Collective property rights lead to secondary forest growth in the Brazilian Amazon.

Forests serve a crucial role in our fight against climate change. Secondary forests provide important potential for conservation of biodiversity and climate change mitigation. In this paper, we explore whether collective property rights in the form of indigenous territories (ITs) lead to higher rates of secondary forest growth in previously deforested areas. We exploit the timing of granting of property rights, the geographic boundaries of ITs and two different methods, regression discontinuity design and difference-in-difference, to recover causal estimates. We find strong evidence that indigenous territories with secure tenure not only reduce deforestation inside their lands but also lead to higher secondary forest growth on previously deforested areas. After receiving full property rights, land inside ITs displayed higher secondary forest growth than land outside ITs, with an estimated effect of 5% using our main RDD specification, and 2.21% using our difference-in-difference research design. Furthermore, we estimate that the average age of secondary forests was 2.2 y older inside ITs with secure tenure using our main RDD specification, and 2.8 y older when using our difference-in-difference research design. Together, these findings provide evidence for the role that collective property rights can play in the push to restore forest ecosystems.

Determinants of tree population temporal stability in a temperate mixed forest over a gradient of nitrogen addition.

Nitrogen (N) deposition is a significant threat to the functioning of forests and negatively impacts the delivery of forest goods and services. Contemporary management approaches seek to adapt forests to such N-deposition stressors, but to date how plant populations in natural forests respond to N deposition and what factors determine the contrasting responses among populations are still unclear. Here, we investigated the impact of N-addition (control: 0 kg ha-1 yr-1; low: 25 kg ha-1 yr-1; medium: 50 kg ha-1 yr-1; high: 75 kg ha-1 ha yr-1) on tree population temporal stability and how initial tree size, mycorrhizal type, and leaf N content (LNC; as a surrogate for functional trait composition) mediate tree population responses to N-addition in a Korean pine and mixed broadleaved dominated temperate forest in northern China. We quantified tree species population temporal stability as the ratio of mean to standard deviation of the year-by-year stem increments recorded in individual trees from 2015 to 2022 experimental period. The results showed different temporal stabilities of tree species among four N-addition levels, with the highest population stability observed within the high N-addition plots. Furthermore, initial tree size had significantly (p < 0.001) positive effects on population temporal stability. The effect of LNC and initial tree size were also contingent on the level of N applied. Specifically, increase in tree population LNC reduced population temporal stability in all plots where N was added. Our results imply that retention of large-sized trees and species with resource-conservative strategies (e.g., low LNC) could enhance forest stability under N deposition.

Future supply of boreal forest ecosystem services is driven by management rather than by climate change.

Forests provide a wide variety of ecosystem services (ES) to society. The boreal biome is experiencing the highest rates of warming on the planet and increasing demand for forest products. To foresee how to maximize the adaptation of boreal forests to future warmer conditions and growing demands of forest products, we need a better understanding of the relative importance of forest management and climate change on the supply of ecosystem services. Here, using Finland as a boreal forest case study, we assessed the potential supply of a wide range of ES (timber, bilberry, cowberry, mushrooms, carbon storage, scenic beauty, species habitat availability and deadwood) given seven management regimes and four climate change scenarios. We used the forest simulator SIMO to project forest dynamics for 100 years into the future (2016-2116) and estimate the potential supply of each service using published models. Then, we tested the relative importance of management and climate change as drivers of the future supply of these services using generalized linear mixed models. Our results show that the effects of management on the future supply of these ES were, on average, 11 times higher than the effects of climate change across all services, but greatly differed among them (from 0.53 to 24 times higher for timber and cowberry, respectively). Notably, the importance of these drivers substantially differed among biogeographical zones within the boreal biome. The effects of climate change were 1.6 times higher in northern Finland than in southern Finland, whereas the effects of management were the opposite-they were three times higher in the south compared to the north. We conclude that new guidelines for adapting forests to global change should account for regional differences and the variation in the effects of climate change and management on different forest ES.

How biotic, abiotic, and functional variables drive belowground soil carbon stocks along stress gradient in the Sundarbans Mangrove Forest?

