Biodiversity matters in feedbacks between climate change and air quality: a study using an individual-based model

Summer ozone pollution in China affected by the intensity of Asian monsoon systems

Ozone in the troposphere is harmful to human health and ecosystems. It has become the most severe air pollutant in China. Here, based on global atmospheric chemistry model simulations during 1981-2019 and nation-wide surface observations, the impacts of interannual variations in Asian summer monsoon (ASM), including East Asian summer monsoon (EASM) and South Asian summer monsoon (SASM), on surface O₃ concentrations during June-July-August (JJA) in China are investigated. EASM intensity has a significant positive correlation with the surface O₃ concentration in south-central China (97.5°-117.5°E, 20°-35°N) with a correlation coefficient of 0.6. Relative to the weak EASM years, O₃ concentrations in strong EASM years increased by up to 5 ppb (10 % relative to the average) due to the weakened transboundary transport of O₃ resulting from the decrease in prevailing southwesterlies. SASM can be divided into two components. The one near East Asia has a similar relation with O₃ in southern China (100°-117.5°E, 22°-32°N) as that of EASM. The other component of SASM is negatively correlated with surface O₃ concentration in eastern China (110°-117.5°E, 22°-34°N) and the maximum difference in O₃ concentrations exceeded 5 ppb (10 %) between the strong and weak monsoon years, which can be explained by the O₃ divergence caused by the anomalous southerlies blowing pollutants away from the northern boundary of eastern China. This study shows that the ASM has an important impact on the O₃ concentrations in China, primarily through changing transboundary transport related to the variability of large-scale circulations, which has great implications for air pollution prevention and mitigation in China. Future projections of ASM suggests that the sustainable and medium development scenarios are the perfect pathways that can help to mitigate O₃ pollution, while high social vulnerability and radiative forcing scenarios could enhance future O₃ pollution in China.

Linking animal behavior to ecosystem change in disturbed environments

Environmental disturbances often cause individuals to change their behavior. The behavioral responses can induce a chain of reactions through the network of species interactions, via consumptive and trait mediated connections. Given that species interactions define ecosystem structure and functioning, changes to these interactions often have ecological repercussions. Here, we explore the transmission of behavioral responses through the network of species interactions, and how the responses influence ecological conditions. We describe the underlying mechanisms and the ultimate impact that the behavioral responses can have on ecosystem structure and functioning, including biodiversity and ecosystems stability and services. We explain why behavioral responses of some species have a larger impact than that of others on ecosystems, and why research should focus on these species and their interactions. With the work, we synthesize existing theory and empirical evidence to provide a conceptual framework that links behavior responses to altered species interactions, community dynamics, and ecosystem processes. Considering that species interactions link biodiversity to ecosystem functioning, a deeper understanding of behavioral responses and their causes and consequences can improve our knowledge of the mechanisms and pathways through which human activities alter ecosystems. This knowledge can improve our ability to predict the effects of ongoing disturbances on communities and ecosystems and decide on the interventions needed to mitigate negative effects.

Climate-Driven Legacies in Simulated Microbial Communities Alter Litter Decomposition Rates

The mechanisms underlying diversity-functioning relationships have been a consistent area of inquiry in biogeochemistry since the 1950s. Though these mechanisms remain unresolved in soil microbiomes, many approaches at varying scales have pointed to the same notion—composition matters. Confronting the methodological challenge arising from the complexity of microbiomes, this study used the model DEMENTpy, a trait-based modeling framework, to explore trait-based drivers of microbiome-dependent litter decomposition. We parameterized DEMENTpy for five sites along a climate gradient in Southern California, United States, and conducted reciprocal transplant simulations analogous to a prior empirical study. The simulations demonstrated climate-dependent legacy effects of microbial communities on plant litter decomposition across the gradient. This result is consistent with the previous empirical study across the same gradient. An analysis of community-level traits further suggests that a 3-way tradeoff among resource acquisition, stress tolerance, and yield strategies influences community assembly. Simulated litter decomposition was predictable with two community traits (indicative of two of the three strategies) plus local environment, regardless of the system state (transient vs. equilibrium). Although more empirical confirmation is still needed, community traits plus local environmental factors (e.g., environment and litter chemistry) may robustly predict litter decomposition across spatial-temporal scales. In conclusion, this study offers a potential trait-based explanation for climate-dependent community effects on litter decomposition with implications for improved understanding of whole-ecosystem functioning across scales.

The Significance of Aggregation Methods in Functional Group Modeling

The growth of forests and the feedbacks between forests and environmental changes are central issues in the planetary carbon cycle, global climate change, and basic plant ecology. A challenge to understanding both growth and feedbacks from local to global scales is that many critical metabolic processes vary among species. An innovation in solving this challenge is the recognition that species can be lumped into “functional groups” based on metabolic similarity, and these functional groups can then be studied in computational models that simulate ecosystem function. Despite the vast resources devoted to functional group studies and the progress made by them, an important logical and biological question has not been formally addressed, “How do the groupings alter the results of modeling studies?” To what extent do modeling results depend on the choices made in aggregating taxa into functional groups. Here, we consider the effects of using different aggregation strategies in simulating the carbon dynamics of a deciduous forest. Understanding the impacts that aggregation strategy has on efforts to simulate regional-to-global-scale forest dynamics offers insights into both ecosystem regulation and model function and addresses this central problem in the study of carbon dynamics.

Tackling unresolved questions in forest ecology: The past and future role of simulation models

Understanding the processes that shape forest functioning, structure, and diversity remains challenging, although data on forest systems are being collected at a rapid pace and across scales. Forest models have a long history in bridging data with ecological knowledge and can simulate forest dynamics over spatio-temporal scales unreachable by most empirical investigations.We describe the development that different forest modelling communities have followed to underpin the leverage that simulation models offer for advancing our understanding of forest ecosystems.Using three widely applied but contrasting approaches - species distribution models, individual-based forest models, and dynamic global vegetation models - as examples, we show how scientific and technical advances have led models to transgress their initial objectives and limitations. We provide an overview of recent model applications on current important ecological topics and pinpoint ten key questions that could, and should, be tackled with forest models in the next decade.Synthesis. This overview shows that forest models, due to their complementarity and mutual enrichment, represent an invaluable toolkit to address a wide range of fundamental and applied ecological questions, hence fostering a deeper understanding of forest dynamics in the context of global change.

An overview of agent-based models in plant biology and ecology

Agent-based modelling (ABM) has become an established methodology in many areas of biology, ranging from the cellular to the ecological population and community levels. In plant science, two different scales have predominated in their use of ABM. One is the scale of populations and communities, through the modelling of collections of agents representing individual plants, interacting with each other and with the environment. The other is the scale of the individual plant, through the modelling, by functional-structural plant models (FSPMs), of agents representing plant building blocks, or metamers, to describe the development of plant architecture and functions within individual plants. The purpose of this review is to show key results and parallels in ABM for growth, mortality, carbon allocation, competition and reproduction across the scales from the plant organ to populations and communities on a range of spatial scales to the whole landscape. Several areas of application of ABMs are reviewed, showing that some issues are addressed by both population-level ABMs and FSPMs. Continued increase in the relevance of ABM to environmental science and management will be helped by greater integration of ABMs across these two scales.