpassed that of models using two predictor variables. The developed models are recommended for adoption by forest companies to increase economic and environmental benefits as they can refine harvest planning by improving the selection of trees for harvesting. Proper tree selection enhances the rate of recovery of high-quality timber from heartwood while observing sustainable forest management practices in Miombo and increasing the proportion of carbon removed from forests, which is subsequently stored in wood products outside the forest.