Mangrove forests, some of the most carbon-dense ecosystems on Earth, play an important role in climate change mitigation through storing carbon in the soil. However, increasing anthropogenic pressures and sea level rise are likely to alter mangrove forest structure and functions, including the major source of carbon in mangrove ecosystems - below-ground soil carbon stocks (BSCS). Although estimating soil carbon stocks has been a popular practice in the mangroves, but poorly understood the (I) the linkage between BSCS and key ecosystem drivers (i.e., biotic, abiotic, and functional) and in (II) determining the pathways of how BSCS and multiple forest variables interact along stress gradients. This lack of understanding limits our ability to predict ecosystem carbon dynamics under future changes in climate. Here, we aimed to understand how abiotic factors (such as salinity, canopy gap fraction, nutrients, and soil pH), biotic factors (e.g., structural parameters, canopy packing, and leaf area index, LAI), and forest functional variables (e.g., growth and aboveground biomass stocks, AGB) affect BSCS (i.e., soil organic carbon, SOC, and root carbon, RC) using spatiotemporal data collected from the Sundarbans Mangrove Forest (SMF) in Bangladesh. We observed that BSCS decreased significantly with increasing salinity (e.g., from 70.6 Mg C ha-1 in the low-saline zone to 44.6 Mg C ha-1 in the high-saline zone). In contrast, the availability of several macronutrients (such as nitrogen, phosphorous, and potassium), LAI, species diversity, AGB, and growth showed a significant positive effect on SOC and RC. Stand properties, including tree height, basal area, density, canopy packing, and structural diversity, had a non-significant but positive impact on RC, while tree height and basal area significantly influenced SOC. Pathway analysis showed that salinity affects BSCS variability directly and indirectly by regulating stand structure and restricting nutrients and forest functions, although basal area, nutrients, and LAI directly enhance RC stocks. Our results indicate that an increase in nutrient content, canopy density, species diversity, and leaf area index can enhance BSCS, as they improve forest functions and contribute to a better understanding of the underlying mechanisms.

Binary Bamboo Forest Growth Optimization Algorithm for Feature Selection Problem.

Inspired by the bamboo growth process, Chu et al. proposed the Bamboo Forest Growth Optimization (BFGO) algorithm. It incorporates bamboo whip extension and bamboo shoot growth into the optimization process. It can be applied very well to classical engineering problems. However, binary values can only take 0 or 1, and for some binary optimization problems, the standard BFGO is not applicable. This paper firstly proposes a binary version of BFGO, called BBFGO. By analyzing the search space of BFGO under binary conditions, the new curve V-shaped and Taper-shaped transfer function for converting continuous values into binary BFGO is proposed for the first time. A long-mutation strategy with a new mutation approach is presented to solve the algorithmic stagnation problem. Binary BFGO and the long-mutation strategy with a new mutation are tested on 23 benchmark test functions. The experimental results show that binary BFGO achieves better results in solving the optimal values and convergence speed, and the variation strategy can significantly enhance the algorithm's performance. In terms of application, 12 data sets derived from the UCI machine learning repository are selected for feature-selection implementation and compared with the transfer functions used by BGWO-a, BPSO-TVMS and BQUATRE, which demonstrates binary BFGO algorithm's potential to explore the attribute space and choose the most significant features for classification issues.

An Entropy-Balanced Orthogonal Learning Bamboo Forest Growth Optimization Algorithm with Quasi-Affine Transformation Evolutionary and Its Application in Capacitated Vehicle Routing Problem.

The bamboo forest growth optimization (BFGO) algorithm combines the characteristics of the bamboo forest growth process with the optimization course of the algorithm. The algorithm performs well in dealing with optimization problems, but its exploitation ability is not outstanding. Therefore, a new heuristic algorithm named orthogonal learning quasi-affine transformation evolutionary bamboo forest growth optimization (OQBFGO) algorithm is proposed in this work. This algorithm combines the quasi-affine transformation evolution algorithm to expand the particle distribution range, a process of entropy increase that can significantly improve particle searchability. The algorithm also uses an orthogonal learning strategy to accurately aggregate particles from a chaotic state, which can be an entropy reduction process that can more accurately perform global development. OQBFGO algorithm, BFGO algorithm, quasi-affine transformation evolutionary bamboo growth optimization (QBFGO) algorithm, orthogonal learning bamboo growth optimization (OBFGO) algorithm, and three other mature algorithms are tested on the CEC2017 benchmark function. The experimental results show that the OQBFGO algorithm is superior to the above algorithms. Then, OQBFGO is used to solve the capacitated vehicle routing problem. The results show that OQBFGO can obtain better results than other algorithms.

Energy-Efficient Clustering Mechanism of Routing Protocol for Heterogeneous Wireless Sensor Network Based on Bamboo Forest Growth Optimizer.

