Trait-based Models Research Articles

The Impact of Microbial Interactions on Ecosystem Function Intensifies Under Stress.

A major challenge in ecology is to understand how different species interact to determine ecosystem function, particularly in communities with large numbers of co-occurring species. We use a trait-based model of microbial litter decomposition to quantify how different taxa impact ecosystem function. Furthermore, we build a novel framework that highlights the interplay between taxon traits and environmental conditions, focusing on their combined influence on community interactions and ecosystem function. Our results suggest that the ecosystem impact of a taxon is driven by its resource acquisition traits and the community functional capacity, but that physiological stress amplifies the impact of both positive and negative interactions. Furthermore, net positive impacts on ecosystem function can arise even as microbes have negative pairwise interactions with other taxa. As communities shift in response to global climate change, our findings reveal the potential to predict the biogeochemical functioning of communities from taxon traits and interactions.

Remote sensing of temperature-dependent mosquito and viral traits predicts field surveillance-based disease risk.

Mosquito-borne diseases contribute substantially to the global burden of disease, and are strongly influenced by environmental conditions. Ongoing and rapid environmental change necessitates improved understanding of the response of mosquito-borne diseases to environmental factors like temperature, and novel approaches to mapping and monitoring risk. Recent development of trait-based mechanistic models has improved understanding of the temperature dependence of transmission, but model predictions remain challenging to validate in the field. Using West Nile virus (WNV) as a case study, we illustrate the use of a novel remote sensing-based approach to mapping temperature-dependent mosquito and viral traits at high spatial resolution and across the diurnal cycle. We validate the approach using mosquito and WNV surveillance data controlling for other key factors in the ecology of WNV, finding strong agreement between temperature-dependent traits and field-based metrics of risk. Moreover, we find that WNV infection rate in mosquitos exhibits a unimodal relationship with temperature, peaking at ~24.6-25.2°C, in the middle of the 95% credible interval of optimal temperature for transmission of WNV predicted by trait-based mechanistic models. This study represents one of the highest resolution validations of trait-based model predictions, and illustrates the utility of a novel remote sensing approach to predicting mosquito-borne disease risk.

SolBeePopecotox: A Population Model for Pesticide Risk Assessments of Solitary Bees.

In agricultural landscapes, solitary bees occur in a large diversity of species and are important for crop and wildflower pollination. They are distinguished from honey bees and bumble bees by their solitary lifestyle as well as different nesting strategies, phenologies, and floral preferences. Their ecological traits and presence in agricultural landscapes imply potential exposure to pesticides and suggest a need to conduct ecological risk assessments for solitary bees. However, assessing risks to the large diversity of managed and wild bees across landscapes and regions poses a formidable challenge. Population models provide tools to estimate potential population-level effects of pesticide exposures, can support field study design and interpretation, and can be applied to expand study data to untested conditions. We present a population model for solitary bees, SolBeePopecotox, developed for use in the context of ecological risk assessments. The trait-based model extends a previous version with the explicit representation of exposures to pesticides from relevant routes. Effects are implemented in the model using a simplified toxicokinetic-toxicodynamic model, BeeGUTS (GUTS = generalized unified threshold model for survival), adapted specifically for bees. We evaluated the model with data from semifield studies conducted with the red mason bee, Osmia bicornis, in which bees were foraging in tunnels over control and insecticide-treated oilseed rape fields. We extended the simulations to capture hypothetical semifield studies with two soil-nesting species, Nomia melanderi and Eucera pruinosa, which are difficult to test in empirical studies. The model provides a versatile tool for higher-tier risk assessments, for instance, to estimate effects of potential exposures, expanding available study data to untested species, environmental conditions, or exposure scenarios. Environ Toxicol Chem 2024;00:1-17. © 2024 SETAC.

