Forest Ecosystems最新文献

Background

No studies have documented long-term trends in aboveground biomass (AGB) for mixed-dipterocarp forests (MDF), the dominant rain forest type in tropical wet equatorial Asia. In our study, we sought to document such trends over forty years across three sites representing lowland to lower montane elevations.

Methods

To do this, we established fifty 100 ​m ​× ​25 ​m plots in 1978 across three sites sampled along an elevation gradient, identified as mature old-growth forest. We measured trees for diameter at breast height that we identified to species and tagged. We took wood samples to calculate species wood-specific gravity. We re-measured plots in 1998 and again in 2018.

Results

We show standing AGB for all sites combined to be 517.52 ​Mg⋅ha⁻¹ in 1978, but this declined by 17% over 40 years to 430.11 ​Mg⋅ha⁻¹. No differences exist among sites in AGB primarily because of considerable within site variation; but interactions of time with site show declines across sites were not uniform, one remained about the same. Relatively few species represented a high proportion of the AGB with the top five species comprising between 34% and 65%, depending upon site and year sampled. One species, Mesua nagassarium, represented a disproportionately large amount of AGB and decline over time, particularly at the low elevation site.

Conclusions

Our results are directly relevant to estimating AGB and standing carbon sequestered in MDF. Our study is the first to demonstrate varying but overall, declining trends in amounts of AGB among forests making predictions of biomass and standing carbon in MDF difficult over wide regions.

Remotely sensed data are frequently used for predicting and mapping ecosystem characteristics, and spatially explicit wall-to-wall information is sometimes proposed as the best possible source of information for decision-making. However, wall-to-wall information typically relies on model-based prediction, and several features of model-based prediction should be understood before extensively relying on this type of information. One such feature is that model-based predictors can be considered both unbiased and biased at the same time, which has important implications in several areas of application. In this discussion paper, we first describe the conventional model-unbiasedness paradigm that underpins most prediction techniques using remotely sensed (or other) auxiliary data. From this point of view, model-based predictors are typically unbiased. Secondly, we show that for specific domains, identified based on their true values, the same model-based predictors can be considered biased, and sometimes severely so.

We suggest distinguishing between conventional model-bias, defined in the statistical literature as the difference between the expected value of a predictor and the expected value of the quantity being predicted, and design-bias of model-based estimators, defined as the difference between the expected value of a model-based estimator and the true value of the quantity being predicted. We show that model-based estimators (or predictors) are typically design-biased, and that there is a trend in the design-bias from overestimating small true values to underestimating large true values. Further, we give examples of applications where this is important to acknowledge and to potentially make adjustments to correct for the design-bias trend. We argue that relying entirely on conventional model-unbiasedness may lead to mistakes in several areas of application that use predictions from remotely sensed data.

REDD+¹ is an economic incentivizing mechanism aimed at reducing or offsetting of carbon emissions in forests, while realizing multiple benefits alongside climate action. Engaging local stakeholders is crucial for its sustainable implementation and benefit-sharing mechanism. This study focuses on the knowledge and understanding of local-level stakeholders about REDD+ and its associated attributes, revealing significant knowledge gaps between areas with and without REDD+ ​pilot activities. For this, we conducted the semi-structured questionnaire interviews (n ​= ​136), key informant interviews (n ​= ​27), and focus group discussions (n ​= ​4) with local-level REDD+ ​stakeholders (LLRS) comprising both inside and outside of pilot project districts in three provinces of Nepal, by adopting the concept of socio-ecological systems (SES). Data were analyzed using a generalized linear model (GLM) and visualized through Sankey diagrams. The results indicate a poor understanding (29%) of LLRS on the REDD+ ​process, its relationship with forests, concerns among stakeholders, and its potential significance. The perception of REDD+ ​knowledge, mechanisms, and benefits significantly (p ​< ​0.05) varied across study areas, age groups, genders, professional backgrounds, educational levels, ownership of private forests, and types of household energy sources used among respondents. Despite receiving readiness funds, stakeholders’ comprehension of the REDD+ ​process remains limited, indicating suboptimal policy implementation. Knowledge gaps were influenced by social background, voices and choices, and the fear of REDD+ ​disrupting traditional practices among the LLRS. The study emphasizes the need to redress the concerns of LLRS by considering their social backgrounds and traditional practices through informed and participatory decision-making, enhance communication, transparency, and inclusive forest governance. The findings show that current external support has not sufficiently enhanced capacity among LLRS, suggesting the need for sufficient and sustainable support through national policy and financing mechanisms. Further, the study identified extremely poor REDD+ ​-related knowledge dissemination within communities, exacerbating challenges in implementation and benefit-sharing mechanisms, revealing the simplification of its process is essential. The study advocates for revising REDD+ ​-related policies to optimize benefits, ensure smooth implementation, realize fair and equitable carbon credits from forests, and foster shared responsibility and ownership among all stakeholders in climate actions through improved forest governance.

