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Purpose of Review Despite the rapidly increasing use of resilience indices to analyze responses of trees and forests to disturbance events, there is so far no common framework to apply and interpret these indices for different purposes. Therefore, this review aims to identify and discuss various shortcomings and pitfalls of commonly used resilience indices and to develop recommendations for a more robust and standardized procedure with a particular emphasis on drought events. Recent Findings Growth-based resilience indices for drought responses of trees are widely used but some important drawbacks and limitations related to their application may lead to spurious results or misinterpretation of observed patterns. The limitations include (a) the inconsistency regarding the selection and characterization of drought events and the climatic conditions in the pre- and post-drought period and (b) the calculation procedure of growth-based resilience indices. Summary We discuss alternative options for metrics, which, when used in concert, can provide a more comprehensive understanding of drought responses in cases where common growth-based resilience indices are likely to fail. In addition, we propose a new analytical framework, the “line of full resilience,” that integrates the three most commonly used resilience indices and show how this framework can be used for comparative drought tolerance assessments such as rankings of different tree species or treatments. The suggested approach could be used to harmonize quantifications of tree growth resilience to drought and it may thus facilitate systematic reviews and development of the urgently needed evidence base to identify suitable management options or tree species and provenances to adapt forests for changing climatic conditions.

Faced with huge environmental problems of ecosystem degradation, “Ecological Redline Policy (ERP)” in China is a new key national-level policy to manage different land use functions in accordance with development and environmental limits. As the water–land complex ecosystem with the largest freshwater lake, wetland natural reserves and ecological importance in China, Poyang Lake Region (PLR) is selected to quantify and map multiple ecosystem services, investigate the ecological function zoning as part of research on ecological zoning control and major ecological source areas to illustrate and address the implementation of this strategy based on the importance and vulnerability analysis of ecosystem services. According to ecological function zoning results, extremely important, highly important, medium important and important zones respectively account for 26.1%, 28.1%, 17.4% and 28.4% of the total area. With an area of 5422.2 km 2 , the extremely important zone is 1010.6 km 2 larger than the ERP. Moreover, 81.6% of the ERP is located in the extremely important zone. By discussing the implications and applications of ecological management, this study contributes to the ecological protection of Poyang Lake and provides a foundation for research on ecological function zoning at the regional scale.

The drivers underlying the development of deep root systems, whether genetic or environmental, are poorly understood but evidence has accumulated that deep rooting could be a more widespread and important trait among plants than commonly anticipated from their share of root biomass. Even though a distinct classification of \"deep roots\" is missing to date, deep roots provide important functions for individual plants such as nutrient and water uptake but can also shape plant communities by hydraulic lift (HL). Subterranean fauna and microbial communities are highly influenced by resources provided in the deep rhizosphere and deep roots can influence soil pedogenesis and carbon storage.Despite recent technological advances, the study of deep roots and their rhizosphere remains inherently time-consuming, technically demanding and costly, which explains why deep roots have yet to be given the attention they deserve. While state-of-the-art technologies are promising for laboratory studies involving relatively small soil volumes, they remain of limited use for the in situ observation of deep roots. Thus, basic techniques such as destructive sampling or observations at transparent interfaces with the soil (e.g., root windows) which have been known and used for decades to observe roots near the soil surface, must be adapted to the specific requirements of deep root observation. In this review, we successively address major physical, biogeochemical and ecological functions of deep roots to emphasize the significance of deep roots and to illustrate the yet limited knowledge. In the second part we describe the main methodological options to observe and measure deep roots, providing researchers interested in the field of deep root/rhizosphere studies with a comprehensive overview. Addressed methodologies are: excavations, trenches and soil coring approaches, minirhizotrons (MR), access shafts, caves and mines, and indirect approaches such as tracer-based techniques.

As a critical component of the geographical divide between the northern and southern regions of China, the ecological stability of the Qinling region has profound implications for ecological balance within China and across East Asia. However, the degradation risk areas of the Qinling region remain unclear, and there are gaps in the delineation of key ecological protection areas. This study examines the improvement and decline in the Qinling region from 2000 to 2023 in terms of ecosystem patterns, quality, and functions. Moreover, key ecological function and degradation risk zones were identified, and future development paths were proposed for the Qinling region. The findings indicate that: (1) Urban area expansion was the most rapid, increasing by about 1800 km², with an average yearly growth rate of 2.43%. Ecosystem quality increased in 48.07% of the Qinling region. The degradation risk zones of ecosystem quality and function were primarily located in the Sanjiangyuan, the Minshan-Qinghai-Tibet Plateau, and the Loess Plateau in Shaanxi, Henan, and Gansu. The core areas for water and soil conservation only accounted for 17.92% and 10.47%, respectively, mainly distributed across the Qinling-Daba Mountains. Based on ecological patterns, quality, functions, and ecological protection and restoration projects, the Qinling region has been divided into two majority categories and 16 subcategories: 7 ecologically key functional areas and 9 degradation risk areas. This study offers recommendations for formulating ecological protection and restoration policies, thereby promoting the sustainable development of the region’s ecology and economy.