From nutrients to fish: Impacts of mesoscale processes in a global CESM-FEISTY eddying ocean model framework

The ocean sustains ecosystems that are essential for human livelihood and habitability of the planet. The ocean holds an enormous amount of carbon, and serves as a critical source of nutrition for human societies worldwide. Climate variability and change impact marine biogeochemistry and ecosystems. Thus, having state-of-the-art simulations of the ocean, which include marine biogeochemistry and ecosystems, is critical for understanding the role of climate variability and change on the ocean biosphere. Here we present a novel global eddy-resolving (0.1° horizontal resolution) simulation of the ocean and sea ice, including ocean biogeochemistry, performed with the Community Earth System Model (CESM). The simulation is forced by the atmospheric dataset based on the Japanese Reanalysis (JRA-55) product over the 1958–2021 period. We present a novel configuration of the CESM marine ecosystem model in this simulation which includes two zooplankton classes: microzooplankton and mesozooplankton. This novel planktonic food web structure facilitates “offline” coupling with the Fisheries Size and Functional Type (FEISTY) model. FEISTY is a size- and trait-based model of fish functional types contributing to fisheries. We present an evaluation of the ocean biogeochemistry, marine ecosystem (including fish types), and sea ice in this high resolution simulation compared to available observations and a corresponding low resolution (nominal 1°) simulation. Our analysis offers insights into environmental controls on trophodynamics within the ocean. We find that this high resolution simulation provides a realistic reconstruction of nutrients, oxygen, sea ice, plankton and fish distributions over the global ocean. On global and large regional scales the high resolution simulation is comparable to the standard 1° simulation, but on smaller scales, explicitly resolving the mesoscale dynamics is shown to be important for accurately capturing trophodynamic structuring, especially in coastal ecosystems. We show that fine-scale ocean features leave imprints on ocean ecosystems, from plankton to fish, from the tropics to polar regions. This simulation also offers insights on ocean acidification over the past 64 years, as well as how large-scale climate variations may impact upper trophic levels. The data generated by the simulations are publicly available and will be a fruitful community resource for a large variety of oceanographic science questions.

Organismal trade-offs and the pace of planktonic life.

No one is perfect, and organisms that perform well in some habitat or with respect to some tasks, do so at the cost of performance in others: there are inescapable trade-offs. Organismal trade-offs govern the structure and function of ecosystems and attempts to demonstrate and quantify trade-offs have therefore been an important goal for ecologists. In addition, trade-offs are a key component in trait-based ecosystem models. Here, I synthesise evidence of trade-offs in plankton organisms, from bacteria to zooplankton, and show how a slow-fast gradient in life histories emerges. I focus on trade-offs related to the main components of an organism's Darwinian fitness, that is resource acquisition, survival, and propagation. All consumers need to balance the need to eat without being eaten, and diurnal vertical migration, where zooplankton hide at depth during the day to avoid visual predators but at the cost of missed feeding opportunities in the productive surface layer, is probably the best documented result of this trade-off. However, there are many other more subtle but equally important behaviours that similarly are the result of an optimisation of these trade-offs. Most plankton groups have also developed more explicit defence mechanisms, such as toxin production or evasive behaviours that are harnessed in the presence of their predators; the costs of these have often proved difficult to quantify or even demonstrate, partly because they only materialise under natural conditions. Finally, all multicellular organisms must allocate time and resources among growth, reproduction, and maintenance (e.g. protein turnover and DNA repair), and mate finding may compromise both survival and feeding. The combined effects of all these trade-offs is the emergence of a slow-fast gradient in the pace-of-life, likely the most fundamental principle for the organisation of organismal life histories. This crystallisation of trade-offs may offer a path to further simplification of trait-based models of marine ecosystems.

Robustness versus productivity during evolutionary community assembly: short-term synergies and long-term trade-offs.

The realization that evolutionary feedbacks need to be considered to fully grasp ecological dynamics has sparked interest in the effect of evolution on community properties like coexistence and productivity. However, little is known about the evolution of community robustness and productivity along diversification processes in species-rich systems. We leverage the recent structural approach to coexistence together with adaptive dynamics to study such properties and their relationships in a general trait-based model of competition on a niche axis. We show that the effects of coevolution on coexistence are two-fold and contrasting depending on the time scale considered. In the short term, evolution of niche differentiation strengthens coexistence, while long-term diversification leads to niche packing and decreased robustness. Moreover, we find that coevolved communities tend to be on average more robust and more productive than non-evolutionary assemblages. We illustrate how our theoretical predictions echo in observed empirical patterns and the implications of our results for empiricists and applied ecologists. We suggest that some of our results such as the improved robustness of Evolutionarily Stable Communities could be tested experimentally in suitable model systems.

Denitrification and the challenge of scaling microsite knowledge to the globe.

