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Our planet is facing a variety of serious threats from climate change that are unfolding unevenly across the globe. Uncovering the spatial patterns of ecosystem stability is important for predicting the responses of ecological processes and biodiversity patterns to climate change. However, the understanding of the latitudinal pattern of ecosystem stability across scales and of the underlying ecological drivers is still very limited. Accordingly, this study examines the latitudinal patterns of ecosystem stability at the local and regional spatial scale using a natural assembly of forest metacommunities that are distributed over a large temperate forest region, considering a range of potential environmental drivers. We found that the stability of regional communities (regional stability) and asynchronous dynamics among local communities (spatial asynchrony) both decreased with increasing latitude, whereas the stability of local communities (local stability) did not. We tested a series of hypotheses that potentially drive the spatial patterns of ecosystem stability, and found that although the ecological drivers of biodiversity, climatic history, resource conditions, climatic stability, and environmental heterogeneity varied with latitude, latitudinal patterns of ecosystem stability at multiple scales were affected by biodiversity and environmental heterogeneity. In particular, α diversity is positively associated with local stability, while β diversity is positively associated with spatial asynchrony, although both relationships are weak. Our study provides the first evidence that latitudinal patterns of the temporal stability of naturally assembled forest metacommunities across scales are driven by biodiversity and environmental heterogeneity. Our findings suggest that the preservation of plant biodiversity within and between forest communities and the maintenance of heterogeneous landscapes can be crucial to buffer forest ecosystems at higher latitudes from the faster and more intense negative impacts of climate change in the future. [ABSTRACT FROM AUTHOR]

Aim: Understanding the biodiversity–stability relationship has become a central issue in ecology and conservation biology. Although the stabilizing effects of tree species diversity on ecosystem productivity are well recorded in small local communities, they remain poorly understood across scales (from local to larger spatial scales). This study evaluates biodiversity–stability effects from local to larger spatial scales in a large temperate forest region, considering a range of environmental conditions and environmental heterogeneity. Location: North‐eastern China (c. 700,000 km2). Time period: 2005–2017. Major taxa studied: Woody plants. Methods: We define stability as the temporal invariability of biomass productivity. Regional metacommunities representing larger spatial scales were developed by aggregating multiple sets of local field plots. Simple regression analysis was used to test biodiversity–stability relationships in metacommunities. Piecewise structural equation modelling was then used to disentangle the effects of diversity and abiotic variables on forest stability at local and larger spatial scales. Multiple mixed‐effects regression models were used to determine the relative contribution of individual predictive variables to stability across spatial scales. Results: We found that local diversity (alpha diversity) was positively related to the stability of local communities (alpha stability), whereas species turnover across space (beta diversity) was positively related to asynchronous dynamics among local communities (spatial asynchrony), regardless of whether abiotic factors were considered or not. We also found that environmental conditions and environmental heterogeneity affected forest stability across scales. The effect of spatial asynchrony on gamma stability was greater than that of alpha stability. Main conclusions: Our results imply that spatial asynchrony is a key to maintaining the stability of ecosystem productivity within a large temperate forest region. We suggest that diverse forests and heterogeneous landscapes should be sustained at multiple spatial scales to buffer the negative effects of climate change and forest degradation. [ABSTRACT FROM AUTHOR]

Net Primary Productivity (NPP) is one of the significant indicators to measure environmental changes; thus, the relevant study of NPP in Northeast China, Asia, is essential to climate changes and ecological sustainable development. Based on the Global Production Efficiency (GLO-PEM) model, this study firstly estimated the NPP in Northeast China, from 2001 to 2019, and then analyzed its spatio-temporal evolution, future changing trend and phenology regularity. Over the years, the NPP of different forests type in Northeast China showed a gradual increasing trend. Compared with other different time stages, the high-value NPP (700–1300 gC·m−2·a−1) in Changbai Mountain, from 2017 to 2019, is more widely distributed. For instance, the NPP has an increasing rate of 6.92% compared to the stage of 2011–2015. Additionally, there was a significant advance at the start of the vegetation growth season (SOS), and a lag at the end of the vegetation growth season (EOS), from 2001 to 2019. Thus, the whole growth period of forests in Northeast China became prolonged with the change of phenology. Moreover, analysis on the sustainability of NPP in the future indicates that the reverse direction feature of NPP change will be slightly stronger than the co-directional feature, meaning that about 30.68% of the study area will switch from improvement to degradation. To conclude, these above studies could provide an important reference for the sustainable development of forests in Northeast China. [ABSTRACT FROM AUTHOR]

Background: There is a serious lack of experience regarding the productive potential of the natural forests in northeastern China, which severely limits the development of sustainable forest management strategies for this most important forest region in China. Accordingly, the objective of this study is to develop a first comprehensive system for estimating the wood production for the five dominant forest types. Methods: Based on a network of 384 field plots and using the state-space approach, we develop a system of dynamic stand models, for each of the five main forest types. Four models were developed and evaluated, including a base model and three extended models which include the effects of dominant height and climate variables. The four models were fitted, and their predictive strengths were tested, using the "seemingly unrelated regression" (SUR) technique. Results: All three of the extended models increased the accuracy of the predictions at varying degrees for the five major natural forest types of northeastern China. The inclusion of dominant height and two climate factors (precipitation and temperature) in the base model resulted in the best performance for all the forest types. On average, the root mean square values were reduced by 13.0% when compared with the base model. Conclusion: Both dominant height and climate factors were important variables in estimating forest production. This study not only presents a new method for estimating forest production for a large region, but also explains regional differences in the effect of site productivity and climate. [ABSTRACT FROM AUTHOR]