Identifying Personal and Social Drivers of Dietary Patterns: An Agent-Based Model of Dutch Consumer Behavior

Understanding the drivers of dietary decisions is crucial for encouraging and facilitating environmentally sustainable consumption patterns. Previous work has focused on the utility that consumers place on factors such as price, quality, and ethics when making dietary decisions, or on the effects of personal values and peer influence on consumption of individual products. However, less attention has been paid to the interacting roles of values, perceptions, and social networks in dietary decision-making, and how these relate to mismatches between values and diet choice. Here, we develop an agent-based model of individual consumers making choices between five possible diets: omnivore, flexitarian, pescatarian, vegetarian, or vegan. Each consumer makes decisions based on personal constraints and values, and their perceptions of how well each diet matches with those values. Consumers can also be influenced by each other’s perceptions via interaction across three social networks: household members, friends, and acquaintances. We show that consumers primarily make decisions based on cost and taste, even when they value ethics and health, and illustrate three potential causes of the ‘attitude-behavior gap’ between ethical motivations and diet choice. This highlights the potential for both policy-driven changes to pricing structures, and increased awareness around sustainability and health attributes of different diets, in overcoming constraints and misperceptions to facilitate transitions to sustainable diets

An Exploration of Drivers of Opinion Dynamics

Our ability to deal with external changes is determined by our collective willingness to transform and adopt new technologies. These factors are driven by people’s opinion on the change itself and the proposed policies. Humans constantly update their opinion by integrating new information they hear with their values, which helps them make a judgement about that new information. Here, we create an agent-based model that explicitly incorporates the concept of values to explore possible drivers of opinion dynamics. In the model, we explore several factors and perform local and global sensitivity analysis to test their individual and interaction effects. We find that consensus formation in the model is mainly determined by factors related to (1) the amount of stochasticity in the opinion updating procedure and (2) the relative ease with which old links are removed and new links are created. Our results demonstrate how opinions and values may co-evolve. Furthermore, they may help in understanding human responses to new policies such as covid-related restrictions or calls to shift to a more plant-based diet

A dynamic energy budget (DEB) model to describe population dynamics of the marine cyanobacterium Prochlorococcus marinus

Small but numerically dominant species such as the cyanobacteria Prochlorococcus play a pivotal role in major nutrient cycles. To understand how Prochlorococcus populations affect nutrient flows, we present and analyze two alternative population models. These models − one including over-shading effects and one not − are based on Dynamic Energy Budget (DEB) theory to describe how growth may be affected by the availability of CO2 and/or inorganic nutrients and by changes in light conditions. In these models individuals have reserves for C, N, and P from which fluxes for maintenance and growth are mobilized. Time series data from laboratory studies of growth under three different nutrient concentrations are used to calibrate the models. The model with carbon, nitrogen and phosphate as limiting factors can reasonably describe the growth in the low N and low P media experiments, while the model with over-shading gives a proper description of the growth under all tested experimental conditions. Modeled C:N:P ratios are within range of reported in scientific literature ones and similar to measured ratios. The results suggest that (1) reserves play a critical role for cyanobacteria to thrive under the often oligotrophic conditions in which they live, and (2) over-shading has a considerable effect as co-limiting factor on the growth of cyanobacteria. We argue that an individual DEB-based model in which multiple nutrient limitations are presented can be used to successfully describe cyanobacterium growth patterns in batch cultures.</p

A dynamic energy budget (DEB) model to describe population dynamics of the marine cyanobacterium Prochlorococcus marinus

Small but numerically dominant species such as the cyanobacteria Prochlorococcus play a pivotal role in major nutrient cycles. To understand how Prochlorococcus populations affect nutrient flows, we present and analyze two alternative population models. These models − one including over-shading effects and one not − are based on Dynamic Energy Budget (DEB) theory to describe how growth may be affected by the availability of CO2 and/or inorganic nutrients and by changes in light conditions. In these models individuals have reserves for C, N, and P from which fluxes for maintenance and growth are mobilized. Time series data from laboratory studies of growth under three different nutrient concentrations are used to calibrate the models. The model with carbon, nitrogen and phosphate as limiting factors can reasonably describe the growth in the low N and low P media experiments, while the model with over-shading gives a proper description of the growth under all tested experimental conditions. Modeled C:N:P ratios are within range of reported in scientific literature ones and similar to measured ratios. The results suggest that (1) reserves play a critical role for cyanobacteria to thrive under the often oligotrophic conditions in which they live, and (2) over-shading has a considerable effect as co-limiting factor on the growth of cyanobacteria. We argue that an individual DEB-based model in which multiple nutrient limitations are presented can be used to successfully describe cyanobacterium growth patterns in batch cultures.</p

