Linking functional traits and demography to model species-rich communities

Development and applications of metabolic models in plant multi-omics research

Plant growth and development are characterized by systematic and continuous processes, each involving intricate metabolic coordination mechanisms. Mathematical models are essential tools for investigating plant growth and development, metabolic regulation networks, and growth patterns across different stages. These models offer insights into secondary metabolism patterns in plants and the roles of metabolites. The proliferation of data related to plant genomics, transcriptomics, proteomics, and metabolomics in the last decade has underscored the growing importance of mathematical modeling in this field. This review aims to elucidate the principles and types of metabolic models employed in studying plant secondary metabolism, their strengths, and limitations. Furthermore, the application of mathematical models in various plant systems biology subfields will be discussed. Lastly, the review will outline how mathematical models can be harnessed to address research questions in this context.

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.

Why incorporate plant architecture into trait-based ecology?

Trait-based ecology has improved our understanding of the functioning of organisms, communities, ecosystems, and beyond. However, its predictive ability remains limited as long as phenotypic integration and temporal dynamics are not considered. We highlight how the morphogenetic processes that shape the 3D development of a plant during its lifetime affect its performance. We show that the diversity of architectural traits allows us to go beyond organ-level traits in capturing the temporal and spatial dimensions of ecological niches and informing community assembly processes. Overall, we argue that consideration of multilevel topological, geometrical, and ontogenetic features provides a dynamic view of the whole-plant phenotype and a relevant framework for investigating phenotypic integration, plant adaptation and performance, and community structure and dynamics.

Using ancient sedimentary DNA to forecast ecosystem trajectories under climate change

Ecosystem response to climate change is complex. In order to forecast ecosystem dynamics, we need high-quality data on changes in past species abundance that can inform process-based models. Sedimentary ancient DNA (sedaDNA) has revolutionised our ability to document past ecosystems' dynamics. It provides time series of increased taxonomic resolution compared to microfossils (pollen, spores), and can often give species-level information, especially for past vascular plant and mammal abundances. Time series are much richer in information than contemporary spatial distribution information, which have been traditionally used to train models for predicting biodiversity and ecosystem responses to climate change. Here, we outline the potential contribution of sedaDNA to forecast ecosystem changes. We showcase how species-level time series may allow quantification of the effect of biotic interactions in ecosystem dynamics, and be used to estimate dispersal rates when a dense network of sites is available. By combining palaeo-time series, process-based models, and inverse modelling, we can recover the biotic and abiotic processes underlying ecosystem dynamics, which are traditionally very challenging to characterise. Dynamic models informed by sedaDNA can further be used to extrapolate beyond current dynamics and provide robust forecasts of ecosystem responses to future climate change. This article is part of the theme issue 'Ecological novelty and planetary stewardship: biodiversity dynamics in a transforming biosphere'.

Assessing the predictive value of morphological traits on primary lifestyle of birds through the extreme gradient boosting algorithm

The relationship between morphological traits and ecological performance in birds is an important area of research, as it can help us to understand how birds are able to adapt and how they are affected by changes in their environment. Many studies have investigated the relationship between morphological traits and certain aspects of the performance and ecological niche of these animals. However, the relationship between morphological traits and the primary lifestyle of birds has not previously been explored. This paper aims to evaluate the predictive potential of morphological data to determine the primary lifestyle of birds through a tree-based machine learning algorithm. By doing this, it is also possible to evaluate these artificial categories that we used to split up birds and know whether they are suitable for dividing them in function of shared morphological characteristics or need a redefinition under more discriminant criteria. Supplementary dataset 1 of the AVONET project was used, which comprises the 11 morphological predictors used in this work and the classification according to the primary lifestyle for more than 95% of the existing bird species. For all morphological traits used, statistically significant univariate differences were found between primary lifestyles. The three fitted machine learning models showed high accuracy, in all cases above 78% and superior to the ones achieved through traditional approaches used as contrasts. The results obtained provide evidence that primary lifestyle can be predicted in birds based on morphological traits, as well as more insights about the relevance of functional traits for ecological modeling. This is another step forward in our mechanistic understanding of bird ecology, while exploring how birds have adapted to their environments and how they interact with their surroundings.

