Thinning intensity affects microbial functional diversity and enzymatic activities associated with litter decomposition in a Chinese fir plantation

Microbial functional diversity and enzymatic activities are critical to maintaining material circulation during litter decomposition in forests.Thinning,an important and widely used silvicultural treatment,changes the microclimate and promotes forest renewal.However,how thinning affects microbial functional diversity and enzymatic activities during litter decomposition remains poorly understood.We conducted thinning treatments in a Chinese fir plantation in a subtropical region of China with four levels of tree stem removal（0,30,50,and 70%）,each with three replicates,and the effects of thinning on microbial functional diversity and enzymatic activities were studied 7 years after treatment by collecting litter samples four times over a 1-year period.Microbial functional diversity and enzymatic activities were analyzed using Biolog Ecoplates（Biolog Inc.,Hayward,CA,USA）based on the utilization of 31 carbon substrates.Total microbial abundance during litter decomposition was lower after the thinning treatments than without thinning.Microbial functional diversity did not differ significantly during litter decomposition,but the types of microbial carbon-source utilization did differ significantly with the thinning treatments.Microbial cellulase and invertase activities during litter decomposition were significantly higher under the thinning treatments due to changes in the litter carbon concentration and the ratios of carbon and lignin to nitrogen.The present study demonstrated the important influence of thinning on microbial activities during litter decomposition.Moderate-intensity thinning may maximize vegetation diversity and,in turn,increase the available substrate sources for microbial organisms in litter and promote nutrient cycling in forest ecosystems.

Active forest management accelerates carbon storage in plantation forests in Lishui,southern China

Background:China has committed to achieving peak CO_(2)emissions before 2030 and carbon neutrality before 2060;therefore,accelerated efforts are needed to better understand carbon accounting in industry and energy fields as well as terrestrial ecosystems.The carbon sink capacity of plantation forests contributes to the mitigation of climate change.Plantation forests throughout the world are intensively managed,and there is an urgent need to evaluate the effects of such management on long-term carbon dynamics.Methods:We assessed the carbon cycling patterns of ecosystems characterized by three typical plantation species(Chinese fir(Cunninghamia lanceolata(Lamb.)Hook.),oak(Cyclobalanopsis glauca(Thunb.)Oerst.),and pine(Pinus massoniana Lamb.))in Lishui,southern China,by using an integrated biosphere simulator(IBIS)tuned with localized parameters.Then,we used the state-and-transition simulation model(STSM)to study the effects of active forest management(AFM)on carbon storage by combining forest disturbance history and carbon cycle regimes.Results:1)The carbon stock of the oak plantation was lower at an early age(<50 years)but higher at an advanced age(>50 years)than that of the Chinese fir and pine plantations.2)The carbon densities of the pine and Chinese fir plantations peaked at 70 years(223.36 Mg⋅ha^(‒1))and 64 years(232.04 Mg⋅ha^(‒1)),respectively,while the carbon density in the oak plantation continued increasing(>100 years).3)From 1989 to 2019,the total carbon pools of the three plantation ecosystems followed an upward trend(an annual increase of 0.16–0.22 Tg C),with the largest proportional increase in the aboveground biomass carbon pool.4)AFM increased the recovery of carbon storage after 1996 and 2009 in the pine and Chinese fir plantations,respectively,but did not result in higher growth in the oak plantation.5)The proposed harvest planning is reasonable and conducive to maximizing the carbon sequestration capacity of the forest.Conclusions:This study provides an example of a carbon cycle coupling model that is potentially suitable for simulating China's plantation forest ecosystems and supporting carbon accounting to monitor peak CO_(2)emissions and reach carbon neutrality.

Integrating cross-sensor high spatial resolution satellite images to detect subtle forest vegetation change in the Purple Mountains,a national scenic spot in Nanjing,China

Accurate information on the location and magnitude of vegetation change in scenic areas can guide the configuration of tourism facilities and the formulation of vegetation protection measures.High spatial resolution remote sensing images can be used to detect subtle vegetation changes.The major objective of this study was to map and quantify forest vegetation changes in a national scenic location,the Purple Mountains of Nanjing,China,using multi-temporal cross-sensor high spatial resolution satellite images to identify the main drivers of the vegetation changes and provide a reference for sustainable management.We used Quickbird images acquired in 2004,IKONOS images acquired in 2009,and WorldView2 images acquired in 2015.Four pixel-based direct change detection methods including the normalized difference vegetation index difference method,multi-index integrated change analysis(MIICA),principal component analysis,and spectral gradient difference analysis were compared in terms of their change detection performances.Subsequently,the best pixel-based detection method in conjunction with object-oriented image analysis was used to extract subtle forest vegetation changes.An accuracy assessment using the stratified random sampling points was conducted to evaluate the performance of the change detection results.The results showed that the MIICA method was the best pixel-based change detection method.And the object-oriented MIICA with an overall accuracy of 0.907 and a kappa coefficient of 0.846 was superior to the pixel-based MIICA.From 2004 to 2009,areas of vegetation gain mainly occurred around the periphery of the study area,while areas of vegetation loss were observed in the interior and along the boundary of the study area due to construction activities,which contributed to 79%of the total area of vegetation loss.During 2009–2015,the greening initiatives around the construction areas increased the forest vegetation coverage,accounting for 84%of the total area of vegetation gain.In spite of this,vegetation loss occurred in the interior of the Purple Mountains due to infrastructure development that caused conversion from vegetation to impervious areas.We recommend that:(1)a local multi-agency team inspect and assess law enforcement regarding natural resource utilization;and(2)strengthen environmental awareness education.

Correction to:Integrating cross-sensor high spatial resolution satellite images to detect subtle forest vegetation change in the Purple Mountains,a national scenic spot in Nanjing,China

The article "Integrating cross-sensor high spatial resolution satellite images to detect subtle forest vegetation change in the Purple Mountains,a national scenic spot in Nanjing,China",written by Fangyan Zhu,Wenjuan Shen,Jiaojiao Diao,Mingshi Li and Guang Zheng,was originally pub-lished electronically on the publisher’s Internet portal(currently SpringerLink)on 14 May 2019 without open access.

Characterizing the effects of climate change on short-term post-disturbance forest recovery in southern China from Landsat time-series observations(1988-2016)

Climate change,a recognized critical environmental issue,plays an important role in regulating the structure and function of forest ecosystems by altering forest disturbance and recovery regimes.This research focused on exploring the statistical relationships between meteorological and topographic variables and the recovery characteristics following disturbance of plantation forests in southern China.We used long-term Landsat images and the vegetation change tracker algorithm to map forest disturbance and recovery events in the study area from 1988 to 2016.Stepwise multiple linear regression(MLR),random forest(RF)regression,and support vector machine(SVM)regression were used in conjunction with climate variables and topographic factors to model short-term forest recovery using the normalized difference vegetation index(NDVI).The results demonstrated that the regenerating forests were sensitive to the variation in temperature.The fitted results suggested that the relationship between the NDVI values of the forest areas and the post-disturbance climatic and topographic factors differed in regression algorithms.The RF regression yielded the best performance with an R2 value of 0.7348 for the validation accuracy.This indicated that slope and temperature,especially high temperatures,had substantial effects on post-disturbance vegetation recovery in southern China.For other mid-subtropical monsoon regions with intense light and heat and abundant rainfall,the information will also contribute to appropriate decisions for forest managers on forest recovery measures.Additionally,it is essential to explore the relationships between forest recovery and climate change of different vegetation types or species for more accurate and targeted forest recovery strategies.