Disturbance history, neighborhood crowding and soil conditions jointly shape tree growth in temperate forests

Understanding how different mechanisms act and interact in shaping communities and ecosystems is essential to better predict their future with global change. Disturbance legacy, abiotic conditions, and biotic interactions can simultaneously influence tree growth, but it remains unclear what are their relative contributions and whether they have additive or interactive effects. We examined the separate and joint effects of disturbance intensity, soil conditions, and neighborhood crowding on tree growth in 10 temperate forests in northeast China. We found that disturbance was the strongest driver of tree growth, followed by neighbors and soil. Specifically, trees grew slower with decreasing initial disturbance intensity, but with increasing neighborhood crowding, soil pH and soil total phosphorus. Interestingly, the decrease in tree growth with increasing soil pH and soil phosphorus was steeper with high initial disturbance intensity. Testing the role of species traits, we showed that fast-growing species exhibited greater maximum tree size, but lower wood density and specific leaf area. Species with lower wood density grew faster with increasing initial disturbance intensity, while species with higher specific leaf area suffered less from neighbors in areas with high initial disturbance intensity. Our study suggests that accounting for both individual and interactive effects of multiple drivers is crucial to better predict forest dynamics.

Influence of phylogenetic diversity of plant communities on tri-trophic interactions

Phylogenetic diversity of plant communities can influence the interaction between plants, herbivores, and their natural enemies. Plant communities with phylogenetically distant species tend to present a wide variety of functional traits and ecological niches, which in turn can influence competitive interactions among plants as well as food and habitat quality for herbivores and their natural enemies. To assess some different mechanisms by which phylogenetic diversity of plant communities can influence herbivores and their natural enemies, we established 12 experimental plots of tropical trees with two treatments: high and low phylogenetic diversity. We measured plant growth and anti-herbivore defenses, herbivore foliar damage, and predator activity in seven species that were present in both treatments. We found significant differences in the expression of plant traits as a function of species identity and their life history, but also depending on the phylogenetic context in which they grew. Pioneer species had higher growth and produced more phenolics in plots with high phylogenetic diversity versus plants in plots with low phylogenetic diversity. Accordingly, herbivore damage in these species was greater in plots with low phylogenetic diversity. Finally, predator activity on caterpillar clay models placed on plants was greater within the low phylogenetic diversity treatment, but only for non-myrmecophytic species. These results suggest that plant phylogenetic diversity can influence the expression of growth and defensive traits and further modify the interaction between plants, herbivores, and their natural enemies. However, such effects depend on plant life history and the presence of mutualistic interaction with ants.

Rethinking the complexity and uncertainty of spatial networks applied to forest ecology

Characterizing tree spatial patterns and interactions are helpful to reveal underlying processes assembling forest communities. Spatial networks, despite their complexity, are powerful to examine spatial interactions at an individual level using well-defined patterns. However, complex forestation networks introduce uncertainties. Validation methods are needed to assess whether network-based metrics can identify different processes. Here, we constructed three types of networks, which reflect various aspects of tree competition. Based on five spatial null models and 199 Monte-Carlo simulations, we were able to select network-based metrics that exhibited well performance in distinguishing different processes. This technique was then applied to a tropical forest dataset in Costa Rica. We found that the average node degree and the clustering coefficient are good metrics like the paired correlation function. In addition, the network approach can identify fine-scale spatial variations of tree competition and its underlying causes. Our analyzes also indicate that a bit of caution is needed when defining the network structure as well as designing network-based metrics. We suggested that validation techniques using corresponding spatial null models are critically important to reduce the negative effects caused by uncertainties of the network.

Functional Diversity of Plant Communities in Relationship to Leaf and Soil Stoichiometry in Karst Areas of Southwest China

Assessment of relationships between functional diversity and ecological stoichiometry in plant communities can aid in determining the relative variability and ecological complementarity of functional attributes among species, which is a better approach to understanding ecosystem processes and functions than studying species taxonomic diversity. Here, we analyzed the relationships among community weighted means of functional traits, functional diversity, and leaf and soil chemical properties of plant communities during various stages of vegetation restoration in Mao Lan National Karst Forest Nature Reserve, located in humid subtropical Guizhou of China. Our results showed significant changes in four weighted functional traits of plant communities at different restoration stages, namely, plant height, leaf width to leaf length ratio, and leaf area. Additionally, with the progression of the recovery of plant communities, functional richness, functional separation, and quadratic entropy, the coefficient tended to increase. Functional divergence tended to gradually decrease. The association of functional diversity with soil chemical properties was stronger than that with leaf ecological stoichiometry. Regarding leaf and soil chemical properties, soil phosphorus content and leaf C:P were particularly important in influencing functional diversity. Our overall findings indicate that functional traits shift from “acquisitive” to “conservative” as the community is restored. Karst plant communities reduce interspecific resource competition as restoration proceeds, thereby increasing functional overlap effects.