In wireless sensor networks (WSN), most sensor nodes are powered by batteries with limited power, meaning the quality of the network may deteriorate at any time. Therefore, to reduce the energy consumption of sensor nodes and extend the lifetime of the network, this study proposes a novel energy-efficient clustering mechanism of a routing protocol. First, a novel metaheuristic algorithm is proposed, based on differential equations of bamboo growth and the Gaussian mixture model, called the bamboo growth optimizer (BFGO). Second, based on the BFGO algorithm, a clustering mechanism of a routing protocol (BFGO-C) is proposed, in which the encoding method and fitness function are redesigned. It can maximize the energy efficiency and minimize the transmission distance. In addition, heterogeneous nodes are added to the WSN to distinguish tasks among nodes and extend the lifetime of the network. Finally, this paper compares the proposed BFGO-C with three classic clustering protocols. The results show that the protocol based on the BFGO-C can be successfully applied to the clustering routing protocol and can effectively reduce energy consumption and enhance network performance.

Forest density intensifies the importance of snowpack to growth in water-limited pine forests.

Warming climate and resulting declines in seasonal snowpack have been associated with drought stress and tree mortality in seasonally snow-covered watersheds worldwide. Meanwhile, increasing forest density has further exacerbated drought stress due to intensified tree-tree competition. Using a uniquely detailed data set of population-level forest growth (n = 2,495 sampled trees), we examined how inter-annual variability in growth relates to snow volume across a range of forest densities (e.g., competitive environments) in sites spanning a broad aridity gradient across the United States. Forest growth was positively related to snowpack in water-limited forests located at low latitude, and this relationship was intensified by forest density. However, forest growth was negatively related to snowpack in a higher latitude more energy-limited forest, and this relationship did not interact with forest density. Future reductions in snowpack may have contrasting consequences, as growth may respond positively in energy-limited forests and negatively in water-limited forests; however, these declines may be mitigated by reducing stand density through forest thinning.

It is not just a 'trade-off': indications for sink- and source-limitation to vegetative and regenerative growth in an old-growth beech forest.

Controls on tree growth are key issues in plant physiology. The hypothesis of our study was that the interannual variability of wood and fruit production are primarily controlled directly by weather conditions (sink limitation), while carbon assimilation (source limitation) plays a secondary role. We analyzed the interannual variability of weather conditions, gross primary productivity (GPP) and net primary productivity (NPP) of wood and fruits of an old-growth, unmanaged Fagus sylvatica forest over 14 yr, including six mast years. In a multiple linear regression model, c. 71% of the annual variation in wood-NPP could be explained by mean air temperature in May, precipitation from April to May (positive influence) and fruit-NPP (negative influence). GPP of June to July solely explained c. 42% of the variation in wood-NPP. Fruit-NPP was positively related to summer precipitation 2 yr before (R2  = 0.85), and negatively to precipitation in May (R2  = 0.83) in the fruit years. GPP had no influence on fruit-NPP. Our results suggest a complex system of sink and source limitations to tree growth driven by weather conditions and going beyond a simple carbon-mediated 'trade-off' between regenerative and vegetative growth.

The climate sensitivity of carbon, timber, and species richness covaries with forest age in boreal-temperate North America.

Climate change threatens the provisioning of forest ecosystem services and biodiversity (ESB). The climate sensitivity of ESB may vary with forest development from young to old-growth conditions as structure and composition shift over time and space. This study addresses knowledge gaps hindering implementation of adaptive forest management strategies to sustain ESB. We focused on a number of ESB indicators to (a) analyze associations among carbon storage, timber growth rate, and species richness along a forest development gradient; (b) test the sensitivity of these associations to climatic changes; and (c) identify hotspots of climate sensitivity across the boreal-temperate forests of eastern North America. From pre-existing databases and literature, we compiled a unique dataset of 18,507 forest plots. We used a full Bayesian framework to quantify responses of nine ESB indicators. The Bayesian models were used to assess the sensitivity of these indicators and their associations to projected increases in temperature and precipitation. We found the strongest association among the investigated ESB indicators in old forests (>170 years). These forests simultaneously support high levels of carbon storage, timber growth, and species richness. Older forests also exhibit low climate sensitivity of associations among ESB indicators as compared to younger forests. While regions with a currently low combined ESB performance benefitted from climate change, regions with a high ESB performance were particularly vulnerable to climate change. In particular, climate sensitivity was highest east and southeast of the Great Lakes, signaling potential priority areas for adaptive management. Our findings suggest that strategies aimed at enhancing the representation of older forest conditions at landscape scales will help sustain ESB in a changing world.