A potential feedback loop underlying glacial-interglacial cycles

The sawtooth-patterned glacial-interglacial cycles in the Earth’s atmospheric temperature are a well-known, though poorly understood phenomenon. Pinpointing the relevant mechanisms behind these cycles will not only provide insights into past climate dynamics, but also help predict possible future responses of the Earth system to changing CO2 levels. Previous work on this phenomenon suggests that the most important underlying mechanisms are interactions between marine biological production, ocean circulation, temperature and dust. So far, interaction directions (i.e., what causes what) have remained elusive. In this paper, we apply Convergent Cross-Mapping (CCM) to analyze paleoclimatic and paleoceanographic records to elucidate which mechanisms proposed in the literature play an important role in glacial-interglacial cycles, and to test the directionality of interactions. We find causal links between ocean ventilation, biological productivity, benthic δ18O and dust, consistent with some but not all of the mechanisms proposed in the literature. Most importantly, we find evidence for a potential feedback loop from ocean ventilation to biological productivity to climate back to ocean ventilation. Here, we propose the hypothesis that this feedback loop of connected mechanisms could be the main driver for the glacial-interglacial cycles.</p

Spatial early warning signals for impending regime shifts: A practical framework for application in real‐world landscapes

Prediction of ecosystem response to global environmental change is a pressing scientific challenge of major societal relevance. Many ecosystems display nonlinear responses to environmental change, and may even undergo practically irreversible ‘regime shifts’ that initiate ecosystem collapse. Recently, early warning signals based on spatiotemporal metrics have been proposed for the identification of impending regime shifts. The rapidly increasing availability of remotely sensed data provides excellent opportunities to apply such model‐based spatial early warning signals in the real world, to assess ecosystem resilience and identify impending regime shifts induced by global change. Such information would allow land‐managers and policy makers to interfere and avoid catastrophic shifts, but also to induce regime shifts that move ecosystems to a desired state. Here, we show that the application of spatial early warning signals in real‐world landscapes presents unique and unexpected challenges, and may result in misleading conclusions when employed without careful consideration of the spatial data and processes at hand. We identify key practical and theoretical issues and provide guidelines for applying spatial early warning signals in heterogeneous, real‐world landscapes based on literature review and examples from real‐world data. Major identified issues include (1) spatial heterogeneity in real‐world landscapes may enhance reversibility of regime shifts and boost landscape‐level resilience to environmental change (2) ecosystem states are often difficult to define, while these definitions have great impact on spatial early warning signals and (3) spatial environmental variability and socio‐economic factors may affect spatial patterns, spatial early warning signals and associated regime shift predictions. We propose a novel framework, shifting from an ecosystem perspective towards a landscape approach. The framework can be used to identify conditions under which resilience assessment with spatial remotely sensed data may be successful, to support well‐informed application of spatial early warning signals, and to improve predictions of ecosystem responses to global environmental change

Yield dissection models to improve yield : A case study in tomato

Yield as a complex trait may either be genetically improved directly, by identifying QTLs contributing to yield, or indirectly via improvement of underlying components, where parents contribute complementary alleles to different components. We investigated the utility of two yield dissection models in tomato for identifying promising yield components and corresponding QTLs. In a harvest dissection, marketable yield was the product of number of fruits and individual fruit fresh weight. In a biomass dissection, total yield was the product of fruit fresh-dry weight ratio and total fruit dry weight. Data came from a greenhouse experiment with a population of hybrids formed from four-way RILs. Trade-offs were observed between the component traits in both dissections. Genetic improvements were possible by increasing the number of fruits and the total fruit dry weight to offset losses in fruit fresh weight and fruit fresh-dry weight ratio. Most yield QTLs colocalized with component QTLs, offering options for the construction of high-yielding genotypes. An analysis of QTL allelic effects in relation to parental origin emphasized the complementary role of the parents in the construction of desired genotypes. Multi-QTL models were used for the comparison of yield predictions from yield QTLs and predictions from the products of components following multi-QTL models for those components. Component QTLs underlying dissection models were able to predict yield with the same accuracy as yield QTLs in direct predictions. Harvest and biomass yield dissection models may serve as useful tools for yield improvement in tomato by either or both of combining individual component QTLs and multi-QTL component predictions. </p

Emerging forest–peatland bistability and resilience of European peatland carbon stores

Northern peatlands store large amounts of carbon. Observations indicate that forests and peatlands in northern biomes can be alternative stable states for a range of landscape settings. Climatic and hydrological changes may reduce the resilience of peatlands and forests, induce persistent shifts between these states, and release the carbon stored in peatlands. Here, we present a dynamic simulation model constrained and validated by a wide set of observations to quantify how feedbacks in water and carbon cycling control resilience of both peatlands and forests in northern landscapes. Our results show that 34% of Europe (area) has a climate that can currently sustain existing rainwater-fed peatlands (raised bogs). However, raised bog initiation and restoration by water conservation measures after the original peat soil has disappeared is only possible in 10% of Europe where the climate allows raised bogs to initiate and outcompete forests. Moreover, in another 10% of Europe, existing raised bogs (concerning ∼20% of the European raised bogs) are already affected by ongoing climate change. Here, forests may overgrow peatlands, which could potentially release in the order of 4% (∼24 Pg carbon) of the European soil organic carbon pool. Our study demonstrates quantitatively that preserving and restoring peatlands requires looking beyond peatland-specific processes and taking into account wider landscape-scale feedbacks with forest ecosystems