Samuel Adeyanju, Cornelius K. A. Pienaah, Janette Bulkan, Mirjam A. F. Ros-Tonen, Terry Sunderland
Over the past four decades, community-based natural resource management (CBNRM) has gained global prominence as an alternative to exclusionary conservation. In Ghana, the Community Resource Management Area (CREMA) initiative represents the country’s principal CBNRM programme. Launched in the early 2000s, CREMA has expanded to over two million hectares across more than 600 communities, yet evidence of its performance remains fragmented across individual studies. This paper synthesizes the empirical literature to evaluate the environmental, economic, and socio-cultural outcomes of CREMAs in Ghana. We conducted a systematic review of 34 peer-reviewed articles, comprising 75 case studies across 24 CREMAs. Research attention is highly concentrated, with the Wechiau and Zukpiri CREMAs accounting for 38% of all documented cases. Overall, CREMAs exhibit substantial variability in outcomes. Environmental impacts range from increased conservation awareness and resource protection to continued deforestation and forest degradation. Economically, CREMAs generate income and employment opportunities, although benefits are often modest, unevenly distributed, and constrained by limited access to conservation financing. Socio-culturally, CREMAs can reinforce cultural norms, strengthen community cohesion, and enhance local management capacity, while also contributing to conflict, mistrust, and the erosion of traditional cultural practices in some contexts. We conclude that CREMAs remain highly relevant due to their geographic scope, strategic locations outside formal protected areas, integration with rural development, and growing policy significance, but their effectiveness is largely context-dependent. CREMAs should be considered as a complementary conservation approach alongside multiple-use protected areas and other effective area-based conservation measures. This review highlights key implications for future research, policy, and practice.
Soil respiration (Rs) is a key component of the terrestrial carbon cycle, yet its accurate prediction remains challenging due to complex interactions among environmental drivers and the distinct mechanisms governing its components. In this study, we conducted monthly 24-h measurement campaigns at hourly intervals of total soil respiration (Rs) and its components, autotrophic respiration (Ra) and heterotrophic respiration (Rh), across four typical forest types in the Wuling Mountains, a representative subtropical mountainous region in central–southern China. Multiple machine learning (ML) models were developed under different modeling strategies, feature engineering schemes, and validation protocols. Component-based modeling markedly outperformed unified and forest-type-based approaches, suggesting that under the current experimental design, differences among respiration components exerted a stronger influence on predictive performance than differences among forest types. Under random validation, ensemble tree models achieved high accuracy for Rs and Rh (R 2 > 0.90), but their advantage narrowed substantially under leave-one-month-out blocked validation. Notably, CatBoost trained without temporal variables showed the most consistent temporal generalization and was therefore selected for ecological interpretation. SHAP analysis revealed that deepest monitored soil hydrothermal coupling and vertical thermal structure were the dominant controls on Rs and Rh, particularly the interaction between the deepest monitored soil temperature and moisture; depth-ablation analysis further confirmed the independent predictive contribution of deep-layer measurements. In contrast, Ra was primarily related to deep-layer daytime temperature and moisture balance but remained the least predictable component, reflecting unmeasured plant physiological controls. Our results highlight the importance of integrating appropriate modeling strategies, validation protocols, and machine learning techniques to improve prediction of soil respiration components. The findings also emphasize the role of hydrothermal coupling and vertical heterogeneity in regulating soil carbon fluxes in subtropical forest ecosystems.
Catherine J Chamberlain, Lynn Riley, Sebastian U Busby, Matthew Russell, Kemen G Austin, Richard Campbell, John S Gunn, Trisha Johnson, David M Martin, Benjamin Rifkin, David Shoch, Ethan P Belair
Improved forest management (IFM) is vital for climate change mitigation. Yet, precise carbon accounting to ensure credible attribution is a challenge. Current accounting protocols vary in their analytical approaches, leading to substantial differences in mitigation outcomes. We evaluated four common decision points in baseline design—modeled versus measured, static versus dynamic, matched versus random, and single versus blended business-as-usual (BAU) scenarios— across diverse regions and forest types in the eastern United States (US) using Forest Inventory and Analysis (FIA) and the Forest Vegetation Simulator (FVS). Dynamic baselines that integrate blended BAUs produced, on average, 63% more conservative estimates of net climate mitigation benefits than static, single BAU models, highlighting how baseline design choices can substantially influence results, with estimated mitigation varying by more than fivefold across accounting approaches, regions, and forest types. Stand characteristics, such as stand density, also affected outcomes. Since the true BAU counterfactual cannot be directly observed, adopting dynamic baselines with blended BAU scenarios can reduce the risk of overestimation, strengthen the credibility of carbon accounting, support more informed investment in IFM projects, and reinforce the integrity of carbon accounting for natural climate solutions.