Purpose of Review International ambitions for massive afforestation and restoration are high. To make these investments sustainable and resilient under future climate change, science is calling for a shift from planting monocultures to mixed forests. But what is the scientific basis for promoting diverse plantations, and what is the feasibility of their establishment and management? As the largest global network of tree diversity experiments, TreeDivNet is uniquely positioned to answer these pressing questions. Building on 428 peer-reviewed TreeDivNet studies, combined with the results of a questionnaire completed by managers of 32 TreeDivNet sites, we aimed to answer the following questions: (i) How and where have TreeDivNet experiments enabled the relationship between tree diversity and tree performance (including productivity, survival, and pathogen damage) to be studied, and what has been learned? (ii) What are the remaining key knowledge gaps in our understanding of the relationship between tree diversity and tree performance? and (iii) What practical insights can be gained from the TreeDivNet experiments for operational, real-world forest plantations? Recent Findings We developed a conceptual framework that identifies the variety of pathways through which target tree performance is related to local neighbourhood diversity and mapped the research efforts for each of those pathways. Experimental research on forest mixtures has focused primarily on direct tree diversity effects on productivity, with generally positive effects of species and functional diversity on productivity. Fewer studies focused on indirect effects mediated via biotic growing conditions (e.g. soil microbes and herbivores) and resource availability and uptake. Most studies examining light uptake found positive effects of species diversity. For pests and diseases, the evidence points mostly towards lower levels of infection for target trees when growing in mixed plantations. Tree diversity effects on the abiotic growing conditions (e.g. microclimate, soil properties) and resource-use efficiency have been less well studied to date. The majority of tree diversity experiments are situated in temperate forests, while (sub)tropical forests, and boreal forests in particular, remain underrepresented. Summary TreeDivNet provides evidence in favour of mixing tree species to increase tree productivity while identifying a variety of different processes that drive these diversity effects. The design, scale, age, and management of TreeDivNet experiments reflect their focus on fundamental research questions pertaining to tree diversity-ecosystem function relationships and this scientific focus complicates translation of findings into direct practical management guidelines. Future research could focus on (i) filling the knowledge gaps related to underlying processes of tree diversity effects to better design plantation schemes, (ii) identifying optimal species mixtures, and (iii) developing practical approaches to make experimental mixed plantings more management oriented.

Through complementarity interactions, mixed-species forests can be more productive than monocultures, and this effect can increase with tree-species richness. However, this is not always the case. This review examines the processes and stand structural attributes that can influence diversity-productivity relationships (DPRs); how they influence resource availability, resource uptake, and resource-use efficiency; and also describes some important differences between tree-diversity versus grassland-diversity experiments. The size of the complementarity effects caused by these processes and stand structures varies along spatial and temporal gradients in resource availability and climate. These spatial and temporal dynamics have now been examined in many studies, and the general patterns are summarized using a simple framework; complementarity is predicted to increase as the availability of resource “ X ” declines (or climatic condition X becomes harsher) if the species interactions improve the availability, uptake, or use efficiency of resource X (or interactions improve climatic condition X ). Importantly, this framework differs from the stress-gradient hypothesis to account for a wider range of inter-specific plant interactions (not only facilitation) by considering contrasting methods used to quantify species interactions while accounting for stand structure. In addition, complementarity (as opposed to facilitation) for a given species combination can increase as growing conditions improve in forests, contrary to predictions of the stress-gradient hypothesis with regards to facilitation. This review indicates that while the effects of tree-species diversity on growth and other forest functions are now receiving a lot of attention, far less is known about the effects of structural diversity on growth or forest functioning. Direct measurements of the processes, as opposed to focusing mainly on growth responses, could greatly contribute to our understanding of structural diversity effects.