Projecting global planted forest area developments and the associated impacts on global forest product markets.

Planted forests are a rising share of total forests globally and an increasingly important source of timber product output, affecting national and global markets. We estimated econometric models of planted forest area by OECD and non-OECD country groups that control for economic, institutional and environmental policies likely to influence future changes in planted forest area. The models are then used to project planted forest area over next 55 years for 180 countries under five alternative scenarios of global socio-economic changes, represented in shared socioeconomic pathways (SSPs), adjunct products emerging from the Fifth Assessment of the Intergovernmental Panel on Climate Change (IPCC). By embedding key features of the SSP projections into a global forest sector model, we evaluate how planted forests lead to different global forest product market outcomes for each SSP, compared to corresponding outcomes where planted forests are not considered separately. Projected global planted forest area in 2070 ranges from 379 million ha (Mha) for SSP3 (a relatively poor and unequal world) to 475 Mha under SSP5 (a relatively wealthier and more equal world), representing respective increases of 46% and 66% compared to 2015. SSPs with the highest planted forest area increases have the lowest product prices (down by 12% by 2070, compared to SSP5 without planted forests) and higher global forest products production and consumption quantities (by as much as 3.3% by 2070, compared to SSP5 without planted forests). However, production does not increase in all countries by similar amounts, due to changes in relative advantages in production brought about by reduced product prices.

Climatically controlled reproduction drives interannual growth variability in a temperate tree species.

Climatically controlled allocation to reproduction is a key mechanism by which climate influences tree growth and may explain lagged correlations between climate and growth. We used continent-wide datasets of tree-ring chronologies and annual reproductive effort in Fagus sylvatica from 1901 to 2015 to characterise relationships between climate, reproduction and growth. Results highlight that variable allocation to reproduction is a key factor for growth in this species, and that high reproductive effort ('mast years') is associated with stem growth reduction. Additionally, high reproductive effort is associated with previous summer temperature, creating lagged climate effects on growth. Consequently, understanding growth variability in forest ecosystems requires the incorporation of reproduction, which can be highly variable. Our results suggest that future response of growth dynamics to climate change in this species will be strongly influenced by the response of reproduction.

Drought timing influences the legacy of tree growth recovery.

Whether and how the timing of extreme events affects the direction and magnitude of legacy effects on tree growth is poorly understood. In this study, we use a global database of Ring-Width Index (RWI) from 2,500 sites to examine the impact and legacy effects (the departure of observed RWI from expected RWI) of extreme drought events during 1948-2008, with a particular focus on the influence of drought timing. We assessed the recovery of stem radial growth in the years following severe drought events with separate groupings designed to characterize the timing of the drought. We found that legacies from extreme droughts during the dry season (DS droughts) lasted longer and had larger impacts in each of the 3 years post drought than those from extreme droughts during the wet season (WS droughts). At the global scale, the average integrated legacy from DS droughts (0.18) was about nine times that from WS droughts (0.02). Site-level comparisons also suggest stronger negative impacts or weaker positive impacts of DS droughts on tree growth than WS droughts. Our results, therefore, highlight that the timing of drought is a crucial factor determining drought impacts on tree recovery. Further increases in baseline aridity could therefore exacerbate the impact of punctuated droughts on terrestrial ecosystems.

Warming-induced upward migration of the alpine treeline in the Changbai Mountains, northeast China.

Treeline responses to environmental changes describe an important phenomenon in global change research. Often conflicting results and generally too short observations are, however, still challenging our understanding of climate-induced treeline dynamics. Here, we use a state-of-the-art dendroecological approach to reconstruct long-term changes in the position of the alpine treeline in relation to air temperature at two sides in the Changbai Mountains in northeast China. Over the past 160 years, the treeline increased by around 80 m, a process that can be divided into three phases of different rates and drives. The first phase was mainly influenced by vegetation recovery after an eruption of the Tianchi volcano in 1702. The slowly upward shift in the second phase was consistent with the slowly increasing temperature. The last phase coincided with rapid warming since 1985, and shows with 33 m per 1°C, the most intense upward shift. The spatial distribution and age structure of trees beyond the current treeline confirm the latest, warming-induced upward shift. Our results suggest that the alpine treeline will continue to rise, and that the alpine tundra may disappear if temperatures will increase further. This study not only enhances mechanistic understanding of long-term treeline dynamics, but also highlights the effects of rising temperatures on high-elevation vegetation dynamics.