Alex Santillán-Sarmiento, Julio C. Carrión-Olmedo, Joseline Salinas-Tapia, Pamela Lojan-Cueva, Diana Flores, Daniela Reyes-Barriga, Liliana Jaramillo, Andrea E. Narváez, Pablo Jarrín-V
The Tumbes-Chocó-Magdalena hotspot is a globally recognized center of endemism that is undergoing severe deforestation. While conservation and restoration efforts typically prioritize the recovery of aboveground macroscopic biodiversity, the response of soil microbial communities remains largely uncharacterized in this region. To address this data gap, we assessed soil bacterial communities in three land-use types (reference forest, early-stage restored parcel, and degraded land) within the "La Esperanza Forest" reserve in the Ecuadorian Chocó. Using 16S rRNA gene metabarcoding and robust compositional data analysis (e.g., ALDEx2, PERMANOVA), we evaluated shifts in taxonomic diversity and community structure. Sequencing revealed 1,208 genera and dominant genera (e.g., Bacillus, Paenibacillus) that persisted across all habitats. However, despite the shared presence of these dominant taxa, the sampled land-use plots exhibited distinct structural differences in their microbial communities. Reference remnant forest's soils exhibited significantly higher community evenness and distinct multivariate partitioning driven by strongly associated structural taxa (e.g., Dinghuibacter, Phyllobacterium). Conversely, degraded lands were significantly enriched in stress-associated bacteria, notably Rhodanobacter. Early-stage restored parcel soil clustered structurally closer to degraded soil than to the reference forest, indicating a pronounced spatial compositional similarity to disturbed soils rather than the reference state. Our findings from this localized study indicate that the fundamental microbial scaffolding of this remnant Chocó Forest soil hosts a distinct bacterial community structure, and that recovering soil microbial diversity in the adjacent old agricultural land may require extended timeframes and additional restoration approaches. We highlight remnant forest patches as critical reservoirs of belowground biodiversity and emphasize the necessity of integrating soil microbiology into continuous, long-term conservation monitoring frameworks.
With the continuing expansion of the wildland-urban interface (WUI) and the increasing complexity of fire risk assessment, integrating heterogeneous and incomplete evaluation information remains an important challenge. This study develops a comprehensive WUI fire risk assessment framework based on gray linguistic 2-tuple relational analysis. The framework establishes a multidimensional indicator system comprising 15 indicators across four categories: fuel conditions, surrounding environment, meteorological conditions, and topographic characteristics. Crisp values, interval values, and fuzzy linguistic variables are transformed into a unified gray linguistic 2-tuple representation, in which the information gray degree explicitly characterizes the associated degree of information incompleteness. Multi-expert evaluation information is aggregated using the gray linguistic 2-tuple weighted averaging (GLTWA) operator, and the relative fire risks of the evaluated areas are ranked using a gray 2-tuple relational degree. A scenario-based numerical experiment is conducted using a controlled synthetic dataset and simulated expert profiles to illustrate the application of the proposed framework. Sensitivity analyses show that variations in information gray degrees and indicator weights affect the numerical relational degrees without changing the relative ranking of the five evaluated areas within the tested perturbation ranges. The computational complexity analysis further indicates that, when the numbers of indicators and experts are fixed, the computational cost scales approximately linearly with the number of evaluated areas. These results suggest that the proposed framework provides a unified and computationally efficient approach for comparative WUI fire risk assessment under heterogeneous and incomplete evaluation information, while further empirical validation using real-world WUI data remains necessary.