Our knowledge of microbial processes-who is responsible for what, the rates at which they occur, and the substrates consumed and products produced-is imperfect for many if not most taxa, but even less is known about how microsite processes scale to the ecosystem and thence the globe. In both natural and managed environments, scaling links fundamental knowledge to application and also allows for global assessments of the importance of microbial processes. But rarely is scaling straightforward: More often than not, process rates in situ are distributed in a highly skewed fashion, under the influence of multiple interacting controls, and thus often difficult to sample, quantify, and predict. To date, quantitative models of many important processes fail to capture daily, seasonal, and annual fluxes with the precision needed to effect meaningful management outcomes. Nitrogen cycle processes are a case in point, and denitrification is a prime example. Statistical models based on machine learning can improve predictability and identify the best environmental predictors but are-by themselves-insufficient for revealing process-level knowledge gaps or predicting outcomes under novel environmental conditions. Hybrid models that incorporate well-calibrated process models as predictors for machine learning algorithms can provide both improved understanding and more reliable forecasts under environmental conditions not yet experienced. Incorporating trait-based models into such efforts promises to improve predictions and understanding still further, but much more development is needed.

Planting design influences green infrastructure performance: Plant species identity and complementarity in rain gardens.

Green infrastructure's capacity to mitigate urban environmental problems, like heat island effects and excessive stormwater runoff, is partially governed by its plant community. Traditionally, green infrastructure design has focused on engineered aspects, such as substrate and drainage, rather than on the properties of its living components. Since the functioning of these plant assemblages is controlled by ecophysiological processes that differ by species, the identity and relative abundance of the species used will influence green infrastructure performance. We used trait-based modeling to derive principles for the effective composition of green infrastructure plant assemblages, parameterizing our model using the vegetation and ecophysiological traits of the species within New York City rain gardens. Focusing on two plant traits that influence rain garden performance, leaf surface temperature and stomatal conductance, we simulated the cumulative temperature and transpiration for plant communities of differing species composition and diversity. The outcomes of the model demonstrate that plant species composition, species identity, selection effects, and interspecific complementarity increase green infrastructure performance in much the way biodiversity affects ecosystem functioning in natural systems. More diverse assemblages resulted in more consistent transpiration and surface temperatures, with the former showing a positive, saturating curve as diversity increased. While the dominant factors governing individual species leaf temperature were abiotic, transpiration was more influential at the community level, suggesting that plants within diverse communities may be cooler in aggregate than any individual species on its own. This implies green infrastructure should employ a variety of vegetation; particularly plants with different statures and physical attributes, such as low-growing ground covers, erect herbaceous perennials, and shrubs.

Predation in a Microbial World: Mechanisms and Trade-Offs of Flagellate Foraging.

Heterotrophic nanoflagellates are the main consumers of bacteria and picophytoplankton in the ocean and thus play a key role in ocean biogeochemistry. They are found in all major branches of the eukaryotic tree of life but are united by all being equipped with one or a few flagella that they use to generate a feeding current. These microbial predators are faced with the challenges that viscosity at this small scale impedes predator-prey contact and that their foraging activity disturbs the ambient water and thus attracts their own flow-sensing predators. Here, I describe some of the diverse adaptations of the flagellum to produce sufficient force to overcome viscosity and of the flagellar arrangement to minimize fluid disturbances, and thus of the various solutions to optimize the foraging-predation risk trade-off. I demonstrate how insights into this trade-off can be used to develop robust trait-based models of microbial food webs.

Which Fantasy Sports Players Will Bet on Sports? A Trait-Based Predictive Model of Fantasy Sport Players Engaging in Sports Betting

Abstract: Fantasy sports is a favorite form of sports consumption, and sports betting is becoming increasingly more popular and accessible. Following the recent Supreme Court decision concerning legalization, many fantasy sports operators are now incorporating sports betting into their platforms to capitalize on this opportunity. However, little research examines the motivations behind why some fantasy players bet on sports while others do not. Using a trait-based model, we investigate the likelihood that fantasy sports players would also engage in sports betting, revealing that traits are predictive of sports betting behavior. Given that traits are not readily observable or easily accessible, a proxy model incorporating commonly available demographic variables is also specified and estimated. Support for our proxy model is found in the widely used proxy means test devised to identify and target those eligible for social programs. As a result, this research is both theoretically driven and actionable.

How predictable are mass extinction events?