Forest resilience to drought varies across biomes.

Forecasted increase drought frequency and severity may drive worldwide declines in forest productivity. Species-level responses to a drier world are likely to be influenced by their functional traits. Here, we analyse forest resilience to drought using an extensive network of tree-ring width data and satellite imagery. We compiled proxies of forest growth and productivity (TRWi, absolutely dated ring-width indices; NDVI, Normalized Difference Vegetation Index) for 11 tree species and 502 forests in Spain corresponding to Mediterranean, temperate, and continental biomes. Four different components of forest resilience to drought were calculated based on TRWi and NDVI data before, during, and after four major droughts (1986, 1994-1995, 1999, and 2005), and pointed out that TRWi data were more sensitive metrics of forest resilience to drought than NDVI data. Resilience was related to both drought severity and forest composition. Evergreen gymnosperms dominating semi-arid Mediterranean forests showed the lowest resistance to drought, but higher recovery than deciduous angiosperms dominating humid temperate forests. Moreover, semi-arid gymnosperm forests presented a negative temporal trend in the resistance to drought, but this pattern was absent in continental and temperate forests. Although gymnosperms in dry Mediterranean forests showed a faster recovery after drought, their recovery potential could be constrained if droughts become more frequent. Conversely, angiosperms and gymnosperms inhabiting temperate and continental sites might have problems to recover after more intense droughts since they resist drought but are less able to recover afterwards.

Impacts of management and climate change on nitrate leaching in a forested karst area.

Forest management and climate change, directly or indirectly, affect drinking water resources, both in terms of quality and quantity. In this study in the Northern Limestone Alps in Austria we have chosen model calculations (LandscapeDNDC) in order to resolve the complex long-term interactions of management and climate change and their effect on nitrogen dynamics, and the consequences for nitrate leaching from forest soils into the karst groundwater. Our study highlights the dominant role of forest management in controlling nitrate leaching. Both clear-cut and shelterwood-cut disrupt the nitrogen cycle to an extent that causes peak concentrations and high fluxes into the seepage water. While this effect is well known, our modelling approach has revealed additional positive as well as negative impacts of the expected climatic changes on nitrate leaching. First, we show that peak nitrate concentrations during post-cutting periods were elevated under all climate scenarios. The maximal effects of climatic changes on nitrate concentration peaks were 20-24 mg L(-1) in 2090 with shelterwood or clear-cut management. Second, climate change significantly decreased the cumulative nitrate losses over full forest rotation periods (by 10-20%). The stronger the expected temperature increase and precipitation decrease (in summer), the lesser were the observed nitrate losses. However, mean annual seepage water nitrate concentrations and cumulative nitrate leaching were higher under continuous forest cover management than with shelterwood-cut and clear-cut systems. Watershed management can thus be adapted to climate change by either reducing peak concentrations or long-term loads of nitrate in the karst groundwater.

Determinants of carbon sequestration in thinned forests.

Given global climate change and the projected increases in the greenhouse effect, enhancing the carbon storage capacity of forest ecosystems is especially critical. To fully realize the potential carbon sequestration, it is imperative to understand the drivers affecting carbon storage in forest ecosystems, particularly with disturbances that disrupt existing balance. In this study, we explored the effects of stem-only harvest at various thinning intensities on forest structure and carbon density in middle-aged natural secondary forests, located in the northern temperate zone. Carbon density included aboveground carbon density (ACD), soil organic carbon stocks (SOCD), and total carbon density (TCD), which was the sum of ACD and SOCD. We employed the random forest analysis method to identify significant variables influencing changes in carbon density. Structural equation modelling (SEM) was then used to determine the drivers of changes in forest carbon density. The results showed that moderate thinning (20 %-35 % trees removed), is an effective management practice for increasing the TCD in forests. Although heavy thinning (35.1 %-59.9 % trees removed) accelerated individual growth, it did not fully offset the carbon removed due to thinning. It is noteworthy that light thinning (0-19.9 % trees removed) not only reduced the species richness but also caused a significant number of tree deaths. Large live trees were an important direct determining factor of ACD, but not the only one. In addition, thinning indirectly influenced ACD by reducing canopy density and deformed tree density. The increase in dead tree density had an adverse impact on SOCD, and this phenomenon increased with the passage of recovery time. Conversely, greater thinning intensity enhanced SOCD. Moreover, TCD was directly influenced by tree height, large live trees, and stand density. Furthermore, thinning altered the conifer ratio, thereby influencing tree growth and indirectly controlling the TCD. We believe that this knowledge will be highly beneficial for successful forest management and enhancing the carbon sequestration capacity of forest ecosystems.

