Establishment of Markov successional model and its application for forest restoration reference in Southern China

Forest succession is the base of establishing restoration reference which plays an important role in forest restoration and restoration estimation. The study presented the establishment of a Markov successional model (MSM) and its application to restoration reference in lower subtropical forest in China. The compositions of successional system in MSM were divided into three species types: pioneering pine trees, heliophytic trees and mesophytic trees. The successional system was divided into three subsystems: early successional stage, mid-successional stage and late-successional stage. Based on the site survey on the changes in the species and their individuals in 25 years, the transition matrices in various subsystems were determined. The predicted results were used to establish the restoration reference of the vegetation restoration in lower subtropical China. According to the ecological restoration reference established in this study, it would take 150 years for the forest to change from pioneer to mature communities in the region. Successional change of tree composition was forecast by the model, and the scenario forecast by the model reflects the actual conditions observed through 52 years of long-term permanent site research. The restoration experience in the region matches the forecast results. The application of a restoration reference model indicates that forest restoration can be accelerated by taking measures which change forest structure. The above results imply that a restoration reference established on the rule of regional forest succession could be very useful not only in directing, but also in assessing and managing regional forest restoration. Previously, one “ideal reference ecosystem” was used as a restoration reference in all correlative studies. In this study, the restoration “process” was used as the restoration reference.     

Performance of a Bayesian state-space model of semelparous species for stock-recruitment data subject to measurement error

Measurement errors in spawner abundance create problems for fish stock assessment scientists. To deal with measurement error, we develop a Bayesian state-space model for stock-recruitment data that contain measurement error in spawner abundance, process error in recruitment, and time series bias. Through extensive simulations across numerous scenarios, we compare the statistical performance of the Bayesian state-space model with that of standard regression for a traditional stock-recruitment model that only considers process error. Performance varies depending on the information content in data, as determined by stock productivity, types of harvest situations, and amount of measurement error. Overall, in terms of estimating optimal spawner abundance S_MSY, the Ricker density-dependence parameter β, and optimal harvest rate h_MSY, the Bayesian state-space model works best for informative data from low and variable harvest rate situations for high-productivity salmon stocks. The traditional stock-recruitment model (TSR) may be used for estimating α and h_MSY for low-productivity stocks from variable and high harvest rate situations. However, TSR can severely overestimate S_MSY when spawner abundance is measured with large error in low and variable harvest rate situations. We also found that there is substantial merit in using h_MSY (or benchmarks derived from it) instead of S_MSY as a management target.     

Economic contribution and the potential use of wood charcoal for soil restoration: a case study of village-based charcoal production in Central Laos

Wood charcoal production provides affordable energy in many developing countries and has substantially contributed to the economy through the provision of rural incomes. In several countries, charcoal production leads to overexploitation of forests due to inefficiencies in processing. This study was undertaken in central Laos to (1) examine and document traditional charcoal production systems; (2) investigate the production capacity, recovery efficiencies and economic gains of existing traditional charcoal production methods; (3) characterize the chemical properties of wood charcoal and investigate the potential for soil restoration and (4) investigate local charcoal producers' perception on forest degradation and their species preferences. Through a socio-economic survey, a cost-based method for economic valuation was undertaken on a range of charcoal production methods currently being used. Laboratory chemical analyses were performed on wood charcoal samples. Results indicated that the traditional mud charcoal mound was used by the majority (82%) of charcoal producers. Total charcoal production per production cycle varied between 400 (produced from 2.7 m³ of wood) and 1600 kg (produced from 18 m³ of wood), with a mean of 938 kg (±120) for traditional mud charcoal mounds. The volume of the traditional mud charcoal mounds correlated positively and significantly with total charcoal production (R ² = 0.45, p?=?0.03), whereas correlated negatively and significantly with the recovery efficiency (R ² = 0.58, p?=?0.01). On average, the local producers receive a total net benefit of 457,272 Lao kip (USD 57.2) in 17 days. We also identified a rice husk mound method of charcoal production, which may not encourage further deforestation while producing rice husk biochar that can be used for soil restoration. Furthermore, we found that there are significant differences (p < 0.05) between the sampled wood charcoals in chemical properties, indicating that the potential of using wood charcoal for the restoration of degraded soils varies from charcoal to charcoal.