Many modern extinction drivers are shared with past mass extinction events, such as rapid climate warming, habitat loss, pollution and invasive species. This commonality presents a key question: can the extinction risk of species during past mass extinction events inform our predictions for a modern biodiversity crisis? To investigate if it is possible to establish which species were more likely to go extinct during mass extinctions, we applied a functional trait-based model of extinction risk using a machine learning algorithm to datasets of marine fossils for the end-Permian, end-Triassic and end-Cretaceous mass extinctions. Extinction selectivity was inferred across each individual mass extinction event, before testing whether the selectivity patterns obtained could be used to 'predict' the extinction selectivity exhibited during the other mass extinctions. Our analyses show that, despite some similarities in extinction selectivity patterns between ancient crises, the selectivity of mass extinction events is inconsistent, which leads to a poor predictive performance. This lack of predictability is attributed to evolution in marine ecosystems, particularly during the Mesozoic Marine Revolution, associated with shifts in community structure alongside coincident Earth system changes. Our results suggest that past extinctions are unlikely to be informative for predicting extinction risk during a projected mass extinction.

Introducing an attitude-based approach to emotional intelligence.

Emotional intelligence (EI) was originally conceived as an ability, followed soon after by mixed, competency and trait theoretical models, broadly described as emotional efficacies (EE). Several models have attempted to integrate both approaches, with different views on whether EI and EE operate in sequence or parallel. One reason for this may be that EE constructs are given the same ontological status whether they represent underlying attitudes, such as self-regard, or behavioral competencies, such as assertiveness. In this paper, it is proposed that attitudes may predominantly act as underlying antecedents of ability-EI and behavioral-EE. Five benefits of this approach are drawn out that help to address some key concerns with current models and measures of EI and EE. First, the inclusion of implicit and explicit attitudes within integrated models of EI/EE would support the dual-processing of conscious and automated processes. From this, an attitude-based dual-processing framework for EI/EE is recommended. Second, the concept of Unconditional Positive Regard (UPR) for self and others, is identified as a potential attitude that may underpin the two core pillars of intrapersonal and interpersonal EI/EE. Third, UPR attitudes would provide an ethical basis for EI/EE that may support ethical and prosocial behavior. Fourth, UPR attitudes may differentiate between the optimal and sub-optimal elements of EI/EE. Fifth, an attitude-based approach to EI/EE may be more aligned with EI/EE being developmental than are the more static ability or trait-based models of EI/EE.

Examining the Construct Validity of Borderline Personality Traits Using Familial Aggregation and Other External Validators.

Numerous studies have questioned the reliability and validity of borderline personality disorder's (BPD) categorical conceptualization. DSM-5 Section III's alternative trait-based model of BPD may better capture borderline pathology, but aspects of its validity should be further established. Thus, the authors examined whether a latent BPD factor derived from Section III traits exhibits (1) familial aggregation among siblings and (2) association with constructs related to borderline pathology. The authors also tested whether gender moderated associations. A total of 498 community-recruited adults completed the Personality Inventory for DSM-5, a behavioral assessment of risk-taking, and reported their history of childhood maltreatment, substance use, nonsuicidal self-injury, and suicidal ideation. Familial aggregation was assessed among 232 sibling pairs. Siblings' BPD scores were significantly correlated and most external validators were significantly associated with BPD, with the exception of risk-taking. Results did not vary by gender. Findings further support the construct validity of Section III's BPD trait profile.

Applying Trait-Based Modeling to Achieve Functional Targets during the Ecological Restoration of an Arid Mine Area

(1) Background: Because ecosystem degradation has become a global phenomenon which seriously affects the health of natural ecosystems and human well-being, restoration of degraded ecosystems has attracted increasing attention. However, many of the methods used in current ecological restoration work have rarely combined ecological restoration practices with the quantitative goal of restoring ecosystem function. (2) Methods: In this study, based on the conceptual framework of response-effect traits and Community Assembly by Trait Selection model (CATS model), a restoration strategy for a degraded abandoned mine in Wuhai City, China has been provided. This restoration strategy connected the ecosystem function targets to the appropriate recovery species and their required abundances. (3) Results: The results showed that a relative abundance ratio of 8:2 for S. grandis to B. dasyphylla was best for a shady slope, while a 6:4 ratio of K. tragus to B. dasyphylla was best for repair on a sunny slope of the degraded mine area. (4) Conclusion: This study provides a typical example of applying ecological theory in practice that will be useful for current and future studies and applications. This approach will ensure that governance efforts to restore degraded ecosystems are effective and efficient.