Determinants of growth and carbon accumulation of common plantation tree species in the three northern regions, China: Responses to climate and management strategies.

The Three-North (Northwest, North and Northeast) Shelter Forests Program (TNSFP) in China has effectively promoted vegetation growth and carbon sink in the temperate semi-humid and semi-arid regions. To compare the afforestation benefits of commonly used tree species in the area and explore the effect of environment on growth and carbon accumulation in plantations, backpack LiDAR was used to acquire 3 dimensional lidar point clouds of forests from a total of 480 pure plantation patches consisting of Pinus sylvestris (P.s.), Pinus tabuliformis (P.t.), Populus spp. (Pop.), and Robinia pseudoacacia (R.p.). Then, diameter at breast height (DBH), forest height, canopy coverage, and aboveground carbon accumulation were calculated for each plantation patches, which ranged from 7.0 to 37.3 cm, 1.5-14.5 m, 10-99 % and 4.2-205.9 Mg/ha, respectively. Generalized linear mixed-effect models and ANOVA were applied to account for the environmental constraints on the variations of forest parameters. Results showed that precipitation had a stronger effect on all the above parameters of plantations than temperature, and P.t. was more sensitive to climate than other three species. With regard to forest management in Pop. plantations, thinning could improve afforestation efficiency because carbon accumulation would reduce after the age exceeds 30 years. In contrast, P.s. populations maintained a continuous increase in carbon accumulation at least before 40 years old, while the radial growth of canopy became saturated after 12 years of age. The optimal planting density for P.s. and Pop. are about 1000 trees/ha, beyond which the increase in carbon accumulation will slow down or change rate of canopy coverage will be insignificant. Within the TNSFP area, P.t. and R.p. plantations would be more suitable in southern regions, while P.s. and Pop. plantations grow better in the northeastern regions. Meanwhile, mountains along the "Hu Line" showed high potential for growth and carbon accumulation for all tree species examined.

Role of data uncertainty when identifying important areas for biodiversity and carbon in boreal forests.

Forest conservation plays a central role in meeting national and international biodiversity and climate targets. Biodiversity and carbon values within forests are often estimated with models, introducing uncertainty to decision making on which forest stands to protect. Here, we explore how uncertainties in forest variable estimates affect modelled biodiversity and carbon patterns, and how this in turn introduces variability in the selection of new protected areas. We find that both biodiversity and carbon patterns were sensitive to alterations in forest attributes. Uncertainty in features that were rare and/or had dissimilar distributions with other features introduced most variation to conservation plans. The most critical data uncertainty also depended on what fraction of the landscape was being protected. Forests of highest conservation value were more robust to data uncertainties than forests of lesser conservation value. Identifying critical sources of model uncertainty helps to effectively reduce errors in conservation decisions.

Predicting crown width using nonlinear mixed-effects models accounting for competition in multi-species secondary forests.

Crown width (CW) is an important tree variable and is often used as a covariate predictor in forest growth models. The precise measurement and prediction of CW is therefore critical for forest management. In this study, we introduced tree species as a random effect to develop nonlinear mixed-effects CW models for individual trees in multi-species secondary forests, accounting for the effects of competition. We identified a simple power function for the basic CW model. In addition to diameter at breast height (DBH), other significant predictor variables including height to crown base (HCB), tree height (TH), and competition indices (CI) were selected for the mixed-effects CW model. The sum of relative DBH (SRD) was identified the optimal distance-independent CI and as a covariate predictor for spatially non-explicit CW models, whereas the sum of the Hegyi index for fixed number competitors (SHGN) was the optimal distance-dependent CI for spatially explicit CW models, with significant linear correlation (R 2 = 0.943, P < 0.001). Both spatially non-explicit and spatially explicit mixed-effects CW models were developed for studied secondary forests. We found that these models can describe more than 50% of the variation in CW without significant residual trends. Spatially explicit models exhibited a significantly larger effect on CW than spatially non-explicit ones; however, spatially explicit models are computationally complex and difficult and can be replaced by corresponding spatially non-explicit models due to the small differences in the fit statistics. The models we present may be useful for forestry inventory practices and have the potential to aid the evaluation and management of secondary forests in the region.