The interspecific variations of plant functional traits can characterize the niche positions of species within communities, while the intraspecific variations can accurately display the species’ niche breadth. Revealing relative contributions of intra- and interspecific variations to plant functional community structure is crucial in understanding how the species coexist together, especially in species-diverse ecosystems. To explore how the intra- and interspecific variations of plant functional traits change along the successional pathway in heterogeneous conditions, we established a series of plots and measured main plant functional traits along the natural regeneration in karst forest ecosystems. By quantifying the intra- and interspecific variations of plant functional traits, we found that the changes in intraspecific variations were relatively lower compared to changes in interspecific variations throughout the natural regeneration. Further analysis showed that the community spatial structure contributed more to the intraspecific variations of plant functional traits, while the soil physicochemical properties contributed more to interspecific variations. Our study suggested that tree species might tend to narrow their niche and change the positions to release the niche overlap when faced with heterogeneous habitat conditions.

The structure of plant communities at local scales depends on both the spatial heterogeneity of abiotic environmental factors and the biotic interactions within the community. However, although environmental filtering due to microtopographic heterogeneity and resource competition among plants caused by spatial variation in tree density and size are considered to be very important in explaining the mechanisms of community assembly, their effects on the processes of individual mortality and recruitment in natural forest regeneration, as well as their relative contributions, are still poorly understood. To address this, we established a 12-ha permanent plot in a subtropical evergreen broad-leaved forest area and measured microtopographic variables such as elevation, slope, aspect, and terrain position index (TPI) using a total station. We monitored the individual mortality and recruitment in forest natural regeneration through repeated surveys at 5-year intervals. We fitted spatial covariance models to jointly use multiple factors from three groups of variables (microtopographic effect, neighborhood density effects, neighborhood size effects) as explanatory variables to analyze their roles in driving the mortality and recruitment of all individual and 12 dominant species in forest natural regeneration at the neighborhood scale. Our results show that: (1) In the crucial early stages of secondary forest restoration, natural regeneration is influenced by a synergy of environmental filtering, due to microtopographic heterogeneity, and resource competition among plants. (2) For distinct species responses, evergreen dominant species’ mortality is largely explained by neighborhood effects, while deciduous species are more affected by topographic factors. Furthermore, the adverse effects of larger conspecific trees on younger trees indicate a pattern of competitive pressure leading to mortality among regenerating trees, such pattern emphasis the influence of parent trees on natural regeneration. (3) As trees grow, their interaction with these stressors evolves, suggesting a shift in their resource acquisition strategies and response to neighborhood effects and environmental factors. Despite these changes, the relative importance of topographic factors in determining survival and recruitment success remains constant. This research highlights the importance of considering both environmental and neighborhood effects in forest management, particularly in early secondary forest restoration.