Might field experiments also be inadvertent metacommunities?

Metacommunity theory predicts that the composition and diversity of a site depends on its characteristics and those of its neighborhood. Dispersal between plots in a field experiment could link responses observed in a focal plot to both its treatment and those of its neighbors. However, the diversity, composition and treatments of neighboring plots are rarely included in analyses of experimental treatments. We analyzed a spatially gridded grassland nitrogen addition experiment and found that plant species richness and the composition of focal plots were influenced not just by their nitrogen treatment but also by the number of species in neighboring plots and their abundances. For each additional species in a focal plot's neighborhood, the species richness of the focal plot increased by 0.30 species per 0.3 m² . Control plots had a significant loss of species, at a rate of ~0.23 species per 0.3 m² per year during the 23-year experiment, but only when their neighborhoods had low species richness. Changes in the abundance of the three dominant species depended both on the nitrogen treatment of a focal plot and on their abundance in adjacent plots. Our analyses suggested that both the experimental nitrogen treatments and metacommunity processes co-determined plant species richness and plant species' abundances. Our findings suggested that analyzing many traditional field experiments with a metacommunity perspective may reveal confounding of experimental treatments and provide empirical data to test metacommunity theory.

Regularized Regression: A New Tool for Investigating and Predicting Tree Growth

Neighborhood models have allowed us to test many hypotheses regarding the drivers of variation in tree growth, but require considerable computation due to the many empirically supported non-linear relationships they include. Regularized regression represents a far more efficient neighborhood modeling method, but it is unclear whether such an ecologically unrealistic model can provide accurate insights on tree growth. Rapid computation is becoming increasingly important as ecological datasets grow in size, and may be essential when using neighborhood models to predict tree growth beyond sample plots or into the future. We built a novel regularized regression model of tree growth and investigated whether it reached the same conclusions as a commonly used neighborhood model, regarding hypotheses of how tree growth is influenced by the species identity of neighboring trees. We also evaluated the ability of both models to interpolate the growth of trees not included in the model fitting dataset. Our regularized regression model replicated most of the classical model’s inferences in a fraction of the time without using high-performance computing resources. We found that both methods could interpolate out-of-sample tree growth, but the method making the most accurate predictions varied among focal species. Regularized regression is particularly efficient for comparing hypotheses because it automates the process of model selection and can handle correlated explanatory variables. This feature means that regularized regression could also be used to select among potential explanatory variables (e.g., climate variables) and thereby streamline the development of a classical neighborhood model. Both regularized regression and classical methods can interpolate out-of-sample tree growth, but future research must determine whether predictions can be extrapolated to trees experiencing novel conditions. Overall, we conclude that regularized regression methods can complement classical methods in the investigation of tree growth drivers and represent a valuable tool for advancing this field toward prediction.

Consequences of spatial patterns for coexistence in species-rich plant communities

Ecology cannot yet fully explain why so many tree species coexist in natural communities such as tropical forests. A major difficulty is linking individual-level processes to community dynamics. We propose a combination of tree spatial data, spatial statistics and dynamical theory to reveal the relationship between spatial patterns and population-level interaction coefficients and their consequences for multispecies dynamics and coexistence. Here we show that the emerging population-level interaction coefficients have, for a broad range of circumstances, a simpler structure than their individual-level counterparts, which allows for an analytical treatment of equilibrium and stability conditions. Mechanisms such as animal seed dispersal, which result in clustering of recruits that is decoupled from parent locations, lead to a rare-species advantage and coexistence of otherwise neutral competitors. Linking spatial statistics with theories of community dynamics offers new avenues for explaining species coexistence and calls for rethinking community ecology through a spatial lens.

