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The maximum photosynthetic carboxylation rate (V cmax) is an influential plant trait that has multiple scaling hypotheses, which is a source of uncertainty in predictive understanding of global gross primary production (GPP). Four trait-scaling hypotheses (plant functional type, nutrient limitation, environmental filtering, and plant plasticity) with nine specific implementations were used to predict global V cmax distributions and their impact on global GPP in the Sheffield Dynamic Global Vegetation Model (SDGVM). Global GPP varied from 108.1 to 128.2 PgC yr−1, 65% of the range of a recent model inter-comparison of global GPP. The variation in GPP propagated through to a 27% coefficient of variation in net biome productivity (NBP). All hypotheses produced global GPP that was highly correlated (r = 0.85–0.91) with three proxies of global GPP. Plant functional type-based nutrient limitation, underpinned by a core SDGVM hypothesis that plant nitrogen (N) status is inversely related to increasing costs of N acquisition with increasing soil carbon, adequately reproduced global GPP distributions. Further improvement could be achieved with accurate representation of water sensitivity and agriculture in SDGVM. Mismatch between environmental filtering (the most data-driven hypothesis) and GPP suggested that greater effort is needed understand V cmax variation in the field, particularly in northern latitudes.

In 300 BC Ancient Greece, Theophrastus was one of the first to organize the diversity of plant life on Earth into categories of function and use (Theophrastus, 1916). Currently, scientists are still working to simplify the vast array of plant species and forms in order to distill general features of plant function, structure, and strategy.

Abstract Oxygen minimum zones (OMZs) are often characterised by nitrogen-to-phosphorus (N:P) ratios far lower than the canonical Redfield ratio. Whereas the importance of variable stoichiometry in phytoplankton has long been recognised, variations in zooplankton stoichiometry have received much less attention. Here we combine observations from two shipboard mesocosm nutrient enrichment experiments with an optimality-based plankton ecosystem model, designed to elucidate the roles of different trophic levels and elemental stoichiometry. Pre-calibrated microzooplankton parameter sets represent foraging strategies of dinoflagellates and ciliates in our model. Our results suggest that remineralisation is largely driven by omnivorous ciliates and dinoflagellates, and highlight the importance of intraguild predation. We hypothesize that microzooplankton respond to changes in food quality in terms of nitrogen-to-carbon (N:C) ratios, rather than nitrogen-to-phosphorus (N:P) ratios, by allowing variations in their phosphorus-to-carbon (P:C) ratio. Our results point towards an important biogeochemical role of flexible microzooplankton stoichiometry.

Urbanization and global climate change can profoundly alter biological systems, yet scientists often analyze their effects separately. We test how the timing of life cycle events (phenology) is jointly influenced by these two components of global change. To do so, we use a long-term phenological data set of 20 common butterfly species from 83 sites across the state of Ohio, USA, with sites that range from rural undeveloped areas to moderately sized cities. These sites span a latitudinal gradient in mean temperature of several °C, mimicking the range of projected global climate warming effects through the end of the century. Although shifts toward earlier phenology are typical of species' responses to either global climate change or urbanization, we found that their interaction delayed several Ohio butterfly species' first appearance and peak abundance phenology. Exploitative species exhibited smaller delays in first appearance and peak abundance phenology in areas that were urbanized and geographically warm. Our results show that phenological responses to urbanization are contingent upon geographic variation in temperature, and that the impacts of urbanization and global climate change should be considered simultaneously when developing forecasts of biological responses to environmental change.

We develop a trait-based model founded on the hypothesis that biological systems evolve and organize to maximize entropy production by dissipating chemical and electromagnetic free energy over longer time scales than abiotic processes by implementing temporal strategies. A marine food web consisting of phytoplankton, bacteria, and consumer functional groups is used to explore how temporal strategies, or the lack thereof, change entropy production in a shallow pond that receives a continuous flow of reduced organic carbon plus inorganic nitrogen and illumination from solar radiation with diel and seasonal dynamics. Results show that a temporal strategy that employs an explicit circadian clock produces more entropy than a passive strategy that uses internal carbon storage or a balanced growth strategy that requires phytoplankton to grow with fixed stoichiometry. When the community is forced to operate at high specific growth rates near 2 d−1, the optimization-guided model selects for phytoplankton ecotypes that exhibit complementary for winter versus summer environmental conditions to increase entropy production. We also present a new type of trait-based modeling where trait values are determined by maximizing entropy production rather than by random selection.

Knowledge of spatial distribution patterns of biodiversity is key to evaluate and ensure ocean integrity and resilience. Especially for the deep ocean, where in situ monitoring requires sophisticated instruments and considerable financial investments, modeling approaches are crucial to move from scattered data points to predictive continuous maps. Those modeling approaches are commonly run on the macrobial level, but spatio-temporal predictions of host-associated microbiomes are not being targeted. This is especially problematic as previous research has highlighted that host-associated microbes may display distribution patterns that are not perfectly correlated not only with host biogeographies, but also with other factors, such as prevailing environmental conditions. We here establish a new simulation approach and present predicted spatio-temporal distribution patterns of deep-sea sponge and coral microbiomes, making use of a combination of environmental data, host data, and microbiome data. This approach allows predictions of microbiome spatio-temporal distribution patterns on scales that are currently not covered by classical sampling approaches at sea. In summary, our presented predictions allow (i) identification of microbial biodiversity hotspots in the past, present, and future, (ii) trait-based predictions to link microbial with macrobial biodiversity, and (iii) identification of shifts in microbial community composition (key taxa) across environmental gradients and shifting environmental conditions.