Understanding understory seedling regeneration mechanisms is important for the sustainable development of temperate primary forests in the context of increasingly intense climate warming events. The poor regeneration of dominant tree species, however, is one of the biggest challenges it faces at the moment. Especially, the regeneration of the shade-intolerant Quercus mongolica seedling is difficult in primary forests, which contrasts with the extreme abundance of understory seedlings in secondary forests. The mechanism behind the interesting phenomenon is still unknown. This study used in-situ monitoring and nursery-controlled experiment to investigate the survival rate, growth performance, as well as nonstructural carbohydrate (NSC) concentrations and pools of various organ tissues of seedlings for two consecutive years, further analyze the understory light availability and simulate the foliage carbon (C) gain in the secondary and primary forest. Results suggested that seedlings in the secondary forest had greater biomass allocation aboveground, height and specific leaf area (SLA) in summer, which allowed the seedling to survive longer in the canopy closure period. High light availability and positive C gain in early spring and late autumn are key factors affecting the growth and survival of understory seedlings in the secondary forest, whereas seedlings in the primary forest had annual negative carbon gain. Through the growing season, the total NSC concentrations of seedlings gradually decreased, whereas those of seedlings in the secondary forest increased significantly in autumn, and were mainly stored in roots for winter consumption and the following year's summer shade period, which was verified by the nursery-controlled experiment that simulated autumn enhanced light availability improved seedling survival rate and NSC pools. In conclusion, our results revealed the survival trade-off strategies of Quercus mongolica seedlings and highlighted the necessity of high light availability during the spring and autumn phenological periods for shade-intolerant tree seedling recruitment.

Pinus sylvestris var. mongolica (P. sylvestris) plantations are extensively established in the boreal zone. Increasing stand biomass of these plantations can effectively enhance carbon stock, which is crucial for mitigating climate change. However, the current understanding of optimizing plantation strategies to maximize stand biomass is primarily derived from experiments in tropical and subtropical zones, which is difficult to extend to the boreal due to substantial climatic differences. Based on a comprehensive dataset from 1,076 sample plots of P. sylvestris plantations in the boreal zone of China, we evaluated the effects of tree species richness and stand density on tree height, diameter at breast height (DBH), and stand biomass to investigate the optimal plantation strategy. Furthermore, we examined how these effects changed with stand age and investigated their relative importance. We found that monocultures at a high stand density of 2,000–2,500 ​ha⁻¹ were the optimal plantation strategy to maximize stand biomass (107.5 ​Mg·ha⁻¹), and this held true at almost all stand ages. Unfortunately, this strategy resulted in low species richness and small individual trees (10.6 ​m height and 9.8 ​cm DBH), thus presenting a trade-off. In addition, as stand age increased, the effect of tree species richness on stand biomass shifted from positive to negative, but the effect of stand density was always positive. Overall, stand age had the greatest effect on stand biomass, followed by stand density and then tree species richness. Our findings reveal a distinct plantation strategy for optimizing stand biomass of P. sylvestris plantations in the boreal zone. More importantly, this study highlights that (1) maximizing stand biomass in the boreal zone may compromise tree species richness; (2) net effects of tree species richness on stand biomass are not always positive, as negative selection effects offset positive complementary effects.

The inflection point is an important feature of sigmoidal height-diameter (H-D) models. It is often cited as one of the properties favoring sigmoidal model forms. However, there are very few studies analyzing the inflection points of H-D models. The goals of this study were to theoretically and empirically examine the behaviors of inflection points of six common H-D models with a regional dataset. The six models were the Wykoff (WYK), Schumacher (SCH), Curtis (CUR), Hossfeld IV (HOS), von Bertalanffy-Richards (VBR), and Gompertz (GPZ) models. The models were first fitted in their base forms with tree species as random effects and were then expanded to include functional traits and spatial distribution. The distributions of the estimated inflection points were similar between the two-parameter models WYK, SCH, and CUR, but were different between the three-parameter models HOS, VBR, and GPZ. GPZ produced some of the largest inflection points. HOS and VBR produced concave H-D curves without inflection points for 12.7% and 39.7% of the tree species. Evergreen species or decreasing shade tolerance resulted in larger inflection points. The trends in the estimated inflection points of HOS and VBR were entirely opposite across the landscape. Furthermore, HOS could produce concave H-D curves for portions of the landscape. Based on the studied behaviors, the choice between two-parameter models may not matter. We recommend comparing several three-parameter model forms for consistency in estimated inflection points before deciding on one. Believing sigmoidal models to have inflection points does not necessarily mean that they will produce fitted curves with one. Our study highlights the need to integrate analysis of inflection points into modeling H-D relationships.