Coral assemblages at higher latitudes favor short-term potential over long-term performance

The persistent exposure of coral assemblages to more variable abiotic regimes is assumed to augment their resilience to future climatic variability. Yet, while the determinants of coral population resilience across species remain unknown, we are unable to predict the winners and losers across reef ecosystems exposed to increasingly variable conditions. Using annual surveys of 3171 coral individuals across Australia and Japan (2016-2019), we explore spatial variation across the short- and long-term dynamics of competitive, stress-tolerant, and weedy assemblages to evaluate how abiotic variability mediates the structural composition of coral assemblages. We illustrate how, by promoting short-term potential over long-term performance, coral assemblages can reduce their vulnerability to stochastic environments. However, compared to stress-tolerant, and weedy assemblages, competitive coral taxa display a reduced capacity for elevating their short-term potential. Accordingly, future climatic shifts threaten the structural complexity of coral assemblages in variable environments, emulating the degradation expected across global tropical reefs.

MOSAIC - A Unified Trait Database to Complement Structured Population Models

Despite exponential growth in ecological data availability, broader interoperability amongst datasets is needed to unlock the potential of open access. Our understanding of the interface of demography and functional traits is well-positioned to benefit from such interoperability. Here, we introduce MOSAIC, an open-access trait database that unlocks the demographic potential stored in the COMADRE, COMPADRE, and PADRINO open-access databases. MOSAIC data were digitised and curated through a combination of existing datasets and new trait records sourced from primary literature. In its first release, MOSAIC (v. 1.0.0) includes 14 trait fields for 300 animal and plant species: biomass, height, growth determination, regeneration, sexual dimorphism, mating system, hermaphrodism, sequential hermaphrodism, dispersal capacity, type of dispersal, mode of dispersal, dispersal classes, volancy, and aquatic habitat dependency. MOSAIC includes species-level phylogenies for 1,359 species and population-specific climate data. We identify how database integration can improve our understanding of traits well-quantified in existing repositories and those that are poorly quantified (e.g., growth determination, modularity). MOSAIC highlights emerging challenges associated with standardising databases and demographic measures.

Fission in a colonial marine invertebrate signifies unique life history strategies rather than being a demographic trait

Each of the few known life-history strategies (e.g., r/K and parity [semelparity and iteroparity]), is a composite stratagem, signified by co-evolved sets of trade-offs with stochastically distributed variations that do not form novel structured strategies. Tracking the demographic traits of 81 Botryllus schlosseri (a marine urochordate) colonies, from birth to death, we revealed three co-existing novel life-history strategies in this long-standing laboratory-bred population, all are bracketed through colonial fission (termed NF, FA and FB for no fission, fission after and fission before reaching maximal colony size, respectively) and derived from organisms maintained in a benign, highly invariable environment. This environment allows us to capture the strategists’ blueprints and their net performance through 13 traits, each branded by high within-strategy variation. Yet, six traits differed significantly among the strategies and, in two, the FB was notably different. These results frame fissions in colonial organisms not as demographic traits, but as pivotal agents for life-history strategies.

Dynamic Species Distribution Modeling Reveals the Pivotal Role of Human-Mediated Long-Distance Dispersal in Plant Invasion

Plant invasions generate massive ecological and economic costs worldwide. Predicting their spatial dynamics is crucial to the design of effective management strategies and the prevention of invasions. Earlier studies highlighted the crucial role of long-distance dispersal in explaining the speed of many invasions. In addition, invasion speed depends highly on the duration of its lag phase, which may depend on the scaling of fecundity with age, especially for woody plants, even though empirical proof is still rare. Bayesian dynamic species distribution models enable the fitting of process-based models to partial and heterogeneous observations using a state-space modeling approach, thus offering a tool to test such hypotheses on past invasions over large spatial scales. We use such a model to explore the roles of long-distance dispersal and age-structured fecundity in the transient invasion dynamics of Plectranthus barbatus, a woody plant invader in South Africa. Our lattice-based model accounts for both short and human-mediated long-distance dispersal, as well as age-structured fecundity. We fitted our model on opportunistic occurrences, accounting for the spatio-temporal variations of the sampling effort and the variable detection rates across datasets. The Bayesian framework enables us to integrate a priori knowledge on demographic parameters and control identifiability issues. The model revealed a massive wave of spatial spread driven by human-mediated long-distance dispersal during the first decade and a subsequent drastic population growth, leading to a global equilibrium in the mid-1990s. Without long-distance dispersal, the maximum population would have been equivalent to 30% of the current equilibrium population. We further identified the reproductive maturity at three years old, which contributed to the lag phase before the final wave of population growth. Our results highlighted the importance of the early eradication of weedy horticultural alien plants around urban areas to hamper and delay the invasive spread.

