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The relationships between biodiversity and community stability have been well-documented in grassland ecosystems, yet the diversity–stability relationship and the mechanisms driving community stability in forests remain poorly understood. In this study, we examined the community stability of a tropical montane forest in China over 10 years to explore the effect of multiple facets of biodiversity (that is, taxonomic, functional, and structural diversity). We further tested the relative importance of biodiversity, functional traits, species asynchrony, species stability, and abiotic factors (that is, soil nutrients) on community stability. We found that multiple facets of biodiversity had inconsistent effects on stability, including a neutral effect of species richness, and weak positive effects of functional diversity and structural diversity. Species asynchrony, rather than biodiversity, was the greatest predictor of community stability, followed by the stability of large trees. Consistent with the mass-ratio hypothesis, the stability of dominant species also had an important direct effect on community stability. Although functional trait composition had no direct effect on stability, it regulated stability via species asynchrony, large tree stability, and dominant species stability. Similarly, soil nutrients conferred minor effects on community stability. Our results indicated that the insurance effect is the main mechanism driving community stability in this subtropical forest. Meanwhile, the mass-ratio hypothesis also played an important role, which suggested that the management and protection of forest ecosystem should not only focus on biodiversity but also the community structural attributes.

Understanding the factors that regulate the functioning of our ecosystems in response to environmental changes can help to maintain the stable provisioning of ecosystem services to mankind. This is especially relevant given the increased variability of environmental conditions due to human activities. In particular, maintaining a stable production and plant biomass during the growing season (intra‐annual stability) despite pervasive and directional changes in temperature and precipitation through time can help to secure food supply to wild animals, livestock, and humans. Here, we conducted a 29‐year field observational study in a temperate grassland to explore how the intra‐annual stability of primary productivity is influenced by biotic and abiotic variables through time. We found that intra‐annual precipitation variability in the growing season indirectly influenced the community intra‐annual biomass stability by its negative effect on intra‐annual species asynchrony. While the intra‐annual temperature variability in the growing season indirectly altered community intra‐annual biomass stability through affecting the intra‐annual species richness. At the same time, although the intra‐annual biomass stability of the dominant species and the dominant functional group were insensitive to climate variability, they also promoted the stable community biomass to a certain extent. Our results indicate that ongoing intra‐annual climate variability affects community intra‐annual biomass stability in the temperate grassland, which has important theoretical significance for us to take active measures to deal with climate change. Intra‐annual growing season precipitation variability and temperature variability indirectly influenced the community intra‐annual biomass stability by negative effect on intra‐annual species asynchrony and intra‐annual species richness, respectively. ; Intra‐annual biomass stability of dominant species and dominant functional group were insensitive to climate variability, they also promoted the stable community biomass to a certain extent.

Understanding the biotic mechanisms of community stability in variable environments has been a focal point of fundamental ecological research. A multitude of mechanisms, encompassing compensatory dynamics arising from negative species covariance, portfolio effect linked to species richness and evenness, and dominant species stability, have been found to collectively enhance community stability. However, it is not clear how their stabilizing effects change and contribute to the maintenance of community stability along environmental gradients. We performed a ten-year investigation in a large shallow lake with a eutrophic gradient across space. With the dataset, we quantified the role of the three stability mechanisms, and their changes in effect size along the eutrophic gradient to determine their relative importance in biomass stability. Our results showed that the biomass stability shifted from one stable state at eutrophic sites to another stable state at hypertrophic sites, and biomass stability was positively correlated with composition stability. In the relatively stable state, biomass stability exhibited a closely synchronized variation along with compositional stability in response to environmental changes. Conversely, in the unstable state, biomass stability displayed weaker sensitivity to environmental changes compared to compositional stability. The effect sizes of different biotic mechanisms of biomass stability varied across the eutrophic gradient. Compensatory dynamics emerged as the primary force governing biomass stability in eutrophic waters, overshadowing the relatively weak impact of the portfolio effect, which might help resist the shift from turbid state to clear state with decreasing nutrient concentrations. However, as nutrient levels increased, the primary force shifted from compensatory dynamics toward the dominant species stability. This study improves our understanding for the biotic mechanisms of phytoplankton community responding to nutrients mitigation in eutrophic waters, which might be one of the most important ecological components for managing communities to maintain ecosystem functioning.