Network science applied to forest megaplots: tropical tree species coexist in small-world networks

Network analysis is an important tool to analyze the structure of complex systems such as tropical forests. Here, we infer spatial proximity networks in tropical forests by using network science. First, we focus on tree neighborhoods to derive spatial tree networks from forest inventory data. In a second step, we construct species networks to describe the potential for interactions between species. We find remarkably similar tree and species networks among tropical forests in Panama, Sri Lanka and Taiwan. Across these sites only 32 to 51% of all possible connections between species pairs were realized in the species networks. The species networks show the common small-world property and constant node degree distributions not yet described and explained by network science. Our application of network analysis to forest ecology provides a new approach in biodiversity research to quantify spatial neighborhood structures for better understanding interactions between tree species. Our analyses show that details of tree positions and sizes have no important influence on the detected network structures. This suggests existence of simple principles underlying the complex interactions in tropical forests.

Experimental Evidence for the Importance of Light on Understory Grass Communities in a Subtropical Forest

Light is one of the most important environmental filters for forest understory grass communities. It is predicted that light can select species with the same light requirements, resulting in a decrease in species compositional dissimilarity among grass communities experiencing the same light intensity, and an increase in community dissimilarity under variable light intensities. However, these predictions have been questioned recently in light of modern coexistence theories, and evidence for them in natural communities is often indistinguishable from patterns created by dispersal limitation and biotic interactions. To help fill this gap, we sampled 48 understory grass communities that had regenerated from the same soil seed bank in Southern China. Plots were established under a light intensity gradient. Changes in species composition and neighborhood densities were monitored over a growing season. Our experimental setup controls for bias from dispersal limitation and is useful for detecting the effects of biotic interactions at different intensities of light. As expected, (1) compositional dissimilarity of grass communities increased between communities with different light intensities. The extent to which communities became more dissimilar was positively correlated with the difference in the light intensity. (2) No significant change in compositional dissimilarity was observed among communities experiencing the same light intensity. (3) Finally, relative neighborhood density significantly decreased in communities with moderate to high shading treatments. Our results clearly show that light can drive compositional divergence among communities under different light densities. However, the light may not lead to convergence among communities experiencing the same low light intensity, because intense competition induced by low light might enlarge species compositional differences, as shown with the neighborhood density analysis. Therefore, our study provides more convincing evidence for the importance of light on understory grass communities in subtropical forests and highlights the need to jointly consider biotic interactions when testing for evidence for environmental filtering.

Coexistence mechanisms of Tamarix chinensis and Suaeda salsa in the Yellow River Delta, China

To examine how two dominant species coexist within a tidal wetland in the Yellow River Delta, we studied the spatial distribution patterns and ecological relationships of Tamarix chinensis and Suaeda salsa. We also analyzed the relationship between these two plant species and soil chemical properties. Nine quadrats were established, and aerial photography was carried out in July 2018 in the study area to investigate plants and soil. Results showed that T. chinensis showed an aggregation distribution at scales of 0-10 m, 0-30 m, and 0-50 m from the sea to inland. Unlike T. chinensis, S. salsa showed an aggregation distribution at approximately 0-50 m in the study area, which meant the aggregation distributions of T. chinensis and S. salsa were found at different scales and S. salsa tended to aggregate distribution compared with T. chinensis. Meanwhile, T. chinensis and S. salsa had negative correlations far from the sea at a scale of 0-20 m and at the offshore area at a scale of 0-30 m. However, in the intermediate area, S. salsa and T. chinensis showed a positive correlation at a scale of 0-30 m. In general, the relationship between the two groups tends to be negatively correlated in a small range. Given that the tidal action decreased from the sea to inland, the driving factors of population aggregation gradually changed from tidal flooding to an interspecific relationship. The different characteristics of the different species may also have had an effect. And the aggregation of adult plant species had a beneficial impact on the establishment and growth of seedlings and plants. Furthermore, soil properties comprised complex actions including environmental conditions and ecological processes. The soil chemical properties such as soil salinity and nutrients were also influenced by the species' canopy.

Integrating the underlying structure of stochasticity into community ecology

Stochasticity is a core component of ecology, as it underlies key processes that structure and create variability in nature. Despite its fundamental importance in ecological systems, the concept is often treated as synonymous with unpredictability in community ecology, and studies tend to focus on single forms of stochasticity rather than taking a more holistic view. This has led to multiple narratives for how stochasticity mediates community dynamics. Here, we present a framework that describes how different forms of stochasticity (notably demographic and environmental stochasticity) combine to provide underlying and predictable structure in diverse communities. This framework builds on the deep ecological understanding of stochastic processes acting at individual and population levels and in modules of a few interacting species. We support our framework with a mathematical model that we use to synthesize key literature, demonstrating that stochasticity is more than simple uncertainty. Rather, stochasticity has profound and predictable effects on community dynamics that are critical for understanding how diversity is maintained. We propose next steps that ecologists might use to explore the role of stochasticity for structuring communities in theoretical and empirical systems, and thereby enhance our understanding of community dynamics.

