The resources that organisms depend on often fluctuate over time, and a variety of common traits are thought to be adaptations to variable resource supply. To understand the trait structure of communities, it is necessary to understand the functional trade-offs that determine what trait combinations are possible and which species can persist and coexist in a given environment. We compare traits across phytoplankton species in order to test for proposed trade-offs between maximum growth rate, equilibrium competitive ability for phosphorus (P), and ability to store P. We find evidence for a three-way trade-off between these traits, and we use empirical trait covariation to parameterize a mechanistic model of competition under pulsed P supply. The model shows that different strategies are favored under different conditions of nutrient supply regime, productivity, and mortality. Furthermore, multiple strategies typically coexist, and the range of traits that persist in the model is similar to the range of traits found in real species. These results suggest that mechanistic models informed by empirical trait variation, in combination with data on the trait structure of natural communities, will play an important role in uncovering the mechanisms that underlie the diversity and structure of ecological communities.

Aquatic ecosystems and processes exhibit a high degree of spatial and temporal heterogeneity, which presents significant challenges for their monitoring. In this paper we report a novel underwater robot, called gliding robotic fish, as an emerging platform for mobile sensing in aquatic environments that can potentially provide high spatiotemporal coverage. The robot represents a hybrid of an underwater glider and a robotic fish, and is capable of exploiting gliding to achieve energy-efficient locomotion while using a fish-like active tail to achieve high maneuverability. Preliminary field-test results are presented, where the robot was used to sample the Kalamazoo River and the Wintergreen Lake in Michigan for concentrations of crude oil and harmful algae, respectively.

Trait-based approaches provide a mechanistic framework to understand and predict the structure and functioning of microbial communities. Resource utilization traits and tradeoffs are among key microbial traits that describe population dynamics and competition among microbes. Several important trade-offs have been identified for prokaryotic and eukaryotic microbial taxa that define contrasting ecological strategies and contribute to species coexistence and diversity. The shape, dimensionality, and hierarchy of trade-offs may determine coexistence patterns and need to be better characterized. Laboratory measured resource utilization traits can be used to explain temporal and spatial structure and dynamics of natural microbial communities and predict biogeochemical impacts. Global environmental change can alter microbial community composition through altering resource utilization by different microbes and, consequently, may modify biogeochemical impacts of microbes.

Light-dependent growth of phytoplankton is a fundamental process in marine ecosystems, but we lack a comprehensive view of how light utilization traits vary across genotypes and species, and how this variation is structured by cell size, taxonomy, and environmental gradients. Here, we compile 308 growth-irradiance experiments performed on 119 species of marine phytoplankton from all major functional groups, and characterize growth-irradiance relationships in terms of the initial slope of the growth-irradiance curve (), the optimal irradiance above which growth declines (I-opt), and the maximum growth rate ((max)). We find that declines with increasing cell size, although cell size appears to be a weak constraint on this trait. There are significant differences across taxa in and (max), with dinoflagellates, raphidophytes, and diazotrophs having the lowest values for both traits, and Phaeocystis spp. and diatoms having relatively high values. I-opt does not vary among taxonomic groups, and all traits exhibit large variation within most groups. Open-ocean isolates tend to have higher , lower I-opt, and lower (max) than coastal isolates, implying adaptation to low light and low productivity. The three traits are correlated across species such that and I-opt are negatively related while (max) is positively correlated with both of these traits. There is some evidence that high carries a cost of high N demand even when nitrogen (not light) is limiting. The results elucidate contrasting light-related ecological strategies across phytoplankton and should help improve the parameterization of major functional groups in biogeochemical models.

Phytoplankton are key players in the global carbon cycle, contributing about half of global primary productivity. Within the phytoplankton, functional groups (characterized by distinct traits) have impacts on other major biogeochemical cycles, such as nitrogen, phosphorus and silica. Changes in phytoplankton community structure, resulting from the unique environmental sensitivities of these groups, may significantly alter elemental cycling from local to global scales. We review key traits that distinguish major phytoplankton functional groups, how they affect biogeochemistry and how the links between community structure and biogeochemical cycles are modelled. Finally, we explore how global environmental change will affect phytoplankton communities, from the traits of individual species to the relative abundance of functional groups, and how that, in turn, may alter biogeochemical cycles.Synthesis. We can increase our mechanistic understanding of the links between the community structure of primary producers and biogeochemistry by focusing on traits determining functional group responses to the environment (response traits) and their biogeochemical functions (effect traits). Identifying trade-offs including allometric and phylogenetic constraints among traits will help parameterize predictive biogeochemical models, enhancing our ability to anticipate the consequences of global change.

Rising temperatures are expected to favour the growth of bloom-forming cyanobacteria in temperate lakes, but may also change the composition of cyanobacterial communities. To predict future community and bloom dynamics, it is therefore important to understand how bloom-forming species respond to temperature. Cylindrospermopsis raciborskii (Woloszynska) Seenayya & Subba Raju is an invasive, toxin-producing, nitrogen-fixer that may benefit from warming. To understand how changing temperatures will influence its ability to compete against native North American bloom-formers, we characterized the thermal reaction norms and temperature traits of three C. raciborskii strains, four strains of Microcystis aeruginosa (Kutzing) Kutzing and one strain of Anabaena flos-aquae (Lyng.) Breb. C. raciborskii strains had higher optimum temperatures and survived higher temperatures than toxic M. aeruginosa strains, but had no apparent advantage over the non-toxic M. aeruginosa strain or A. flosaquae. M. aeruginosa strains and A. flos-aquae tolerated lower temperatures than C. raciborskii, suggesting that fitness differences at low temperature may be important in limiting the latter's spread. Furthermore, we found that nutrient availability strongly influenced thermal reaction norm shape: nitrogen deprivation lowered growth rates and decreased both low-and high-temperature tolerance, but did not affect the optimum temperature in C. raciborskii.

Aim Ecological and evolutionary forces shape the functional traits of species within and across environments, generating biogeographical patterns in traits. We aimed to: (1) determine the extent to which temperature traits of phytoplankton are adapted to their local environment, and (2) detect and explain differences in patterns of adaptation between functional groups (reflecting evolutionary history) and across ecosystems (freshwater versus marine).

Gliding robotic fish, a hybrid of underwater gliders and robotic fish, are energy-efficient and highly maneuverable, and hold strong promise for long-duration sampling of underwater environments. In this paper a novel systematic autonomous water-column-based sampling scheme for gliding robotic fish is proposed to measure the three-dimensional spatial distributions of variables of interest in aquatic environments. The scheme exploits energy-efficient spiral-down motion to sample each water column, followed by sagittal-plane glide-up towards the direction of next water column. Once surfacing, the robot uses GPS guidance to reach the next column location through swimming. To enhance the path tracking performance, a two-degree-of-freedom controller involving H-infinity control is used in the spiral motion, and a sliding-mode controller is employed to regulate the yaw angle during glide-up. The sampling scheme has been implemented on a gliding robotic fish prototype, "Grace", and verified first in pool experiments, and then in field experiments involving the sampling of harmful algae concentration in the Wintergreen Lake, Michigan.