Most studies on the impacts of plant invasions focus on species richness or diversity of invaded communities, but much less attention has been paid to structural changes such as the representation of species with different traits. To bridge this knowledge gap, we assess the impact of dominant species on the trait composition of recipient communities (i.e., how species with certain height, seed mass, specific leaf area, clonality, and life form are represented in the vegetation plots sampled). We sampled vegetation that comprised three species native to Eurasia and invasive in North America (i.e., Agrostis capillaris, Bromus tectorum, and Cirsium arvense) and three species native to North America and invasive in Europe (i.e., Aster novi‐belgii, Lupinus polyphyllus, and Solidago canadensis), in both their native and invaded ranges. This study system based on reciprocal inter‐continental invasions allowed us to assess whether the impact on trait composition differed (1) between the native and invaded ranges and (2) between the two continents. The relationships between species’ dominance and trait composition were tested using linear mixed‐effect models and ordination methods. A general trend was that dominant species with an impact on species richness also had an impact on trait composition, especially in North America, where even the native dominants affected the trait composition of the community. Further, the impact of Eurasian dominants in North America was stronger than that associated with the opposite direction of invasion, due to a strong negative effect of Eurasian invaders on local tall clonal perennials. Our results show that (1) the traits of species in the invaded community co‐determine the impact of invasion and are related to the impacts on species richness and composition; (2) the impacts on trait composition differ between the native and invaded ranges; and (3) the direction of invasion affects the impact on trait composition.

Cliffs are unique ecosystems with an outstanding but relatively unknown plant diversity, harboring rare, endemic and threatened species, but also rock-specialist or generalist species that can become locally common and dominant on cliffs. The rising popularity of climbing represents an increasing threat to cliff biota, affecting community composition and potentially diminishing diversity and species associations. We used a novel sampling design of closely-paired climbed versus unclimbed points along the cliff-face. We sampled along climbing routes of different climbing intensities in El Potrero Chico (Nuevo León, Mexico), identifying plant species and analyzing species associations and community composition in climbed and unclimbed plots. Diversity on the sampled cliffs was high, even greater than in other regional ecosystems. We found reduced abundance, cover, and diversity in climbed plots, irrespective of climbing intensity. Dominant species on the sampled cliffs were the most negatively affected by rock climbing in terms of abundance, and some locally rare species, including endemics and endangered species, were entirely absent from climbed plots. Co-occurrence analysis showed that the number of associations between pairs of dominant and common species were greatly reduced in climbed plots, and that positive associations between locally rare species existed in unclimbed plots but not in climbed plots, which may contribute to the disappearance of endemic and threatened species. Finally, NMDS analysis revealed that the community composition changed significantly due to climbing. Our results indicate that conservation science should convince stakeholders of the need for a holistic conservation of cliff ecosystems and not focus solely on emblematic or rare species, since plant community dynamics and preservation depend on interactions between plant species.

Studying the patterns of changes in species diversity and soil properties can improve our knowledge of community succession. However, there is still a gap in understanding how soil conditions are related to plant diversity in tropical coastal secondary forests. We sampled plant diversity and soil nutrients spanning two different years (2012 and 2019) to assess the patterns of species diversity and relationships of soil nutrients and species diversity on Hainan Island, southern China. Results showed that the soil pH and total nitrogen (TN) significantly decreased while the soil organic matter (OM) and total phosphorus (TP) significantly increased from 2012 to 2019. Plant species diversity was significantly higher in 2012 than in 2019, and the dominant species significantly changed in two different years. Using multiple regression analysis, we determined that soil TP and TN were significantly related to plant diversity in 2012 and 2019, respectively. Using CCA analysis, TN and OM were the strongest predictors for dominant species in 2012, whereas the soil TP and TN were the strongest predictors for dominant species in 2019. Our findings show a significant change in plant diversity and dominant species after 7 years of development in the tropical coastal secondary forest. The patterns of plant diversity and soil nutrients increase our knowledge of forest restoration in coastal areas.

