Renqiang Li

Improving the estimate of forest biomass carbon storage by combining two forest inventory systems

ABSTRACT Quantifying forest carbon storage and its spatial distribution at regional scales is critical for the creation of greenhouse gases inventories, the evaluation of forest services and carbon-oriented forest management. The plot-based forest inventory (PBFI) and stand-based forest inventory (SBFI) collect extensive information on trees and stands respectively, and together, provide an opportunity to improve the regional estimates of forest carbon. In this study, we applied the SBFI to overcome the spatial extent limits of the PBFI in neighboring plots and improve the regional carbon estimation. We found that the forests in Sichuan Province reserved a total of 624.2 Tg C in biomass and featured a large spatial heterogeneity, with high values in natural forests and low values in plantations. We found that the solo use of PBFI derived a slightly higher (46.63 Mg C/ha) estimation on average compared with the integrated method (43.6 Mg C/ha). However, when considering the spatial distribution, the PBFI generated an overestimation of young forests located between 3000and 4000 m in elevation, and an underestimation in mature forests. The spatially explicit biomass carbon estimation could be helpful in guiding regional forest management and biodiversity conservation.

Spatiotemporal Assessment of Forest Biomass Carbon Sinks: The Relative Roles of Forest Expansion and Growth in Sichuan Province, China

Spatiotemporal patterns of forest carbon (C) sinks and accurate estimation of such patterns are crucial to sustainable forest management. We combined individual tree biomass equations and a Random Forest algorithm to assess the spatiotemporal changes in biomass C sequestration and to further quantify the relative contributions of forest areal expansion and growth to biomass C sinks in Sichuan Province, China, over the past 25 yr. Forest area and average biomass C density increased from 10.5 million ha and 45.7 Mg C ha in 1988 to 14.2 million ha and 52.3 Mg C ha in 2012. Average C density was generally larger in the north and west of Sichuan Province compared with other regions. The expanded forest area and enhanced C density have jointly led to a rise in total C storage by 54.9% over this period in Sichuan Province. It was estimated that the forest areal expansion has been a larger contributor to C sinks than forest growth in Sichuan Province (69 vs. 31%), especially in the regions of the northwestern high mountains and the hilly country of the Sichuan basin. However, the relative contributions of areal expansion exhibited different trends in five subregions and 15 forest species groups in this province. Our study suggests that it is necessary to develop a new forestry management mode to maintain the long-term health of forest ecosystems in Sichuan Province, which should attach more importance to improving forest quality and selecting tree species in different subregions while increasing forested area in the future.

Climatic information improves statistical individual-tree mortality models for three key species of Sichuan Province, China

Abstract• Key messageClimate variables improve individual-tree mortality models for fir, oak and birch.• ContextClimate is considered as an important driver of tree mortality, but few studies have included climate factors in models to explore their importance for modelling individual-tree mortality.• AimsTo measure the performance of climate-based models, we built individual-tree mortality models using individual, stand, and climate variables for fir (Abies faxoniana Rehd. et Wils.), oak (Quercus aquifolioides Rehd. et Wils.), and birch (Betula albo-sinensis Burk.) in Southwest China, and explored the corresponding effects on tree death.• MethodsWe developed tree mortality models based on 287 permanent plots from the Sichuan Forest Inventory data, and compared the models based on variables of individual (I), stand (S), and climate (C) levels, and different combinations (I + S, I + C, S + C, I + S + C) among these groups to improve model performance. We employed relative Akaike information criterion (AIC), area under receiver operating characteristic curve (AUC), and Hosmer-Lemeshow’s goodness-of-fit statistic for model evaluation and validation.• ResultsWe found that tree mortalities of the three species could be better predicted (AUC > 0.8) by carefully selecting variables at three ecological scales (individual, stand, and regional climate). Our results suggest that the higher mortality of the object trees occurs when they endure lower radial growth of the previous years, more intensive competition, and moderate canopy cover (for birch), while lower mortality was seen in an appropriate range of climate conditions and at higher stand canopy cover (for fir and oak).• ConclusionThe results have significance for incorporating the effects of a changing climate into mortality models.

Quantifying the evidence for co-benefits between species conservation and climate change mitigation in giant panda habitats

Conservationists strive for practical, cost-effective management solutions to forest-based species conservation and climate change mitigation. However, this is compromised by insufficient information about the effectiveness of protected areas in increasing carbon storage, and the co-benefits of species and carbon conservation remain poorly understood. Here, we present the first rigorous quantitative assessment of the roles of giant panda nature reserves (NRs) in carbon sequestration, and explore the co-benefits of habitat conservation and climate change mitigation. Results show that more than 90% of the studied panda NRs are effective in increasing carbon storage, with the mean biomass carbon density of the whole NRs exhibiting a 4.2% higher growth rate compared with lands not declared as NRs over the period 1988–2012, while this effectiveness in carbon storage masks important patterns of spatial heterogeneity across the giant panda habitats. Moreover, the significant associations have been identified between biomass carbon density and panda’s habitat suitability in ~85% NRs and at the NR level. These findings suggest that the planning for carbon and species conservation co-benefits would enhance the greatest return on limited conservation investments, which is a critical need for the giant panda after its conservation status has been downgraded from “endangered” to “vulnerable”.