Microbial ecological functions are an emergent property of community composition. For some ecological functions, this link is strong enough that community composition can be used to estimate the quantity of an ecological function. Here, we apply random forest regression models to compare the predictive performance of community composition and environmental data for bacterial production (BP). Using data from two independent long-term ecological research sites—Palmer LTER in Antarctica and Station SPOT in California—we found that community composition was a strong predictor of BP. The top performing model achieved an R2 of 0.84 and RMSE of 20.2 pmol L−1 hr−1 on independent validation data, outperforming a model based solely on environmental data (R2 = 0.32, RMSE = 51.4 pmol L−1 hr−1). We then operationalized our top performing model, estimating BP for 346 Antarctic samples from 2015 to 2020 for which only community composition data were available. Our predictions resolved spatial trends in BP with significance in the Antarctic (P value = 1 × 10−4) and highlighted important taxa for BP across ocean basins. Our results demonstrate a strong link between microbial community composition and microbial ecosystem function and begin to leverage long-term datasets to construct models of BP based on microbial community composition.

Forest ecological function is one of the key indicators reflecting the quality of forest resources. The traditional weighting method to assess forest ecological function is based on a large amount of ground survey data; it is accurate but costly and time-consuming. This study utilized three machine learning algorithms to estimate forest ecological function levels based on multi-source data, including Sentinel-2 optical remote sensing images and digital elevation model (DEM) and forest resource planning and design survey data. The experimental results showed that Random Forest (RF) was the optimal model, with overall accuracy of 0.82, recall of 0.66, and F1 of 0.62, followed by CatBoost (overall accuracy = 0.82, recall = 0.62, F1 = 0.58) and LightGBM (overall accuracy = 0.76, recall = 0.61, F1 = 0.58). Except for the indicators from remote sensing images and DEM data, the five ground survey indicators of forest origin (QI_YUAN), tree age group (LING_ZU), forest category (LIN_ZHONG), dominant species (YOU_SHI_SZ), and tree age (NL) were used in the modeling and prediction. Compared to the traditional methods, the proposed algorithm has lower cost and stronger timeliness.

Revealing the dependence and uniqueness of microbial communities on microplastics could help us better understand the assembly of the microplastic microbial community in river ecosystems. In this study, we investigated the composition and ecological functions of the bacterial community on microplastics from the Three Gorges Reservoir area compared with those in water, sediment, and soil at species-level via full-length 16S rRNA gene sequencing. The results showed that the full-length 16S rRNA sequencing provided more detail and accurate taxa resolution of the bacterial community in microplastics (100%), water (99.90%), sediment (99.95%), and soil (100%). Betaproteobacteriales were the most abundant bacteria in microplastics (14.1%), water (32.3%), sediments (27.2%), and soil (21.0%). Unexpectedly, oligotrophic SAR11 clade was the third abundant bacteria (8.51%) and dominated the ecological functions of the bacterial community in water, but it was less observed on microplastics, with a relative abundance of 2.73×10-5. However, four opportunistic pathogens identified at the species level were selectively enriched on microplastics. Stenotrophomonas maltophilia was the main opportunistic pathogen on microplastics (0.29%). Sediment rather than soil and water may be contributed mostly to pathogens on microplastics. Moreover, some bacteria species with the biodegradation function of microplastics were enriched on microplastics, such as bacteria Rhodobacter sp., and endemic bacteria Luteimonas sp. The distinct bacteria composition on microplastics enhanced several ecological functions, such as xenobiotics biodegradation, which allows screening the bacteria with the biodegradation function of microplastics through long-term exposure.

Saline lakes, characterized by high salinity and limited nutrient availability, provide an ideal environment for studying extreme halophiles and their biogeochemical processes. The present study examined prokaryotic microbial communities and their ecological functions in lentic sediments (with the salinity gradient and time series) using 16S rRNA amplicon sequencing and a metagenomic approach. Our findings revealed a negative correlation between microbial diversity and salinity. The notable predominance of Archaea in high-salinity lakes signified a considerable alteration in the composition of the microbial community. The results indicate that elevated salinity promotes homogeneous selection pressures, causing substantial alterations in microbial diversity and community structure, and simultaneously hindering interactions among microorganisms. This results in a notable decrease in the complexity of microbial ecological networks, ultimately influencing the overall ecological functional responses of microbial communities such as carbon fixation, sulfur, and nitrogen metabolism. Overall, our findings reveal salinity drives a notable predominance of Archaea , selects for species adapted to extreme conditions, and decreases microbial community complexity within saline lake ecosystems.