Modelling coupled human-environment complexity for the future of the biosphere: strengths, gaps and promising directions

Humans and the environment form a single complex system where humans not only influence ecosystems but also react to them. Despite this, there are far fewer coupled human-environment system (CHES) mathematical models than models of uncoupled ecosystems. We argue that these coupled models are essential to understand the impacts of social interventions and their potential to avoid catastrophic environmental events and support sustainable trajectories on multi-decadal timescales. A brief history of CHES modelling is presented, followed by a review spanning recent CHES models of systems including forests and land use, coral reefs and fishing and climate change mitigation. The ability of CHES modelling to capture dynamic two-way feedback confers advantages, such as the ability to represent ecosystem dynamics more realistically at longer timescales, and allowing insights that cannot be generated using ecological models. We discuss examples of such key insights from recent research. However, this strength brings with it challenges of model complexity and tractability, and the need for appropriate data to parameterize and validate CHES models. Finally, we suggest opportunities for CHES models to improve human-environment sustainability in future research spanning topics such as natural disturbances, social structure, social media data, model discovery and early warning signals. This article is part of the theme issue 'Ecological complexity and the biosphere: the next 30 years'.

Long-term coexistence of two invasive vespid wasps in NW Patagonia (Argentina)

In Patagonia (Argentina) two non-native vespid wasps became established in the last decades. Vespula germanica was first detected in 1980, while V. vulgaris arrived some 30 years later. Both species can have a strong negative impact on agro-industrial economic activities, the natural environment, and outdoor human activities. Biological invasions may be influenced negatively by the degree of interaction with the resident native community and alien species already present. The sequential arrival and coexistence of Vespula wasps in Argentina for several years allows us to understand key questions of invasion ecology. Additionally, recognizing the outcome of the invasion by vespids in Patagonia, a region lacking native social wasps, may help plan species-focused mitigation and control strategies. We explored the role of competition in terms of invasion success, and the strategies that promote coexistence. Two possible scenarios, using niche overlap indices and isocline equations, were proposed to determine competition coefficients. Using a simple mathematical modeling framework, based on field collected data, we show that food resources do not play a central role in competitive interaction. The competition coefficients obtained from the equations were different from those inferred from the overlap indices (0.53 and 0.54-0.076 and 0.197, respectively). Together, these findings suggest that no matter the arrival order, V. vulgaris, always reaches higher densities than V. germanica when both species invade new regions. Our work contributes to further our understanding on the worldwide invasion processes deployed by these two eusocial insects.

Forest regeneration within Earth system models: current process representations and ways forward

Earth system models must predict forest responses to global change in order to simulate future global climate, hydrology, and ecosystem dynamics. These models are increasingly adopting vegetation demographic approaches that explicitly represent tree growth, mortality, and recruitment, enabling advances in the projection of forest vulnerability and resilience, as well as evaluation with field data. To date, simulation of regeneration processes has received far less attention than simulation of processes that affect growth and mortality, in spite of their critical role maintaining forest structure, facilitating turnover in forest composition over space and time, enabling recovery from disturbance, and regulating climate-driven range shifts. Our critical review of regeneration process representations within current Earth system vegetation demographic models reveals the need to improve parameter values and algorithms for reproductive allocation, dispersal, seed survival and germination, environmental filtering in the seedling layer, and tree regeneration strategies adapted to wind, fire, and anthropogenic disturbance regimes. These improvements require synthesis of existing data, specific field data-collection protocols, and novel model algorithms compatible with global-scale simulations. Vegetation demographic models offer the opportunity to more fully integrate ecological understanding into Earth system prediction; regeneration processes need to be a critical part of the effort.