A trait-based typification of urban forests as nature-based solutions

Urban forests as nature-based solutions (UF-NBS) are important tools for climate change adaptation and sustainable development. However, achieving both effective and sustainable UF-NBS solutions requires diverse knowledge. This includes knowledge on UF-NBS implementation, on the assessment of their environmental impacts in diverse spatial contexts, and on their management for the long-term safeguarding of delivered benefits. A successful integration of such bodies of knowledge demands a systematic understanding of UF-NBS. To achieve such an understanding, this paper presents a conceptual UF-NBS model obtained through a semantic, trait-based modelling approach. This conceptual model is subsequently implemented as an extendible, re-usable and interoperable ontology. In so doing, a formal, trait-based vocabulary on UF-NBS is created, that allows expressing spatial, morphological, physical, functional, and institutional UF-NBS properties for their typification and a subsequent integration of further knowledge and data. Thereby, ways forward are opened for a more systematic UF-NBS impact assessment, management, and decision-making.

Plankton energy flows using a global size-structured and trait-based model

Plankton community models are critical tools for understanding the processes that shape marine plankton communities, how plankton communities impact biogeochemical cycles, and the feedbacks between community structure and function. Here, using the flexible Marine Biogeochemistry Library (MARBL), we present the Size-based Plankton ECological TRAits (MARBL-SPECTRA) model, which is designed to represent a diverse plankton community while remaining computationally tractable. MARBL-SPECTRA is composed of nine phytoplankton and six zooplankton size classes represented using allometric scaling relationships for physiological traits and interactions within multiple functional types. MARBL-SPECTRA is embedded within the global ocean component of the Community Earth System Model (CESM) and simulates large-scale, emergent patterns in phytoplankton growth limitation, plankton phenology, plankton generation time, and trophic transfer efficiency. The model qualitatively reproduces observed global patterns of surface nutrients, chlorophyll biomass, net primary production, and the biogeographies of a range of plankton size classes. In addition, the model simulates how predator:prey dynamics and trophic efficiency vary across gradients in total ecosystem productivity. Shorter food chains that export proportionally more carbon from the surface to the ocean interior occur in productive, eutrophic regions, whereas in oligotrophic regions, the food chains are relatively long and export less organic matter from the surface. The union of functional type modeling with size-resolved, trait-based modeling approaches allows MARBL-SPECTRA to capture both large-scale elemental cycles and the structure of planktonic food webs affecting trophic transfer efficiency.

Integrating Genome-Resolved Metagenomics with Trait-Based Process Modeling to Determine Biokinetics of Distinct Nitrifying Communities within Activated Sludge.

Conventional bioprocess models for wastewater treatment are based on aggregated bulk biomass concentrations and do not incorporate microbial physiological diversity. Such a broad aggregation of microbial functional groups can fail to predict ecosystem dynamics when high levels of physiological diversity exist within trophic guilds. For instance, functional diversity among nitrite-oxidizing bacteria (NOB) can obfuscate engineering strategies for their out-selection in activated sludge (AS), which is desirable to promote energy-efficient nitrogen removal. Here, we hypothesized that different NOB populations within AS can have different physiological traits that drive process performance, which we tested by estimating biokinetic growth parameters using a combination of highly replicated respirometry, genome-resolved metagenomics, and process modeling. A lab-scale AS reactor subjected to a selective pressure for over 90 days experienced resilience of NOB activity. We recovered three coexisting Nitrospira population genomes belonging to two sublineages, which exhibited distinct growth strategies and underwent a compositional shift following the selective pressure. A trait-based process model calibrated at the NOB genus level better predicted nitrite accumulation than a conventional process model calibrated at the NOB guild level. This work demonstrates that trait-based modeling can be leveraged to improve our prediction, control, and design of functionally diverse microbiomes driving key environmental biotechnologies.

Species-specific traits predict whole-assemblage detritus processing by pond invertebrates.