Mesozooplankton play a key role in marine ecosystems as they modulate the transfer of energy from phytoplankton to large marine organisms. In addition, they directly influence the oceanic cycles of carbon and nutrients through vertical migrations, fecal pellet production, respiration, and excretion. Mesozooplankton are mainly made up of metazoans, which undergo important size changes during their life cycle, resulting in significant variations in metabolic rates. However, most marine biogeochemical models represent mesozooplankton as protists-like organisms. Here, we study the potential caveats of this simplistic representation by using a chemostat-like zero-dimensional model with four different Nutrient-Phytoplankton-Zooplankton configurations in which the description of mesozooplankton ranges from protist-type organisms to using a size-based formulation including explicit reproduction and ontogenetic growth. We show that the size-based formulation strongly impacts mesozooplankton. First, it generates a delay of a few months in the response to an increase in food availability. Second, the increase in mesozooplankton biomass displays much larger temporal variations, in the form of successive cohorts, because of the dependency of the ingestion rate to body size. However, the size-based formulation does not affect smaller plankton or nutrient concentrations. A proper assessment of these top-down effects would require implementing our size-resolved approach in a 3-dimensional biogeochemical model. Furthermore, the bottom-up effects on higher trophic levels resulting from the significant changes in the temporal dynamics of mesozooplankton could be estimated in an end-to-end model coupling low and high trophic levels.

The theoretical exploration of how diversity influences stability has traditionally been approached by species-centric methods. Here we offer an alternative approach to the diversity–stability problem by examining the stability and dynamics of size and trait distributions of individuals. The analysis is performed by comparing the properties of two size spectrum models. The first model considers all individuals as belonging to the same “average” species, i.e., without a description of diversity. The second model introduces diversity by further considering individuals by a trait, here asymptotic body size. The dynamic properties of the models are described by a stability analysis of equilibrium solutions and by the non-equilibrium dynamics. We find that the introduction of trait diversity expands the set of parameters for which the equilibrium is stable and, if the community is unstable, makes the oscillations smaller, slower, and more regular. The stabilizing mechanism is the variation in growth rate between individuals with the same body size but different trait values.

Chemostat experiments are employed to study predator-prey and other trophic interactions, frequently using phytoplankton-zooplankton systems. These experiments often use population dynamics as fingerprints of ecological and evolutionary processes, assuming that the contributions of all major actors to these dynamics are known. However, bacteria are often neglected although they are frequently present. We argue that even without external carbon sources bacteria may affect the experimental outcomes depending on experimental conditions and the physiological traits of bacteria, phytoplankton and zooplankton. Using a static carbon flux model and a dynamic simulation model we predict the minimum and maximum impact of bacteria on phytoplankton-zooplankton population dynamics. Under bacteria-suppressing conditions, we find that the effect of bacteria is indeed negligible and their omission justified. Under bacteria-favouring conditions, however, bacteria may strongly affect average biomasses. Furthermore, the population dynamics may become highly complex resulting in wrong conclusions if bacteria are not considered. Our model results provide suggestions to reduce the bacterial impact experimentally. Next to optimizing experimental conditions (e.g. the dilution rate) the appropriate choice of the zooplankton predator is decisive. Counterintuitively, bacteria have a larger impact if they are not ingested by the predator as high bacterial biomasses and complex population dynamics arise via competition for nutrients with the phytoplankton. Only if the predator is at least partly bacterivorous the impact of bacteria is minimized. Our results help to improve both the design of chemostat experiments and their interpretation and thus advance the study of ecological and evolutionary processes in aquatic food webs.

1. Recovering biological diversity and ecosystem functioning are primary objectives of ecological restoration, yet these outcomes are often unpredictable. Assessments based on functional traits may help with interpreting variability in both community composition and ecosystem functioning because of their mechanistic and generalizable nature. This promise remains poorly realized, however, because tests linking environmental conditions, functional traits, and ecosystem functioning in restoration are rare. 2. Here, we provide such a test through what is to our knowledge the first empirical application of the 'response-effect trait framework' to restoration. This framework provides a trait-based bridge between community assembly and ecosystem functioning by describing how species respond to environmental conditions based on traits and how the traits of species affect ecosystem functioning. 3. Our study took place across 29 prairies restored from former agricultural fields in southwestern Michigan. We considered how environmental conditions affect ecosystem functioning through and independently of measured functional traits. To do so, we paired field-collected trait data with data on plant community composition and measures of ecosystem functioning and used structural equation modelling to determine relationships between environmental conditions, community-weighted means of functional traits and ecosystem functioning. 4. Environmental conditions were predictive of trait composition. Sites restored directly from tillage (as opposed to those allowed to fallow) supported taller species with larger seeds and higher specific leaf area (SLA). Site age and fire frequency were both negatively related to SLA. We also found a positive relationship between soil moisture and SLA. 5. Both trait composition and environmental conditions predicted ecosystem functioning, but these relationships varied among the measured functions. Pollination mode (animal pollination) increased and fire frequency decreased floral resource availability, seed mass had a negative effect on below-ground biomass production, and vegetative height increased decomposition rate. Soil moisture and fire frequency both increased while site age decreased above-ground biomass production, and site age and soil moisture both increased decomposition rate. 6. Synthesis and applications. Our results suggest that both trait composition and environmental conditions play a role in shaping ecosystem function during restoration, and the importance of each is dependent on the function of interest. Because of this, environmental heterogeneity will be necessary to promote multiple ecosystem functions across restored landscapes. A trait-based approach to restoration can aid interpretation of variable outcomes through insights into community assembly and ecosystem functioning.