Species Diversity Regulates Ecological Strategy Spectra of Forest Vegetation Across Different Climatic Zones

Ecological strategy is the tactics employed by species in adapting to abiotic and biotic conditions. The ecological strategy spectrum is defined as the relative proportion of species in different ecological strategy types within a community. Determinants of ecological strategy spectrum of plant community explored by most previous studies are about abiotic factors. Yet, the roles of biotic factors in driving variations of ecological strategy spectra of forest communities across different geographic regions remains unknown. In this study, we established 200 0.04-ha forest dynamics plots (FDPs) and measured three-leaf functional traits of tree and shrub species in four forest vegetation types across four climatic zones. Based on Grime's competitor, stress-tolerator, ruderal (CSR) triangular framework, and the StrateFy method, we categorized species into four ecological strategy groups (i.e., C-, S-, Int-, and R-groups) and related the ecological spectra of the forests to three species diversity indices [i.e., species richness, Shannon-Wiener index, and stem density (stem abundance)]. Linear regression, redundancy analysis, and variance partition analysis were utilized for assessing the roles of species diversity in regulating ecological strategy spectra of forest communities across different climatic zones. We found that the proportion of species in the C- and Int-groups increased, while the proportion of species in the S-group decreased, with the increase of three indices of species diversity. Among the three species diversity indices, stem abundance played the most important role in driving variations in ecological strategy spectra of forests across different climatic zones. Our finding highlights the necessity of accounting for biotic factors, especially stem abundance, in modeling or predicting the geographical distributions of plant species with varied ecological adaptation strategies to future environmental changes.

Biota-mediated carbon cycling-A synthesis of biotic-interaction controls on blue carbon

Research into biotic interactions has been a core theme of ecology for over a century. However, despite the obvious role that biota play in the global carbon cycle, the effects of biotic interactions on carbon pools and fluxes are poorly understood. Here we develop a conceptual framework that illustrates the importance of biotic interactions in regulating carbon cycling based on a literature review and a quantitative synthesis by means of meta-analysis. Our study focuses on blue carbon ecosystems-vegetated coastal ecosystems that function as the most effective long-term CO₂ sinks of the biosphere. We demonstrate that a multitude of mutualistic, competitive and consumer-resource interactions between plants, animals and microbiota exert strong effects on carbon cycling across various spatial scales ranging from the rhizosphere to the landscape scale. Climate change-sensitive abiotic factors modulate the strength of biotic-interaction effects on carbon fluxes, suggesting that the importance of biota-mediated carbon cycling will change under future climatic conditions. Strong effects of biotic interactions on carbon cycling imply that biosphere-climate feedbacks may not be sufficiently represented in current Earth system models. Inclusion of new functional groups in these models, and new approaches to simplify species interactions, may thus improve the predictions of biotic effects on the global climate.

A Partition Theorem for a Randomly Selected Large Population

A theorem on the partitioning of a randomly selected large population into stationary and non-stationary components by using a property of the stationary population identity is stated and proved. The methods of partitioning demonstrated are original and these are helpful in real-world situations where age-wise data is available. Applications of this theorem for practical purposes are summarized at the end.

Theoretical guidelines for editing ecological communities

Having control over species abundances and community resilience is of great interest for experimental, agricultural, industrial and conservation purposes. Here, we theoretically explore the possibility of manipulating ecological communities by modifying pairwise interactions. Specifically, we establish which interaction values should be modified, and by how much, in order to alter the composition or resilience of a community towards a favorable direction. While doing so, we also take into account the experimental difficulties in making such modifications by including in our optimization process, a cost parameter, which penalizes large modifications. In addition to prescribing what changes should be made to interspecies interactions given some modification cost, our approach also serves to establish the limits of community control, i.e. how well can one approach an ecological goal at best, even when not constrained by cost.