Functional trait diversity determines if ecosystem processes are sensitive to shifts in species abundances or composition. For example, trait variation suggests detritivores process detritus at different rates and make different contributions to whole-assemblage processing, which could be sensitive to compositional shifts. Here, we used a series of microcosm experiments to quantify species-specific coarse and fine particulate organic matter (CPOM and FPOM) processing for ten larval caddisfly species and three non-caddisfly species in high-elevation wetlands. We then compared trait-based models including life history, dietary, and extrinsic traits to determine which traits explained interspecific variation in detritus processing. Finally, we compared processing by mixed caddisfly assemblages in microcosms and natural ponds to additive predictions based on species-specific processing to determine if single-species effects are additive in multi-species assemblages. We found considerable interspecific variation in biomass-specific CPOM (13-fold differences) and FPOM (8-fold differences) processing. Furthermore, on a mass-specific basis, amphipods, chironomids, and caddisflies processed similar amounts of detritus, suggesting non-shredder taxa could process more than previously recognized. Trait models including dietary percent detritus, development rate, body size, and wetland hydroperiod explained 81 and 57% of interspecific variation in CPOM and FPOM processing, respectively. Finally, species-specific additive predictions were strikingly similar to mixed-assemblage processing in microcosms and natural ponds, with the largest difference being a 15% overestimate. Thus, additivity of species-specific processing suggests single-species rates may be useful for understanding functional consequences of shifting assemblages, and a trait-based approach to predicting species-specific processing could support generating additive predictions of whole-assemblage processing.

Beyond body size-new traits for new heights in trait-based modelling of predator-prey dynamics.

Food webs map feeding interactions among species, providing a valuable tool for understanding and predicting community dynamics. Using species’ body sizes is a promising avenue for parameterizing food-web models, but such approaches have not yet been able to fully recover observed community dynamics. Such discrepancies suggest that traits other than body size also play important roles. For example, differences in species’ use of microhabitat or non-consumptive effects of intraguild predators may affect dynamics in ways not captured by body size. In Laubmeier et al. (2018), we developed a dynamic food-web model incorporating microhabitat and non-consumptive predator effects in addition to body size, and used simulations to suggest an optimal sampling design of a mesocosm experiment to test the model. Here, we perform the mesocosm experiment to generate empirical time-series of insect herbivore and predator abundance dynamics. We minimize least squares error between the model and time-series to determine parameter values of four alternative models, which differ in terms of including vs excluding microhabitat use and non-consumptive predator-predator effects. We use both statistical and expert-knowledge criteria to compare the models and find including both microhabitat use and non-consumptive predator-predator effects best explains observed aphid and predator population dynamics, followed by the model including microhabitat alone. This ranking suggests that microhabitat plays a larger role in driving population dynamics than non-consumptive predator-predator effects, although both are clearly important. Our results illustrate the importance of additional traits alongside body size in driving trophic interactions. They also point to the need to consider trophic interactions and population dynamics in a wider community context, where non-trophic impacts can dramatically modify the interplay between multiple predators and prey. Overall, we demonstrate the potential for utilizing traits beyond body size to improve trait-based models and the value of iterative cycling between theory, data and experiment to hone current insights into how traits affect food-web dynamics.

Trait-Based Model Reproduces Patterns of Population Structure and Diversity of Methane Oxidizing Bacteria in a Stratified Lake

In stratified lakes, methane oxidizing bacteria are critical methane converters that significantly reduce emissions of this greenhouse gas to the atmosphere. Efforts to better understand their ecology uncovered a surprising diversity, vertical structure, and seasonal succession. It is an open question how this diversity has to be considered in models of microbial methane oxidation. Likewise, it is unclear to what extent simple microbial traits related to the kinetics of the oxidation process and temperature optimum, suggested by previous studies, suffice to understand the observed ecology of methane oxidizing bacteria. Here we incorporate niche partitioning in a mechanistic model of seasonal lake mixing and microbial methane oxidation in a stratified lake. Can we model MOB diversity and niche partitioning based on differences in methane oxidation kinetics and temperature adaptation? We found that our model approach can closely reproduce diversity and niche preference patterns of methanotrophs that were observed in seasonally stratified lakes. We show that the combination of trait values resulting in coexisting methanotroph communities is limited to very confined regions within the parameter space of potential trait combinations. However, our model also indicates that the sequence of community assembly, and variations in the stratification and mixing behavior of the lake result in different stable combinations. A scenario analysis introducing variable mixing conditions showed that annual weather conditions and the pre-existing species also affect the developing stable methanotrophic species composition of the lake. Both, effect of pre-existing species and the environmental impact suggest that the MOB community in lakes may differ from year to year, and a stable community may never truly occur. The model further shows that there are always better-adapted species in the trait parameter space that would destabilize and replace an existing stable community. Thus, natural selection may drive trait values into the specific configurations observed in nature based on physiological limits and tradeoffs between traits.