Phylogeny and ecological processes influence grass coexistence at different spatial scales within the steppe biome

Phylogenetic analyses are essential for disentangling how environmental filtering and competition determine species coexistence across spatial scales. Inner Mongolia steppe has strong environmental gradients, but how the phylogenetic relatedness of co-occurring species and phylogenetic signals of functional traits change across spatial scales remains unclear. We investigated the phylogenetic structure of grass assemblages along environmental gradients from regional to local scales, and measured functional traits within assemblages. We compared phylogenetic signals of plant traits between the same numbers of species randomly selected from the regional pool and species observed at the local scale, did phylogenetic principal component analysis to infer the main factors driving species coexistence, and examined the key plant trait-environment relationships across the phylogeny to reveal ecological adaptation mechanisms. Regionally, grass species were phylogenetically clustered with contrasting climate preferences. With decreasing spatial scales, species richness declined, changing from phylogenetically clustered to overdispersed, and phylogenetic signals of plant traits became weaker. At the local scale, grass assemblages were structured by soil water content and neighbor density, and the trait-environment relationships were less clear than those at the regional scale. This study demonstrated that at smaller scales, co-occurring grass species in the steppe tended to be more phylogenetically overdispersed, and that phylogenetic signals of plant functional traits became weaker with increasing abiotic and biotic interactions. Our findings contributed evidence for understanding species coexistence and maintenance at scales spanning regional to local communities in the East Asia steppe biome.

When do we have the power to detect biological interactions in spatial point patterns?

Uncovering the roles of biotic interactions in assembling and maintaining species-rich communities remains a major challenge in ecology. In plant communities, interactions between individuals of different species are expected to generate positive or negative spatial interspecific associations over short distances. Recent studies using individual-based point pattern datasets have concluded that (a) detectable interspecific interactions are generally rare, but (b) are most common in communities with fewer species; and (c) the most abundant species tend to have the highest frequency of interactions. However, it is unclear how the detection of spatial interactions may change with the abundances of each species, or the scale and intensity of interactions. We ask if statistical power is sufficient to explain all three key results.We use a simple two-species model, assuming no habitat associations, and where the abundances, scale and intensity of interactions are controlled to simulate point pattern data. In combination with an approximation to the variance of the spatial summary statistics that we sample, we investigate the power of current spatial point pattern methods to correctly reject the null model of pairwise species independence.We show the power to detect interactions is positively related to both the abundances of the species tested, and the intensity and scale of interactions, but negatively related to imbalance in abundances. Differences in detection power in combination with the abundance distributions found in natural communities are sufficient to explain all the three key empirical results, even if all pairwise interactions are identical. Critically, many hundreds of individuals of both species may be required to detect even intense interactions, implying current abundance thresholds for including species in the analyses are too low. Sy n thesis. The widespread failure to reject the null model of spatial interspecific independence could be due to low power of the tests rather than any key biological process. Since we do not model habitat associations, our results represent a first step in quantifying sample sizes required to make strong statements about the role of biotic interactions in diverse plant communities. However, power should be factored into analyses and considered when designing empirical studies.

Disentangling the functional trait correlates of spatial aggregation in tropical forest trees

Environmental filtering and dispersal limitation can both maintain diversity in plant communities by aggregating conspecifics, but parsing the contribution of each process has proven difficult empirically. Here, we assess the contribution of filtering and dispersal limitation to the spatial aggregation patterns of 456 tree species in a hyperdiverse Amazonian forest and find distinct functional trait correlates of interspecific variation in these processes. Spatial point process model analysis revealed that both mechanisms are important drivers of intraspecific aggregation for the majority of species. Leaf drought tolerance was correlated with species topographic distributions in this aseasonal rainforest, showing that future increases in drought severity could significantly impact community structure. In addition, seed mass was associated with the spatial scale and density of dispersal-related aggregation. Taken together, these results suggest environmental filtering and dispersal limitation act in concert to influence the spatial and functional structure of diverse forest communities.