Causal effects of biodiversity on ecosystem functions can be estimated using experimental or observational designs — designs that pose a tradeoff between drawing credible causal inferences from correlations and drawing generalizable inferences. Here, we develop a design that reduces this tradeoff and revisits the question of how plant species diversity affects productivity. Our design leverages longitudinal data from 43 grasslands in 11 countries and approaches borrowed from fields outside of ecology to draw causal inferences from observational data. Contrary to many prior studies, we estimate that increases in plot-level species richness caused productivity to decline: a 10% increase in richness decreased productivity by 2.4%, 95% CI [−4.1, −0.74]. This contradiction stems from two sources. First, prior observational studies incompletely control for confounding factors. Second, most experiments plant fewer rare and non-native species than exist in nature. Although increases in native, dominant species increased productivity, increases in rare and non-native species decreased productivity, making the average effect negative in our study. By reducing the tradeoff between experimental and observational designs, our study demonstrates how observational studies can complement prior ecological experiments and inform future ones. Isolating the relationships between biodiversity and ecosystem functioning in natural ecosystems is challenging. Here, the authors apply a causal inference approach to observational data from grasslands and find a negative effect of biodiversity on productivity driven by non-native and rare species.

Silicon (Si) and calcium (Ca), as elements abundant in the Earth’s crust, are closely related to plant growth and stress resistance and have similar roles. Understanding the stoichiometry of Si and Ca can provide more insight into the mechanical and stress resistance of plants, as well as their preferences for the absorption and use of Si and Ca. Here, we measured the content of Si and Ca in the leaves of the dominant tree species located in the Mount Wuyi National Park, with an elevation ranging from 800 m to 1700 m, in an attempt to reveal changes in the Si and Ca content and ratio in the leaves along the altitude, as well as their possible relationships with environmental factors and phylogeny. The results indicated that the leaf Si and the leaf Si/Ca decreased, while the leaf Ca increased significantly with the increase in elevation. Changes in environmental factors induced by variations in elevation affected the silicon and calcium stoichiometry characteristics of the leaves, either directly or indirectly. Specifically, the mean annual precipitation, soil available silicon, soil organic matter, and soil bulk density accounted for most of the variations in leaf silicon and calcium. The leaf silicon and calcium stoichiometry was phylogenetically conservative, suggesting more similar characteristics among closely related tree species. Structural equation modeling and variation partitioning indicated that phylogeny might be more important than environmental factors in influencing leaf Si and Ca stoichiometry. Additionally, the shared effects of environmental factors and taxonomic levels indicated changes in the forest community, and the differential responses of different functional types due to elevation variation also affected the altitudinal patterns of leaf Si and Ca stoichiometry.

The pattern of a few abundant species and many rarer species is a defining characteristic of communities worldwide. These abundant species are often referred to as dominant species. Yet, despite their importance, the term dominant species is poorly defined and often used to convey different information by different authors. Based on a review of historical and contemporary definitions we develop a synthetic definition of dominant species. This definition incorporates the relative local abundance of a species, its ubiquity across the landscape, and its impact on community and ecosystem properties. A meta-analysis of removal studies shows that the loss of species identified as dominant by authors can significantly impact ecosystem functioning and community structure. We recommend two metrics that can be used jointly to identify dominant species in a given community and provide a roadmap for future avenues of research on dominant species. In our review, we make the case that the identity and effects of dominant species on their environments are key to linking patterns of diversity to ecosystem function, including predicting impacts of species loss and other aspects of global change on ecosystems.

The distribution of forest-dominant tree species is crucial for ecosystem assessment. Remote sensing monitoring requires annual ground sample data, but consistent field surveys are challenging. This study addresses this by combining sample migration learning and machine learning for multi-year tree species classification in the Three Gorges Reservoir area in China. Using the continuous change detection and classification (CCDC) algorithm, sample data from 2023 were successfully migrated to 2018–2022, achieving high migration accuracy (R2 = 0.8303, RMSE = 4.64). Based on migrated samples, random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB) algorithms classified forest tree species with overall accuracies above 70% and Kappa coefficients above 0.6. XGB. They outperformed other algorithms, with classification accuracy of over 80% and Kappa above 0.75 in almost all years. The final map indicates stable distribution from 2018 to 2023, with eucalyptus covering over 40% of the forest area, followed by horsetail pine, fir, cypress, and wetland pine.